WO2025101606A1 - Capteurs acousto-mécaniques à large bande sans fil en tant que réseaux corporels sans fil aux fins d'une surveillance physiologique continue - Google Patents

Capteurs acousto-mécaniques à large bande sans fil en tant que réseaux corporels sans fil aux fins d'une surveillance physiologique continue Download PDF

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Publication number
WO2025101606A1
WO2025101606A1 PCT/US2024/054723 US2024054723W WO2025101606A1 WO 2025101606 A1 WO2025101606 A1 WO 2025101606A1 US 2024054723 W US2024054723 W US 2024054723W WO 2025101606 A1 WO2025101606 A1 WO 2025101606A1
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WO
WIPO (PCT)
Prior art keywords
bams
sounds
living subject
devices
sound
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Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
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PCT/US2024/054723
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English (en)
Inventor
John A. Rogers
Anthony R. BANKS
Jaeyoung Yoo
Ankit BHARAT
Debra E. Weese-Mayer
Wissam SHALISH
Seyong Oh
Jong Yoon Lee
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Royal Institution for the Advancement of Learning
Sibel Health Inc
Northwestern University
Ann and Robert H Lurie Childrens Hospital of Chicago
Original Assignee
Royal Institution for the Advancement of Learning
Sibel Health Inc
Northwestern University
Ann and Robert H Lurie Childrens Hospital of Chicago
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Publication of WO2025101606A1 publication Critical patent/WO2025101606A1/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb occurring during breathing
    • A61B5/1135—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb occurring during breathing by monitoring thoracic expansion
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1102—Ballistocardiography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4222—Evaluating particular parts, e.g. particular organs
    • A61B5/4255—Intestines, colon or appendix
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6832—Means for maintaining contact with the body using adhesives
    • A61B5/6833—Adhesive patches
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/003—Detecting lung or respiration noise
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/04—Babies, e.g. for SIDS detection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/04—Babies, e.g. for SIDS detection
    • A61B2503/045—Newborns, e.g. premature baby monitoring
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0204—Acoustic sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches

Definitions

  • the present invention relates generally to healthcare and vital sign monitoring, and more particularly to wireless broadband acousto-mechanical sensing (BAMS) system and methods using BAMS sensor devices as body area networks for continuous physiological monitoring of a living subject, and applications of the same.
  • BAMS wireless broadband acousto-mechanical sensing
  • the invention relates to a broadband acousto-mechanical sensing (BAMS) system for monitoring physiological signals of a living subject.
  • the BAMS system includes: one or more wireless, skin-interfaced BAMS devices disposed on the living subject, being time-synchronized and wirelessly communicate with each other, wherein each BAMS device comprises an accelerometer configured to capture acceleration data from the living subject and an acoustic mechanical device configured to capture body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; and a control device being time-synchronized and wirelessly communicate with the one or more BAMS devices, configured to manage and process the acceleration data and the body sounds from the one or more BAMS devices and to generate information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices.
  • Another aspect of the invention relates to a method of monitoring physiological signals of a living subject with a broadband acousto-mechanical sensing (BAMS) system.
  • the method includes: managing and processing, by a control device being time- synchronized and wirelessly communicate with one or more, skin-interfaced BAMS devices, acceleration data and body sounds from the one or more BAMS devices, wherein the one or more BAMS device are disposed on the living subject, the one or more BAMS devices are time- synchronized and wirelessly communicate with each other, each BAMS device comprises an accelerometer configured to capture the acceleration data from the living subject and an acoustic mechanical device configured to capture the body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; and generating, by the control device, information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices.
  • the body movements include chest wall movements and abdominal wall excursions
  • the body sounds include respiratory sounds, lung sounds, gastro-intestinal sounds, bowel sounds, and cardiac sounds.
  • each BAMS device includes: a flexible printed circuit board (fPCB); an inertial measurement unit (IMU) disposed on the fPCB, wherein the IMU functions as the accelerometer configured to capture 3-axis acceleration data from the living subject; and two microphones, respectively disposed on a body-facing side and an ambient-facing side of the FPCB, configured to capture the body sounds from the living subject ambient sounds from the environment.
  • fPCB flexible printed circuit board
  • IMU inertial measurement unit
  • each BAMS device further comprises a top elastomer layer and a bottom elastomer layer sandwiching the fPCB, wherein the bottom elastomer layer forms the body -facing surface attached to the living subject, and the top elastomer layer forms the ambientfacing surface.
  • each BAMS device further comprises a Bluetooth Low Energy (BLE) system on a chip (SoC) disposed on the fPCB, configured to wirelessly communicate with the control device.
  • BLE Bluetooth Low Energy
  • SoC SoC
  • each BAMS device further comprises an embedded power supply disposed on the fPCB or a wireless charging power supply.
  • each BAMS device is configured to generate the body sound by applying an adaptive filtering algorithm to perform sound separation to the body sounds and the ambient sounds in order to minimize contribution of the ambient sounds to the body sounds.
  • control device comprises a graphical user interface (GUI) configured to real-time display quantitative information of the body movements and the body sounds of the living subject.
  • GUI graphical user interface
  • the one or more BAMS devices are disposed at one or more of the following locations of the living subject: a right upper chest area; a right lower chest area; a right axilla area; a right upper back area; a right mid back area; a right lower back area; a left upper chest area; a left lower chest area; a left axilla area; a left upper back area; a left mid back area; a left lower back area; and a suprasternal notch area.
  • a BAMS device for capturing physiological signals of a living subject.
  • the BAMS device includes: a flexible printed circuit board (fPCB); an inertial measurement unit (EMU) disposed on the fPCB, wherein the IMU functions as the accelerometer configured to capture 3-axis acceleration data from the living subject; and two microphones, respectively disposed on a body -facing side and an ambient-facing side of the FPCB, configured to capture the body sounds from the living subject ambient sounds from the environment.
  • fPCB flexible printed circuit board
  • EMU inertial measurement unit
  • the BAMS device further comprises a top elastomer layer and a bottom elastomer layer sandwiching the fPCB, wherein the bottom elastomer layer forms the body -facing surface attached to the living subject, and the top elastomer layer forms the ambientfacing surface.
  • the BAMS device further comprises a Bluetooth Low Energy (BLE) system on a chip (SoC) disposed on the fPCB, configured to wirelessly communicate with the control device.
  • BLE Bluetooth Low Energy
  • SoC SoC
  • the BAMS device further comprises an embedded power supply disposed on the fPCB or a wireless charging power supply.
  • the BAMS device is configured to generate the body sound by applying an adaptive filtering algorithm to perform sound separation to the body sounds and the ambient sounds in order to minimize contribution of the ambient sounds to the body sounds.
  • the BAMS system and the BAMS device may be utilized in a variety of applications.
  • a method of determining regional lung function of a living subject using the BAMS system as described is provided.
  • a method of monitoring bilateral lung function of a living subject using the BAMS system as described is provided.
  • a method of monitoring athletic performance of a living subject for conditioning using the BAMS system as described is provided.
  • a non-transitory tangible computer-readable medium for storing instructions which, when executed by one or more processors, cause the method as described to be performed.
  • the method includes: managing and processing, by a control device being time-synchronized and wirelessly communicate with one or more, skin-interfaced BAMS devices, acceleration data and body sounds from the one or more BAMS devices, wherein the one or more BAMS device are disposed on the living subject, the one or more BAMS devices are time-synchronized and wirelessly communicate with each other, each BAMS device comprises an accelerometer configured to capture the acceleration data from the living subject and an acoustic mechanical device configured to capture the body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; generating, by the control device, information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices; detecting, by the control device, whether the information of the body movements and the body sounds of the living subject
  • the BAMS system includes: one or more wireless, skin-interfaced BAMS devices disposed on the living subject, being time- synchronized and wirelessly communicate with each other, wherein each BAMS device comprises an accelerometer configured to capture acceleration data from the living subject and an acoustic mechanical device configured to capture body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; and a control device being time-synchronized and wirelessly communicate with the one or more BAMS devices, configured to manage and process the acceleration data and the body sounds from the one or more BAMS devices and to generate information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices, detect whether the information of the body movements and the body sounds of the living subject show a partial reduction or an absence thereof, and determine, in response to detecting
  • each BAMS device is configured to generate the body sound by applying an adaptive filtering algorithm to perform sound separation to the body sounds and the ambient sounds in order to minimize contribution of the ambient sounds to the body sounds.
  • each BAMS device further comprises a Bluetooth Low Energy (BLE) system on a chip (SoC), configured to wirelessly communicate with the control device.
  • BLE Bluetooth Low Energy
  • SoC SoC
  • the body movements include chest wall movements and abdominal wall excursions
  • the body sounds include respiratory sounds and lung sounds.
  • control device comprises a graphical user interface (GUI) configured to real-time display quantitative information of the body movements and the body sounds of the living subject.
  • GUI graphical user interface
  • FIG. 1A schematically shows a functional block diagram of a BAMS system according to certain embodiments of the present invention.
  • FIG. IB schematically shows a control device of FIG. 1A according to certain embodiments of the present invention.
  • FIG. 2A shows wireless networks of skin-interfaced miniaturized sensors of body sounds and motions for continuous physiological monitoring and diagnostics, where (A) shows a schematic illustration of a system for tracking (i) cardiorespiratory activity, (ii) gastro-intestinal sounds, and (iii) multi-location respiratory sounds; (B) shows a photograph of the BAMS device on a neonatal model and associated real-time GUI; (C) shows a schematic exploded-view illustration of a BAMS device; and (D) shows a block diagram of the physiological monitoring scheme that combines an IMU with body-facing and ambient-facing microphones.
  • FIG. 2B shows a diagram illustrating data for comparison of clinical results obtained with a BAMS device (chest wall movements, respiratory sounds, and cardiac sounds) and with systems for recording electrocardiograms, and exhaled CO2 tracing from a 19-month-old patient in the pediatric intensity care unit (PICU).
  • BAMS device chest wall movements, respiratory sounds, and cardiac sounds
  • PICU pediatric intensity care unit
  • FIG. 3 shows thermal stability tests of the microphone and IMU, where (A) shows a photograph of the thermal test set-up, (B) and (C) show histogram plots of (B) accelerations and (C) sound intensities captured at two different temperatures, (D) shows acceleration, microphone, and temperature data measured as a function of temperature from 32°C to 40°C, and (E) and (F) show magnified views of acceleration, microphone, and sound intensity data at (E) 32°C and (F) 40°C.
  • FIG. 4 shows a real-time wireless network of skin-integrated sensors of body sounds and movements for continuous physiological monitoring and visual feedback.
  • FIG. 5 shows a table of performance comparison of digital stethoscopes.
  • FIG. 6 shows data on power consumption and wireless charging, where (A) shows current consumption and (B) shows 1 -second average current during standby and during operation of the device, (C) shows battery residual voltage as a function of operating time starting with a fully charged battery, and (D) shows battery voltage during wireless charging.
  • FIG. 7A shows characterization of individual BAMS systems and wireless networks for cardiorespiratory monitoring, where (A) shows a schematic illustration of sound separation using a pair of microphone setup; (B) shows cardiorespiratory sound captured in an ambient environment with white noise and with crying sounds at 90 dB (i) time series data from the body-facing and ambient-facing microphones, and spectrogram representation of the former, (ii) corresponding results after two-step adaptive filtering; (C) shows a schematic illustration of body locations for cardiorespiratory monitoring; (D) shows spectrogram of sounds collected on the (i) suprasternal notch (SN), (ii) upper chest, and (iii) lower chest; and (E) shows normalized data for chest wall movements extracted from the IMU of the device on the SN and the sound intensity associated with respiration (respiratory sound, > 150 Hz) at each location.
  • A shows a schematic illustration of sound separation using a pair of microphone setup
  • B shows cardiorespiratory sound captured in an ambient environment with
  • FIG. 7B shows characterization of individual BAMS systems and wireless networks for cardiorespiratory monitoring, where (1) shows normalized ECG data and sound intensity associated with cardiac activity (cardiac sound, ⁇ 150 Hz) at each location; (2) shows comparison of heart rate interval range between ECG and cardiac sound from the microphone; and (3) shows cardiac sound intensity during resting and exercising.
  • FIG. 8 shows a flow chart of two-step adaptive acoustic filtering for separate measurements of body and ambient sounds.
  • FIG. 9 shows characterization of noise cancellation using the BAMS system, where (A) and (B) shows experimental setup using a lung sound trainer, sound meter, with (A) a BAMS system and (B) a commercial digital stethoscope (3MTM Littmann® CORE, Eko) with active noise cancellation; (C) shows breath sound and heart sound intensity recorded in an ambient of 90 dB white noise across frequency from 20 to 400 Hz; and (D) and (E) shows signal-to-noise ratio (SNR) of breath sound and heart sound for the BAMS system and the commercial digital stethoscope, measured in different ambient conditions, including (D) levels of white noise and (E) types of sounds with a noise level of 75 dB.
  • SNR signal-to-noise ratio
  • FIG. 10 shows comparison of cardiac activity captured by microphone and IMU in white noise, where (A) shows spectrogram of body sound without and with noise cancellation, as well as IMU data, in an ambient white noise across frequencies from 20 to 400 Hz; (B) and (C) shows magnified signals at 40 dB and 80 dB noise levels; and (D) shows the signal-to-noise ratio (SNR) of cardiac activity for the microphone and IMU, measured in different ambient noise conditions.
  • A shows spectrogram of body sound without and with noise cancellation, as well as IMU data, in an ambient white noise across frequencies from 20 to 400 Hz
  • B shows magnified signals at 40 dB and 80 dB noise levels
  • D shows the signal-to-noise ratio (SNR) of cardiac activity for the microphone and IMU, measured in different ambient noise conditions.
  • SNR signal-to-noise ratio
  • FIG. 11 shows calibration of BAMS system sound, where (A) shows time series data from the microphone and corresponding spectrogram representation captured at various level of white noise sound across frequency from 20 to 400 Hz; (B) shows correlation between decibel readings from the sound meter and the intensity measured by the BAMS system; and (C) shows comparison of sound levels between the sound meter and the calibrated BAMS system.
  • FIG. 12 shows continuous long-term cardiorespiratory monitoring during sleep and vigorous activity.
  • FIG. 13 shows respiratory sound monitoring in quiet and noisy environments and while rubbing clothing against the device, patting the body and directly tapping the device.
  • FIG. 14A shows cardiopulmonary monitoring and cardio-respiratory coupling analysis during daily activities, where (A) shows data corresponding to skin temperature, physical activity, and body sounds captured during daily activities; and (B) and (C) shows spectrogram images and intensity of breath and heart sounds as a function of time for recordings collected indoors and outdoors.
  • FIG. 14B shows cardiopulmonary monitoring and cardio-respiratory coupling analysis during daily activities, where (1) shows respiratory rate, heart rate, heart sound intensity, heart rate variability, and cardio-respiratory coupling extracted from data collected indoors and outdoors; (2) shows correlation between heart rate and respiratory rate; and (3) shows cardiorespiratory coupling values as a function of physical activity levels.
  • FIG. 15 shows monitoring of swallowing signals using the BAMS system on the (A) suprasternal notch, (B) upper chest and (C) middle chest, where (i) shows a schematic illustration of the device mounting location for each placement, (ii) shows chest movement and swallowing signal using lowpass filtered IMU and highpass filtered IMU, and (iii) shows spectrogram and intensity of body sound for each placement.
  • FIG. 16 shows estimation of cardiac activity during and after exercise from measurements on the lower chest, where (A) shows data corresponding to physical activity, heart rate intervals, heart sound intensity, root mean square of continuous difference (RMSSD) between heartbeats captured by FDA-approved ECG monitoring system and from the microphone and IMU data of a BAMS device during and after exercise; (B) and (C) shows the ECG signal, spectrogram images, and intensity of heart sounds during (B) resting and (C) exercising; and (D) shows Bland-Altman plots comparing measurements for heart rate.
  • RMSSD root mean square of continuous difference
  • FIG. 17 shows Bland- Altman plots comparing measurements for root mean square of continuous difference (RMSSD).
  • FIG. 18 shows analysis of cardiac sound intensities, where (A) shows continuous blood pressure monitoring data and cardiac sound intensities during resting, breath holding, and cold pressor tests; and (B) shows correlation between cardiac sound intensity and blood pressure.
  • FIG. 19 shows heart sound monitoring during vocalization (A) without noise cancellation and (B) with noise cancellation.
  • FIG. 20 shows continuous, wireless monitoring of respiratory sounds and other physiological parameters from neonates in a neonatal intensive care unit (NICU), where (A) shows a photograph of the BAMS device on a neonate (born at 30 weeks' gestation, 33 weeks' post-menstrual age, and weighing 1.56kg); (B) shows representative respiration waveforms associated with a pneumotachograph device and with a BAMS system (chest movements and acoustic spectrograms) from a neonate; (C) shows ambient noise level, body rotation angle, heart rate, respiratory rate, breathing intensity, and breathing intervals determined from data collected using a BAMS system and FDA-approved clinical monitors from a neonate; (D) shows correlation of normalized intensity of pneumotachograph data and respiratory sounds; (E) shows breathing intervals; and (F) shows respiratory sound intensity recorded from ten different neonates for 500 seconds; the box plot shows the range between the 25th and 75th percentiles, and the midline indicates the median of each data set.
  • NNIU neonatal
  • FIG. 21 shows a photograph of the BAMS device on a neonate (born at 30 weeks' gestation, 33 weeks' post-menstrual age, and weighing 1.56kg) with wire-based nasal temperature sensor, RIP band and ECG system.
  • FIG. 22 shows Bland-Altman plots of measurements of respiratory rate using the BAMS system and pneumotach module in continuous, wireless monitoring of respiratory sounds from neonates in a neonatal intensive care unit (NICU).
  • NICU neonatal intensive care unit
  • FIG. 23 shows a table of comparison of Pearson correlation values between airflow rate and breath sound intensity from previous research.
  • FIG. 24 shows monitoring of neonatal cardiac activity using the BAMS system, where (A) shows ECG signal, spectrogram image, and heart sound intensity, and (B) shows Bland- Altman plots comparing heart rate determined using the BAMS system with ECG measurements (5 neonates, 136,013 data points).
  • FIG. 25 shows spectrogram images and time series results comparing respiratory behaviors obtained from breath sounds measured with the microphone in a BAMS system, temperature measured with a nasal thermistor, chest wall movement measured with the IMU in a BAMS system, and the summation of respiratory inductance plethysmograms measured with chest and abdomen RIP bands.
  • FIG. 26 shows Bland-Altman plots comparing respiratory rate determined using the BAMS system with nasal temperature flow measurements (5 neonates, 42,738 data points).
  • FIG. 27 shows monitoring of internal air flow using time-synchronized sensors. Schematic illustration and images showing the mounting locations of the devices, including (i) Device-A: Suprasternal notch, and (ii) Device-B: Right upper chest).
  • FIG. 28 shows gastrointestinal activity monitoring using a microphone and EMG sensor, where (A) shows spectrogram of bowel sounds, (B) shows bowel sound intensity recorded by the microphone, and (C) shows EMG signal during gastrointestinal activity.
  • FIG. 29 shows continuous, wireless monitoring of gastro-intestinal sounds (bowel sounds) from neonates in a neonatal intensive care unit (NICU), where (A) shows a photograph of a pair of BAMS devices on an infant (46 weeks' post-menstrual age); (B) and (C) show spectrograms and sound intensities (> 150Hz) for data collected (B) before feeding and (C) after feeding using a BAMS system placed on the upper right abdomen of a neonate; (D) shows bowel sound peak counts, normalized bowel sound intensity at the upper right and lower left abdomen, and difference in normalized bowel sound intensities between the upper right and lower left abdomen; (E)-(G) show comparison of (E) bowel sound peak counts per minutes and (F) bowel sound intensity before and after feeding, and (G) difference in normalized bowel sound intensities between upper right and lower left abdomen at 15 min after feeding and 15-30 min after feeding, where the box plot shows the range between the 25th and 75th percentiles, and the midline indicates the median of each
  • FIG. 30 shows a flowchart of data processing steps for detecting bowel sounds.
  • FIG. 31 shows time synchronization of a wireless network of BAMS systems, where (A) shows a block diagram of the scheme for time synchronization; (B) shows a photograph of the test setup using 13 BAMS devices and a vibration generator; (C) shows sound data recorded by the 13 BAMS devices; (D) shows cross-correlation results and time differences between BAMS devices; (E) shows average time differences between BAMS devices; and the error bars correspond to the standard deviation of time differences between devices.
  • FIG. 32A shows wireless networks of BAMS systems and results for simultaneous spatio-temporal mapping lung sounds and chest wall movements, where (A) and (B) shows (i) CT image and sound distribution of (ii) anterior and (iii) posterior thorax of (A) healthy subject (patient A) and (B) chronic lung disease patient (patient B).
  • FIG. 32B shows additional results for simultaneous spatio-temporal mapping lung sounds and chest wall movements, where (1) and (2) shows distribution of (i) chest movement, (ii) sound intensity, (iii) dominant sound frequency of (1) a healthy subject and (2) a lung disease patient during exhalation.
  • FIG. 33 shows a schematic illustration of the locations of devices in a wireless network for lung sound monitoring.
  • FIG. 34 shows CT images showing coronal and axial sections of a healthy subject, Patient A.
  • FIG. 35 shows CT images showing coronal and axial sections of a patient with chronic lung disease (Patient B), exhibiting a left upper lobe consolidation, volume loss, and bronchiectasis, consistent with radiation pneumonitis, as well as right peripheral pleuroparenchymal fibrosis.
  • FIG. 36 shows crackle respiratory sounds detected in the posterior chest of Patient B, where (A) shows a schematic illustration of the locations of a wireless network of device for lung sound monitoring; and (B) shows representative data that illustrate crackle sounds.
  • FIG. 37 shows analysis of the distribution of lung sounds across healthy subjects and patients with chronic lung disease, where (A) and (B) show correlation of (A) lung sound intensity and air flow rate and (B) lung sound energy and air volume from BAMS systems located on the suprasternal notch, upper and lower posterior chest for 10 healthy subjects; (C) shows dominant frequency of lung sounds on the suprasternal notch, upper chest and lower chest location for 10 healthy subjects during exhalation; (D) shows distribution of the ratio of the upper right and upper left anterior chest lung sound intensity and sound intensity at the suprasternal notch during exhalation for healthy subjects, lung disease patients with both lungs intact, and lung disease patients with either left upper lung resection or right upper lung resection; (E) shows distribution of the right upper posterior dominant expiratory frequency and sound intensity at the suprasternal notch during exhalation in patients with lung disease in the right upper lung, and subjects without lung disease; (F) shows the box plot about ratio of the upper right and upper left anterior chest lung sound intensity for healthy subjects, lung disease patients with both lungs intact, and
  • FIG. 38 shows spectrogram image and the intensity of lung sounds during inhalation and exhalation, where inhale/ exhale intensity corresponds to peak sound intensity, and the inhale/exhale sound energy is determined by integrating sound intensity during inhalation and exhalation, as a surrogate for airflow volume.
  • FIG. 39 shows comparison of left upper and right upper lung sound intensity ratios across a collection of device mounting locations, where (A) shows a schematic illustration of device locations; and (B) shows the ratios of left upper and right upper anterior chest lung sound intensities, where the error bars correspond to the standard deviation of ratio of intensities.
  • FIG. 40 shows analysis of the distribution of lung sound intensity across healthy subjects and adult patients with chronic lung disease, where distribution of the ratio of right and left anterior lung sound intensity and sound intensity at the suprasternal notch during exhalation for healthy subjects and lung disease patients with either left lung or right lung resection at (A) the upper chest and (C) the lower chest; and box plots depicting the ratio of right and left anterior lung sound at (B) the upper chest and (D) the lower chest.
  • FIG. 41 A shows analysis of the distribution of lung sound frequency across healthy subjects and patients with chronic lung disease, where distribution of the right posterior dominant expiratory frequency and sound intensity at the suprasternal notch during exhalation in patients with lung disease and subjects without lung disease at the upper chest, and the box plot depicting the dominant expiratory frequency at the upper chest.
  • FIG. 4 IB shows analysis of the distribution of lung sound frequency across healthy subjects and patients with chronic lung disease, where distribution of the right posterior dominant expiratory frequency and sound intensity at the suprasternal notch during exhalation in patients with lung disease and subjects without lung disease at the middle chest, and the box plot depicting the dominant expiratory frequency at the middle chest.
  • FIG. 41C shows analysis of the distribution of lung sound frequency across healthy subjects and patients with chronic lung disease, where distribution of the right posterior dominant expiratory frequency and sound intensity at the suprasternal notch during exhalation in patients with lung disease and subjects without lung disease at the lower chest; and the box plot depicting the dominant expiratory frequency at the lower chest.
  • FIG. 42 shows scenarios for long-term data storage using streaming data and local flash memory.
  • FIG. 43 shows an RF system for wireless charging a device while worn on a baby, where (A) shows a schematic illustration and (B) shows a photograph of the wireless charging system integrated into the base of an incubator; (C) shows an infrared (IR) image, and (D) shows temperature of the device during an 8-hour charging period.
  • FIG. 44 shows time synchronization on the anterior and posterior body surfaces, where (A) shows a schematic illustration of the layout for evaluating errors in time synchronization, (B) shows microphone data from 13 sensors corresponding to the 60 bpm sound from a metronome, (C) shows a magnified view of sound peak signals, and (D) shows time difference between BAMS sensors and the master sensor during a 2-hour 30-minute recording period.
  • FIG. 45 shows effects of ambient noise on measurements of respiratory sounds, where (A) shows microphone data and spectrogram image of respiratory sounds with and without 70 dB white noise, (B) shows frequency distribution of respiratory sounds determined by FFT of recorded data, (C) shows a spectrogram image and respiratory sound intensity with bandpass filtering from 150 Hz to 300 Hz, and (D) shows a spectrogram image and respiratory sound intensity with sound separation.
  • FIG. 46 shows cardiorespiratory sound information, where (A) shows a photograph of the cardiorespiratory sound monitoring set-up using a child heart and lung sounds trainer (Simulaids, Nasco Education), (B) and (D) show spectrogram images of (B) respiratory sounds and (D) cardiac sounds, and (C) and (E) show frequency distribution of (C) respiratory sounds and (E) cardiac sounds determined by FFT of the measured signals.
  • A shows a photograph of the cardiorespiratory sound monitoring set-up using a child heart and lung sounds trainer (Simulaids, Nasco Education)
  • B) and (D) show spectrogram images of (B) respiratory sounds and (D) cardiac sounds
  • C) and (E) show frequency distribution of (C) respiratory sounds and (E) cardiac sounds determined by FFT of the measured signals.
  • FIG. 47 shows respiratory rate detection algorithm, where (A) shows a revised respiratory rate detection flowchart; and (B) shows inhale (Marker: x) and exhale (Marker: o) marks and signals of pneumotach, chest movement (IMU), respiratory sound intensity (microphone), and spectrogram of respiratory sound at low and high respiratory signals.
  • FIG. 48 shows signal associated with activity level, acceleration (x, y, z axis), bandpass filtered acceleration along the z axis from the IMU, and spectrogram plot of data from the microphone during resting, walking, and squatting.
  • FIG. 49 shows optical images of securely mounted devices adhered to convex and concave regions of an infant model.
  • FIG. 50 shows the cardiac sound intensity on the Child Heart and Lung Sounds Trainer (Simulaids, Nasco Education) as a function of distance between the body and the microphone.
  • FIG. 51 shows intestinal motility and gastrointestinal sound in infants in the PICU in response to patient-controlled Analgesia, where (A) and (B) show spectrogram and sound intensity of GI sound (A) before and (B) after patient-controlled analgesia; and (C) shows GI sound peak counts per minute and normalized bowel sound intensity.
  • FIG. 52 shows an example of periodic breathing detected using a wireless acoustic sensor.
  • FIG. 53 shows an example of hypopnea and central apnea detected using the wireless acoustic sensor.
  • first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the invention.
  • relative terms such as “lower” or “bottom” and “upper” or “top,” may be used herein to describe one element’s relationship to another element as illustrated in the figures. It will be understood that relative terms are intended to encompass different orientations of the device in addition to the orientation depicted in the figures. For example, if the device in one of the figures, is turned over, elements described as being on the “lower” side of other elements would then be oriented on “upper” sides of the other elements. The exemplary term “lower”, can, therefore, encompasses both an orientation of “lower” and “upper,” depending on the particular orientation of the figure.
  • “around”, “about”, “approximately” or “substantially” shall generally mean within 20 percent, preferably within 10 percent, and more preferably within 5 percent of a given value or range. Numerical quantities given herein are approximate, meaning that the term “around”, “about”, “approximately” or “substantially” can be inferred if not expressly stated.
  • the phrase “at least one of A, B, and C” should be construed to mean a logical (A or B or C), using a non-exclusive logical OR.
  • the term “and/or” includes any and all combinations of one or more of the associated listed items.
  • the human body generates various forms of subtle, broadband mechano-acoustic signals that contain valuable information on cardiorespiratory and gastrointestinal health, as important biomarkers for continuous physiological monitoring.
  • Existing device options ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but with disadvantages that restrict measurement locations, prevent continuous, longitudinal tracking, limit use to controlled environments, and support only single-point measurements. These constraints are significant for many applications, such as those in monitoring airway obstruction, adventitious lung sounds, and intestinal motility.
  • certain aspects of the present invention introduce a wireless, skin-interfaced sensor technology that combines skin-integrated microphones and accelerometers to capture broadband signals that provide information on processes ranging from slow body movements, to digestive activity, to respiratory sounds, to cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds.
  • the broadband signals may span high frequency body sounds (up to frequencies of ⁇ 1 kHz) to slow body movements (near 0 Hz), with capabilities for time- synchronized measurements at several body locations simultaneously.
  • BAMS broadband acousto-mechanical sensing
  • the devices include capabilities for separate, simultaneous recordings of sounds from internal body processes and the external environment, as sound is captured using an integrated pair of opposing microphones and interpreted with associated signal-processing algorithms.
  • the small sizes, the lightweight construction, the soft mechanical properties, and the gentle adhesive interfaces allow for measurements from nearly any location of the body, and across broad ranges of patients, from premature infants to elderly patients. This collection of features is currently unavailable in existing research devices such as those that rely exclusively on accelerometers without the ability for sensitive high frequency measurements, or those that leverage advanced digital stethoscopes without the dual -mi crophone architecture, the skin-compatible form, and the capacity for multimodal continuous operation.
  • the system can also perform spatio-temporal mapping the dynamics of gastro-intestinal processes and airflow into/out of the lungs. Studies demonstrate utility in various aspects of patient care, from premature infants in neonatal intensive care units to adult patients in thoracic surgery clinics.
  • Real-time monitoring with this BAMS system enables quantitative, continuous tracking of essential body sounds, ranging from multiple aspects of cardiorespiratory function, gastrointestinal activity, swallowing and respiration, and spatially mapped dynamic properties of air flow into and out of the lungs.
  • the inventors report the successful deployment of these BAMS systems in monitoring and providing clinical data for premature babies in neonatal intensive care units (15 subjects) and adult patients (55 subjects) in thoracic surgery clinic.
  • the results suggest broad potential applications of this technology in many aspects of patient care.
  • the following describes the detailed engineering aspects of these technology platforms, quantifies their various measurement capabilities and, where possible, compares the results to state-of-the-art clinically approved technologies.
  • the BAMS system includes: one or more wireless, skin-interfaced BAMS devices disposed on the living subject, being time-synchronized and wirelessly communicate with each other, wherein each BAMS device comprises an accelerometer configured to capture acceleration data from the living subject and a pair of microphones configured to capture body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; and a control device being time-synchronized and wirelessly communicate with the one or more BAMS devices, configured to manage and process the acceleration data and the body sounds from the one or more BAMS devices and to generate information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices.
  • FIG. 1A schematically shows a functional block diagram of a BAMS system according to certain embodiments of the present invention.
  • the BAMS system as shown in FIG. 1 A is an exemplary system, and is not intended to limit the apparatus for monitoring physiological parameters of a living subject.
  • the living subject is a human subject or a non -human subject.
  • the exemplary system 100 includes a plurality of BAMS devices 110 and 150, namely a first BAMS device 110 and a second BAMS device 150, and a control device 190 adapted in wireless communication with the BAMS devices 110 and 150.
  • the BAMS devices 110 and 150 are wireless, skin-interfaced devices which are time-synchronized and communicate with the control device 190 wirelessly and bidirectionally, and are respectively attached to different positions of the living subject to capture acceleration data and body sounds from the living subject.
  • each of the BAMS devices 110 and 150 may include an accelerometer configured to capture acceleration data from the living subject and a pair of microphones configured to capture body sounds from the living subject.
  • the accelerometer and the acoustic mechanical device in each BAMS device 110 and 150 are time- synchronized.
  • the first BAMS device 110 is disposed at a first position 410 of the living subject
  • the second BAMS device 150 is disposed at a second position 420 of the living subject.
  • the control device 190 may be implemented by, for example, a microcontroller unit (MCU) that is being time-synchronized and communicate with the first and second BAMS devices 110 and 150 wirelessly, and is used to manage and process the acceleration data and the body sounds from the first and second BAMS devices 110 and 150 and to generate information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the first and second BAMS devices 110 and 150.
  • MCU microcontroller unit
  • the body movements include chest wall movements and abdominal wall excursions
  • the body sounds include respiratory sound, lung sounds, gastro-intestinal sounds, bowel sound, and cardiac sounds.
  • each of the BAMS devices 110 and 150 may include: a flexible printed circuit board (fPCB); an inertial measurement unit (IMU) disposed on the fPCB, wherein the IMU functions as the accelerometer configured to capture 3-axis acceleration data from the living subject; and two microphones, respectively disposed on a body -facing side and an ambient-facing side of the FPCB, configured to capture the body sounds from the living subject ambient sounds from the environment.
  • fPCB flexible printed circuit board
  • IMU inertial measurement unit
  • each of the BAMS devices 110 and 150 may further include a top elastomer layer and a bottom elastomer layer sandwiching the fPCB, wherein the bottom elastomer layer forms the body-facing surface attached to the living subject, and the top elastomer layer forms the ambient-facing surface.
  • each of the BAMS devices 110 and 150 may further include a Bluetooth Low Energy (BLE) system on a chip (SoC) disposed on the fPCB, configured to wirelessly communicate with the control device.
  • BLE Bluetooth Low Energy
  • SoC system on a chip
  • each of the BAMS devices 110 and 150 may further include an embedded power supply disposed on the fPCB or a wireless charging power supply.
  • each of the BAMS devices 110 and 150 may further include is configured to generate the body sound by applying an adaptive filtering algorithm to perform sound separation to the body sounds and the ambient sounds in order to minimize contribution of the ambient sounds to the body sounds.
  • control device comprises a graphical user interface (GUI) configured to real-time display quantitative information of the body movements and the body sounds of the living subject.
  • GUI graphical user interface
  • FIG. 1A shows the BAMS system 100 having two BAMS devices 110 and 150, it is possible that the BAMS system 100 may have only one BAMS device or more than two BAMS devices. In other words, the BAMS system 100 may have one or more BAMS devices.
  • FIG. IB schematically shows a control device of FIG. 1A according to certain embodiments of the present invention.
  • the control device 190 is in the form of a computing device, which includes a processor 192, a memory 194, and a storage device 196, and a bus 198 interconnecting the processor 192, the memory 194 and the storage device 196.
  • the control device 190 may be in the form of a general computer, such as a desktop computer, a laptop computer, a tablet or a mobile device, or a specialized computer or other types of computing devices.
  • the control device 190 may include necessary hardware and/or software components (not shown) to perform its corresponding tasks. Examples of these hardware and/or software components may include, but not limited to, other required memory modules, network ports, interfaces, buses, Input/Output (I/O) modules and peripheral devices, and details thereof are not elaborated herein.
  • the processor 192 controls operation of the control device 190, which may be used to execute any computer executable code or instructions.
  • the processor 192 may be a central processing unit (CPU), and the computer executable code or instructions being executed by the processor 192 may include an operating system (OS) and other applications, codes or instructions stored in the control device 190.
  • the control device 190 may run on multiple processors, which may include any suitable number of processors.
  • the memory 194 may be a volatile memory module, such as the random-access memory (RAM), for storing the data and information during the operation of the control device 190.
  • the memory 194 may be in the form of a volatile memory array.
  • the control device 190 may run on more than one memory 194.
  • the storage device 196 is a non-volatile storage media or device for storing the computer executable code or instructions, such as the OS and the software applications for the control device 190.
  • Examples of the storage device 196 may include hard drives, flash memory, memory cards, USB drives, or other types of non-volatile storage devices such as floppy disks, optical drives, or any other types of data storage devices.
  • the control device 190 may have more than one storage device 196, and the software applications of the control device 190 may be stored in the more than one storage device 196 separately.
  • the computer executable code or instructions stored in the storage device 196 may include computer executable instructions 199 for managing and processing the BAMS devices 110 and 150 as shown in FIG. 1 A.
  • the computer executable instructions 199 when executed at the processor 110, manage and process acceleration data and body sounds obtained from the BAMS devices 110 and 150, and generate corresponding information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the BAMS devices 110 and 150.
  • the computer executable instructions 199 when executed at the processor 110, may further provide a graphical user interface (GUI), such that the GUI may real-time display quantitative information of the body movements and the body sounds of the living subject.
  • GUI graphical user interface
  • Another aspect of the invention relates to a method of monitoring physiological signals of a living subject with a broadband acousto-mechanical sensing (BAMS) system.
  • the method includes: disposing one or more wireless, skin-interfaced BAMS devices on the living subject, wherein the one or more BAMS devices are time-synchronized and wirelessly communicate with each other, and wherein each BAMS device comprises an accelerometer configured to capture acceleration data from the living subject and a pair of microphones configured to capture body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; and providing a control device being time-synchronized and wirelessly communicate with the one or more BAMS devices, to manage and process the acceleration data and the body sounds from the one or more BAMS devices and to generate information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices.
  • a BAMS device for capturing physiological signals of a living subject.
  • the BAMS device includes: a flexible printed circuit board (fPCB); an inertial measurement unit (EMU) disposed on the fPCB, wherein the IMU functions as the accelerometer configured to capture 3-axis acceleration data from the living subject; and two microphones, respectively disposed on a body-facing side and an ambient-facing side of the FPCB, configured to capture the body sounds from the living subject ambient sounds from the environment.
  • the accelerometer and the microphones are time- synchronized.
  • the BAMS system and the method of monitoring physiological signals of the living subject as described above may be applied for detecting specific physiological signals.
  • yet another aspect of the invention relates to a method of detecting hypopnea and central apnea of a living subject with a BAMS system.
  • the method includes: managing and processing, by a control device being time- synchronized and wirelessly communicate with one or more, skin-interfaced BAMS devices, acceleration data and body sounds from the one or more BAMS devices, wherein the one or more BAMS device are disposed on the living subject, the one or more BAMS devices are time- synchronized and wirelessly communicate with each other, each BAMS device comprises an accelerometer configured to capture the acceleration data from the living subject and an acoustic mechanical device configured to capture the body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; generating, by the control device, information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices; detecting, by the control device, whether the information of the body movements and the body sounds of the living subject show a partial reduction or an absence thereof; and in response to detecting the information of the body movements and the body sounds of the living subject showing the partial reduction or the absence thereof
  • the BAMS system includes: one or more wireless, skin-interfaced BAMS devices disposed on the living subject, being time- synchronized and wirelessly communicate with each other, wherein each BAMS device comprises an accelerometer configured to capture acceleration data from the living subject and an acoustic mechanical device configured to capture body sounds from the living subject, and the accelerometer and the acoustic mechanical device in each BAMS device are time-synchronized; and a control device being time-synchronized and wirelessly communicate with the one or more BAMS devices, configured to manage and process the acceleration data and the body sounds from the one or more BAMS devices and to generate information of body movements and body sounds of the living subject based on the acceleration data and the body sounds obtained by the one or more BAMS devices, detect whether the information of the body movements and the body sounds of the living subject show a partial reduction or an absence thereof, and determine, in response to detecting
  • each BAMS device is configured to generate the body sound by applying an adaptive filtering algorithm to perform sound separation to the body sounds and the ambient sounds in order to minimize contribution of the ambient sounds to the body sounds.
  • each BAMS device further comprises a BLE SoC, configured to wirelessly communicate with the control device.
  • the body movements include chest wall movements and abdominal wall excursions, and the body sounds include respiratory sounds and lung sounds.
  • control device comprises a GUI configured to real-time display quantitative information of the body movements and the body sounds of the living subject.
  • FIG. 2A(A) illustrates three clinically relevant applications of the BAMS network system, where recordings capture sounds and physical motions across a frequency range from 1 kHz to near 0 Hz. Gently adhering a single BAMS device at the suprasternal notch allows for simultaneous measurements of cardiac and respiratory sounds, providing continuous monitoring of cardiorespiratory activity, as shown in FIG. 2A(A)(i). Time-synchronized devices placed on the abdomen enable spatio-temporal monitoring of gastro-intestinal sounds, for tracking the progress of digestion, as shown in FIG. 2A(A)(ii).
  • FIG. 2A(A)(iii) An advanced implementation involves 13 wirelessly time-synchronized devices placed at targeted sites across the anterior and posterior chest for regional monitoring of pulmonary health, rehabilitation, and disease progression, as shown in FIG. 2A(A)(iii).
  • the applicability of this technology spans across nearly any type of patient and age, from premature babies in neonatal intensive care units (NICUs) to patients with chronic lung diseases in the outpatient clinic or in the intensive care unit and patients following lung resection, as demonstrated in the following sections.
  • the picture in FIG. 2A(B) shows a BAMS device on a neonate model, positioned for cardiorespiratory monitoring.
  • GUI real-time graphical user interface
  • a real-time graphical user interface displays quantitative information on body movements and a spectrogram of body sounds at 100 ms intervals, thereby capturing parameters such as body orientations, physical activities, along with sound intensities and frequencies associated with both the body and the ambient sounds.
  • Data communication exploits standard Bluetooth Low Energy (BLE) protocols.
  • BLE Bluetooth Low Energy
  • FIG. 2A(C) depicts an exploded view illustration of a BAMS device, which includes an inertial measurement unit (IMU, LSM6DSL, STMicroelectronics), a pair of microphones (ICS- 40180, TDK), one body -facing (toward the body) and the other ambient-facing (toward the surroundings), a BLE system on a chip (SoC, ISP- 1807, Insight SIP), a 2GB flash memory (MT29F2G, Micron), and a wireless charging antenna mounted, all on a flexible printed circuit board (FPCB).
  • IMU inertial measurement unit
  • LSM6DSL LSM6DSL
  • STMicroelectronics a pair of microphones
  • ICS- 40180, TDK a pair of microphones
  • SoC SoC
  • ISP- 1807 ISP- 1807
  • Insight SIP 2GB flash memory
  • M2G 2GB flash memory
  • Micron Micron
  • the BAMS system achieves broadband operation by combining an IMU and a pair of microphones with an analog-to-digital converter with high sampling rate, thereby enabling detection of signals across a wide frequency range from measurements of body orientation (fraction of a Hz, -0.01 Hz) to body sounds (-500 Hz).
  • the 3-axis acceleration data captured by the IMU related to body orientation (-0 Hz), body motion (-1 Hz) and physical activity (-20 Hz) without interference from ambient sounds.
  • the IMU lacks, however, the sensitivity required to measure subtle body sounds such as those associated with respiratory and cardiac activity, and bowel movements.
  • the microphone system exhibits high sensitivity in the frequency range of 20 Hz to 20 kHz, making it efficient for capturing even weak body sounds, up to frequencies limited by the analog-to-digital converter in the BLE SoC (-20 kHz samples/second).
  • High fidelity measurements of body sounds represent an advanced capability of the technology reported here, following from the integration of a pair of opposing microphones.
  • These body-facing and ambient-facing microphones allow selective measurements of body and ambient sounds, using algorithms described subsequently.
  • the spectral and temporal characteristics of body sounds without confounding effects of ambient sounds provide insights into subtle activities associated with respiration, digestion, sub-audible vocalizations and cardiac cycles, as the basis of diverse, clinically actionable information for patient care, as shown in FIG. 2A(D).
  • the IMU and microphone sensors exhibit stable performance with deviations of 0.1% and 0.4%, respectively, within the typical body temperature range of 32 °C to 40 °C. This stability enables reliable and consistent use in various clinical cases, as shown in FIG. 3.
  • Real-time data analytics on the time-series data related to body sounds enable detection of risk events ranging from tachycardia and bradycardia to severe wheezing/coughing, apneic events and digestive abnormalities.
  • An LED encapsulated within the device structure can be activated based on threshold settings to serve as an alarm to caregivers, in addition to warnings and phone calls that can be initiated through the user interface, as shown in FIG. 4.
  • FIG. 2B shows the results of cardiorespiratory monitoring using an FDA-approved ECG device and a monitoring system for exhaled CO2, together with the output of a single BAMS device located at the suprasternal notch of a 19-month-old infant.
  • Passing the acceleration data through a bandpass filter (/bandpass 0.1 - 1 Hz) yields signals related to movements of the chest.
  • the results exhibit strong correlations between chest movements, respiratory sound intensities, and exhaled CO2 levels, signifying respirations.
  • FIG. 5 presents a table of comparison of data collected using the BAMS device, with a recently reported wearable stethoscope and with a commercial stethoscope (3MTM Littmann® CORE, Eko).
  • the BAMS system is much smaller (240 times smaller in volume), and lighter (21 times lower in weight) than the commercial stethoscope (3MTM Littmann® CORE, Eko), thereby allowing for continuous hands-free monitoring.
  • the soft and flexible mechanical properties of the BAMS system, the ability for separate measurements of body and ambient sounds, and the capacity for time-synchronized operation across a wireless network of devices represent additional distinguishing features.
  • BAMS system which is 0.036mA at 3.7V in the stand-by mode, and 2.8 mA at 3.7 V in the active mode
  • BAMS system features a wireless charging scheme that enables a fully depleted battery to be charged to its full state in approximately 4 hours, as shown in FIG. 6.
  • the body-facing and ambient-facing microphones capture sound information from two directions to enable differential detection of sounds from the body and the surroundings, as shown in FIG. 7A(A).
  • a two-step adaptive filtering algorithm applied to the data recorded by these two microphones minimizes the contribution of ambient sounds to body sounds as shown in FIG. 8, and vice versa.
  • the application of adaptive filtering algorithm is unique in the context of body-worn microphones, specifically for continuous physiological monitoring. Additionally, the methods serve dual purposes of separately measuring body and ambient sounds, providing important contextual information to aid in the interpretation of the physiological signals. As an example, without this scheme, environments with crying sounds at 90 dB render detection of cardiopulmonary sounds impossible, as shown in FIG. 7A(B)(i).
  • the sound-separated cardiac features extracted from the dual microphone setup and the seismocardiogram data captured by the IMU exhibit SNR values of 20 dB and 12 dB, respectively, for the case of a device mounted on the suprasternal notch. Both results indicate negligible confounding effects of ambient sound, as shown in FIG. 10.
  • Applying the same separation algorithm to data from the ambient-facing microphone using data from the body-facing microphone yields sounds in the environment, with complementary value in understanding the context of patient care, as shown in FIG. 11.
  • This system can also be used in various daily life scenarios, where comprehensive monitoring of not only standard parameters such as heart rate and respiratory rate are possible, but also autonomic measures including heart rate variability (HRV), cardiorespiratory coupling (CRC), and swallowing, all with simultaneous measurements of body orientation and physical activity enabled by the IMU, as shown in FIG. 14A, FIG. 14B and FIG. 15.
  • HRV heart rate variability
  • CRC cardiorespiratory coupling
  • swallowing all with simultaneous measurements of body orientation and physical activity enabled by the IMU, as shown in FIG. 14A, FIG. 14B and FIG. 15.
  • the system demonstrates exceptional performance across various activities, encompassing sleep to exercise, providing high-quality data on physical activity levels, respiratory rate, respiratory sounds (frequency and intensity), heart rate, and cardiac sound intensity over extended periods of time, as shown in FIG. 12.
  • the data collected during sleep also reveal patterns of snoring. Even during intense physical activity, the recordings allow for stable monitoring of respiratory and cardiac sounds.
  • the algorithms have challenges in removing artifacts resulting from physical
  • High-frequency tracheal and low- frequency vesicular sounds can be captured by recording from the suprasternal notch and the chest area, respectively, as shown in FIG. 7A(D).
  • Reduced speeds of airflow and increased movements of the chest wall at the lower chest lead to decreases in the intensity of the respiratory sounds, defined as the cumulative power spectral density above 150 Hz after a short- time Fourier transform (STFT), as shown in FIG. 7A(E).
  • STFT short- time Fourier transform
  • the intensities of the SI cardiac sounds are higher than those of S2 on the lower chest, at locations close to the tricuspid and mitral valves of the heart.
  • the intensities of S2 sounds generated by the pulmonic and aortic valves are higher than those of S 1 on the upper chest and suprasternal notch, as shown in FIG. 7B(1).
  • the SI sound appears clearly in data from the lower chest, even during and after exercise, despite short R-R intervals (363 ms, heart rate of 165 beats per min). These intervals and the heart rates determined from the microphone data match those extracted from ECG recordings, with an average error of 0.2 ms and 0.02 beats per min (bpm), thereby establishing the capacity for reliable measurements of HRV, as shown in FIG. 7B(2) and FIG. 16. These results are within regulatory guidelines set by the US FDA (errors less than ⁇ 10% or ⁇ 5 bpm for HR).
  • the Bland-Altman (BA) plot quantitatively compares the root mean square of continuous difference (RMSSD) between cardiac cycles for HRV.
  • the average difference and standard deviation between RMSSD values extracted from BAMS and ECG waveforms are 0.2 ms and 0.5 ms, respectively, as shown in FIG. 17. Furthermore, the intensity of cardiac sounds, as depicted in FIG. 7B(3), showed an increase during exercise. These sounds have the potential to correlate with blood pressure, as they occur when a moving column of blood comes to a sudden stop or decelerates significantly. Comparing the results from a blood pressure monitor (Finapres® NOVA) with cardiac sound intensity revealed a high correlation trend (FIG. 18) with a Pearson's correlation coefficient of 0.83.
  • the low-frequency nature of cardiac sounds ( ⁇ 150 Hz) provides clean separation from those associated with vocalization, enabling accurate cardiac activity monitoring in daily life scenarios such as exercising, walking, and speaking, after sound separation, as shown in FIG. 19.
  • Premature infants in the NICU are at risk of cardiorespiratory instability due to immature respiratory control centers and respiratory airflow obstruction, which typically manifest as central or obstructive apneas with fluctuations in heart rate and/or oxygen saturation. Noise in the environment can further adversely affect these physiological responses, and excessive auditory stimulation can lead to additional risks of hearing loss and abnormal sensory responses. As a result, continuous monitoring of both cardiopulmonary activity and noise characteristics local to the infant are equally important.
  • FIG. 20(A) highlights an example of the results of monitoring respiration from premature infants in an academic NICU.
  • FIG. 20(B) shows results from a pneumotach module, with simultaneous chest movements and sound data from a BAMS device on the suprasternal notch. Clear cardiac and respiratory signals appear in the spectrogram below and above 150 Hz, respectively.
  • the pneumotach module detects adequate and reduced airflows, consistent with sound intensities observed in the spectrogram. Segments of absent airflow appear in both body sound and pneumotach measurements. Importantly, these periods of airflow obstruction are not consistently accompanied by absent movements of the chest. Several physiological reasons can explain these discrepancies. First, measurements of chest movements using accelerometry can be susceptible to noise caused by body motion.
  • the amplitude of the chest movement signal does not necessarily equate with an equivalent and proportional change in lung volume during inspiration and expiration.
  • neonates and especially preterm infants are at risk of chest wall distortion due to their highly compliant chest wall.
  • a rise in the chest movement signal indicates the presence of a respiratory effort but may not correlate with the degree of airflow during that breath.
  • infants continue to make respiratory efforts. For all those reasons, the magnitude of airflow and chest movement signals may not always correspond.
  • FIG. 20(C) summarizes representative BAMS data from an in-NICU neonate, including ambient noise, body orientation, heart rate, and respiratory rate, in comparison with the readings obtained from FPA-approved clinical monitors, as shown in FIG. 21.
  • the breathing interval and sound intensity determined with the BAMS device correlate with pauses in breathing and breathing airflow rate.
  • FIG. 22 compares respiratory rates determined using pneumotach and body sounds for 10 in-NICU neonates. The average difference and standard deviation of the respiratory rates are 0.44 bpm and 2.13 bpm, respectively. This result lies within the range of FDA-cleared bedside monitoring systems ( ⁇ 3 bpm).
  • FIG. 20(D) The data for normalized airflow rates and respiratory sound intensities of 10 in-NICU newborns show a Pearson's correlation value of 0.87, as shown in FIG. 20(D).
  • Our findings reveal a high level of correlation values compared to those reported in previous studies (the table as shown in FIG. 23).
  • FIG. 20(E) and (F) display the distribution of breathing intervals and respiratory sound intensity of 10 neonates over 500 seconds, s featuring the expected inter- and intra-variability in respiratory rates and airflow.
  • the data demonstrate instances with more prolonged periods of airflow obstruction, providing valuable insights into respiratory patterns and abnormalities.
  • FIG. 20Furthermore the BAMS device reliably monitors respiratory sounds, heart rate, and other physiological parameters over a more prolonged period of 3 hours in a cohort of 5 in-NICU neonates.
  • the difference in heart rate determined using cardiac sounds and ECG waveforms is 0.015 bpm, with a standard deviation of 0.85 bpm, as shown in FIG. 24.
  • Respiratory sounds align well with chest movements and with data from respiratory inductance plethysmography (RIP) and nasal temperature, as shown in FIG. 25.
  • the difference in respiratory rate determined by respiratory sounds and nasal temperature data is 0.06 bpm, with a standard deviation of 1.92 bpm, as shown in FIG. 26.
  • the system's capabilities can be extended by mounting two devices with time synchronization — one at the suprasternal notch and the other at the right upper chest — to investigate the movement of air through the trachea and the percentage of air transmitted to the lungs, as shown in FIG. 27.
  • FIG. 29(A) displays such a system attached to the right upper and left lower abdomen of an infant.
  • FIGS. 19(B) and 19(C) present spectrograms and sound intensities recorded from the right upper abdomen before and after feeding, respectively.
  • the data processing flow presented in FIG. 30 identifies peaks in the sound intensity that exceed a certain threshold when accelerations associated with motion are less than 0.1 g, to eliminate artifacts that can arise from physical contact with the device.
  • the trends in normalized intensity and bowel sound peak counts captured from the right and left abdomen appear in FIG. 29(D). The difference in normalized intensities yields spatio-temporal information related to intestinal motility.
  • the number of peaks in bowel sounds for 3 infants increase from an average of 5/min to 21/min before and after feeding, respectively, as shown in FIG. 29(E).
  • the average intensity in the right upper abdomen is 27.5 dB before feeding and 36.9 dB after, as shown in FIG. 29(F).
  • Post feeds peaks distribute mainly in the right upper quadrant of the abdomen for the first 15 minutes and then largely migrate to the left lower quadrant of the abdomen for the next 15 minutes, as shown in FIG. 29(G).
  • PCA patient-controlled analgesia
  • FIG. 32A(A) and FIG. 34 display CT images of the lungs of a healthy subject (Patient A) alongside spectrograms of sounds over 150 Hz captured by the BAMS devices during inhalation and exhalation.
  • FIG. 32A(B) and FIG. 35 display corresponding results for a patient with chronic lung disease (radiation pneumonitis and fibrosis), who additionally had their right upper lung lobe, and part of their left upper lung lobe and right lower lung lobe resected (Patient B).
  • chronic lung disease radiation pneumonitis and fibrosis
  • Patient A exhibit similar distributions of chest wall movement, maximum sound intensities, and sound frequencies for the left and right sides of the body, as shown in FIG. 32B(1).
  • the decrease in the frequencies and intensities of sounds from the lower chest result from physiologic reduced rates of airflow and increased thickness of the chest wall.
  • FIG. 37 presents a comparative analysis of data obtained from healthy subjects and patients with chronic lung diseases. This analysis highlights the significance of airflow rate, airflow volume, and sound frequency for the diagnosis of obstructive and restrictive lung diseases.
  • the results rely on data from BAMS devices mounted on the suprasternal notch and the upper and lower posterior regions of the chest, along with separate measurements of nasal airflow rate and flow volume using a peak flow meter.
  • the airflow rate corresponds to the maximum sound intensity of the cumulative power spectral density at 150 Hz and higher.
  • An additional parameter, the sound energy can be calculated by integrating the sound intensity over time, for comparison to the nasal airflow volume, as shown in FIG. 38.
  • FIG. 37(A) shows a correlation between sound intensity measured at different locations (suprasternal notch, upper posterior and lower posterior thorax) and nasal airflow rate for 10 healthy subjects.
  • the Pearson’s correlation values between sound intensity and nasal airflow rate are 0.73, 0.79, and 0.75 at the suprasternal notch, upper and lower posterior positions, respectively.
  • correlation values between sound energy and nasal airflow volume are 0.71, 0.76, and 0.75 at these corresponding locations, respectively, as shown in FIG. 37(B).
  • FIG. 37(C) illustrates the dominant frequency distribution of lung sounds in healthy subjects at each location. This information is relevant in monitoring obstruction and airway conditions in patients with heterogeneous lung disease states.
  • these parameters can assist with tracking of disease progression or response to treatment in these chronic lung disease patients.
  • the estimation of airflow rate and air volume based on data from the BAMS devices can additionally facilitate monitoring of the Tiffeneau-Pinelli index, with the potential for routine, daily monitoring of lung disease and diagnosis of obstructive and restrictive pulmonary diseases.
  • FIG. 37(D) and (F) show the sound intensity measured at the suprasternal notch and the ratio of intensities from the left and right upper anterior chest for healthy subjects, chronic lung disease patients with no lung resections, and patients with right upper lobe or left upper lobe resections. Healthy subjects exhibit higher sound intensity at the suprasternal notch than chronic lung disease patients, with an average intensity of 54 dB. In contrast, chronic lung disease patients without lung resections, those with left upper lung resections, and right upper lung resections have average intensities of 38 dB, 30 dB, and 36 dB, respectively.
  • the average sound intensity ratios (left upper lung sound intensity / right upper lung sound intensity) are 0.98, 1.01, 0.78, and 1.5, respectively, consistent with a reduction in sound intensities at locations of resected lung tissues.
  • the variations in this ratio exceed those attributable to uncertainties in attachment position, as depicted in FIG. 39.
  • FIG. 37(E) and (G) compare the dominant expiratory frequency of the right upper posterior lung between healthy patients and those with chronic lung disease. The latter group exhibits an average frequency of 256 Hz.
  • the healthy subjects show frequencies of 219 Hz, distinguishing them from the patients with lung disease (E- value ⁇ 0.05).
  • the onset of lung disease increases airway restrictions, thereby increasing the dominant sound frequency.
  • FIGS. 40, 41A, 41B and 41C show notable differences between healthy subjects and patients with lung conditions, as presented in FIGS. 40, 41A, 41B and 41C. Specifically, in each of FIGS. 40, 41A, 41B and 41C, the box plots show the range between the 25th and 75th percentiles, with the median indicated by the midline for each subject type.
  • the present study introduces a technology designed for simultaneous measurements of body movements and sounds as a reliable source of physiological signals, with applicability in the hospital and at home. Demonstration examples span from premature neonates with respiratory and digestive disorders in the NICU to adult patients with lung disease in pulmonology clinics and patients in thoracic surgery clinic. Various characterization studies and performance benchmarking measurements confirmed the accuracy of the system, and the uniqueness of its operational capabilities.
  • the dual-microphone (body- and ambient-facing) design, the sound separation algorithms, the broadband capabilities, the time-synchronized operation of networks of devices, and the small, skin-compatible form factors, have created a broad range of unique possibilities in patient monitoring that deserve evaluation.
  • the BAMS device can detect both airflow (using the dual -mi crophone setup) and chest movements (using the IMU component), which in combination allow for the identification and classification of all apnea subtypes (central, obstructive, and mixed apneas).
  • apneas are ubiquitous in preterm infants and are a leading cause of in-hospital morbidities and prolonged NICU hospitalization, yet cannot be accurately distinguished in terms of subtype (central, obstructive, mixed) using current monitoring standards.
  • enhanced apnea detection and classification in this population may lead to more targeted and personalized management approaches, improved patient outcomes, and reduced length of hospitalization and costs.
  • the BAMS system may aid in quantifying the degree of airflow obstruction in at-risk term neonates, such as infants with severe hypotonia (ex: Trisomy 18, Prader-Willi Syndrome) and congenital upper airway obstruction (ex: Pierre-Robin Sequence).
  • resulting data can provide real-time feedback whenever the air entry is diminished on one side relative to the other; this may promptly alert the clinician of a possible pathology such as atelectasis, consolidation, or a pneumothorax, thereby leading to early diagnosis and treatment.
  • reduced bowel sounds may act as an early warning sign for impending gastro-intestinal complication such as bowel dysmotility, obstruction or necrotizing enterocolitis, or sensitivity to opiates.
  • increasing bowel sounds may serve as objective markers of improved peristalsis and bowel health after a gastro-intestinal surgery, thereby aiding in the decision to resume or progress feeds.
  • Standard pulmonary function tests do not provide regional lung function assessments, as they provide just a single numeric value meant to represent both lungs as an aggregate. These tests operate under the assumption that all regions of the lung contribute equally to function, which is not the case, particularly in chronic lung disease.
  • the technology reported here can be utilized alone or in conjunction with pulmonary function tests, and in both the inpatient and outpatient setting, where it can provide real time insight into regional lung function and disease status. These capabilities are of particular interest to thoracic surgeons when performing pre-operative planning as it provides information on how much an area of lung that they are planning to resect may contribute to overall respiratory function. This knowledge can allow thoracic surgeons to better advise on patients’ management and more appropriately risk stratify patients at high risk for post-operative complications.
  • the portable nature of the BAMS system allows for easy use in various settings, including home environments. As a result, it facilitates post-operative management of lung resection through daily monitoring, enabling tracking of regional lung recovery and the development of any detrimental complications, ultimately enhancing the quality of medical management. Additionally, if the time scale is extended to many hours /days, this would allow for the gathering of more clinical information to assess if any underlying pathology has temporal or circadian symptoms (z.e., worse in morning, at night, when exercising or sleeping) or environmental perturbations (i.e., orthostatic or other postural changes). Interest extends to providers who treat chronic lung diseases, such as chronic obstructive pulmonary disease, interstitial lung disease and pneumonia, as these devices can allow them to monitor patient’s regional lung function to assess the efficacy of current medical management.
  • chronic lung diseases such as chronic obstructive pulmonary disease, interstitial lung disease and pneumonia
  • the BAMS devices may assist providers in determining ventilator settings, by giving them real-time feedback on regional lung ventilation to ensure adequate respiratory support. Given the lack of portable diagnostic tools, providing real-time regional lung function assessment at the bedside is critically important since patients currently must be transferred off the intensive care unit to obtain other diagnostic tests, which can put the patient in danger.
  • Additional possibilities include (1) monitoring of swallowing events and respiratory cycles for patients with dysphagia, (2) tracking of patterns of speech for patients with dementia and (3) measuring a collection of parameters, including HRV, related to cardiorespiratory function for patients with diabetes, high blood pressure, cardiac arrhythmias, asthma, anxiety, and depression. These and other possibilities in recordings of unusual biophysical markers represent areas of current work.
  • Each device included five components: a pair of microphones, an EMU, Flash memory, a Bluetooth SoC, and hardware for power and wireless charging. Locating the first two components on separate islands with serpentine traces as interconnects enhanced the mechanical deformability of the system.
  • the ambient-facing and body -facing microphones (ICS-40180, TDK) each connected to an amplifier circuit with a 64-fold gain and a bandpass filter from 10 Hz to 2 kHz. The amplified signal was converted into a 14-bit ADC value at a sampling rate of 1 kHz.
  • the IMU (LSM6DSL, STMicroelectronics) delivered three-axis acceleration data at a sampling rate of 104 Hz to the Bluetooth SoC (ISP- 1807, Insight SIP) via serial peripheral interface communication protocols.
  • the microphone data at 1 kHz and the IMU data at 104 Hz passed into 2 GB Flash memory (MT29F2G, Micron) with time stamps defined using an internal clock at 16 MHz.
  • 2GB Flash memory By utilizing a 2GB Flash memory, the inventors are able to store data in the local memory for up to 16 hours. For continuous monitoring over periods longer than 16 hours, we can transfer the data in real-time to an iPad/iPhone placed nearby. Local memory can then be used for data storage during other times, as shown in FIG. 42.
  • the wireless charging and power components included a charging coil with a resonance frequency of 13.56 MHz, a voltage rectifier, a voltage regulator, a battery charger IC, and a 3.7 V lithium-polymer battery (110 mAh).
  • Customized firmware was uploaded to the Bluetooth SoC using Segger Embedded Studio.
  • a silicone elastomer (Silbione-4420) defined an encapsulating structure, with overall dimensions of 40 x 20 mm 2 , a thickness of 8 mm, and a weight of 6 g.
  • the BAMS system incorporates a wireless charging scheme that operates at a standard radio frequency band of 13.56 MHz, which is approved by the Federal Communications Commission (FCC) for use in industrial, scientific, and medical devices.
  • This frequency band is chosen for its minimal absorption in living tissues, ensuring the safety and well-being of the user during charging.
  • the BAMS device is removed from the body for charging, ensuring a convenient and hassle-free charging experience.
  • the device can be considered for charging while still being worn by the baby.
  • the signal-to-noise ratio (SNR) of respiratory and cardiac sounds captured using the commercial digital stethoscope decreased by 12% and 15%, respectively.
  • the SNR decreased by 62% and 48%, respectively (FIG. 9).
  • the reduction in SNR was only 2% and 4% for the BAMS device.
  • the scheme for time synchronization between multiple devices exploited a master device to broadcast its 16 MHz local clock information through RF signals at 100 ms intervals to slave devices with different RF addresses. Updates to the local clock information of the slave devices used the clock information received from the master. This clock information also passed to the mobile device, for storage in memory with the coordinated universal time.
  • Characterization of the accuracy of this scheme involved monitoring the peak delay between 13 devices exposed to sound swept from 500 Hz to 1 kHz sourced from a vibration generator at a speed of 5 Hz/s.
  • Cross-correlation of time series sound data defined the time delays between each device.
  • the results showed an average timing difference of 0.2 ms and a standard deviation of 6 ms, as shown in FIG. 31.
  • a master device was placed next to the monitoring iPad to transmit accurate time information.
  • sound from an external metronome was used to calculate the time differences of sound peaks recorded by each sensor. The results, as shown in FIG.
  • Data collected from the body-facing and ambient-facing microphones included contributions from body sounds and ambient sounds. Sound separation used a two-step adaptive filtering method, as depicted in FIG. 8.
  • the ambient sound noise signal is extracted by subtracting the body-facing microphone's sound signal from the ambientfacing microphone's sound signal.
  • the body sound signal is obtained by subtracting the ambient sound noise signal, extracted by the first adaptive filtering, from the sound signal of the body-facing microphone.
  • RLS recursive least squares
  • the respiratory sound intensity between 150 Hz and 300 Hz exhibited a signal-to-noise ratio (SNR) of 27 dB without ambient noise, but it decreased to 17 dB in the presence of 70 dB ambient noise.
  • SNR signal-to-noise ratio
  • the sound separation techniques employed in the BAMS device effectively separated the respiratory sound signals from the ambient noise, as shown in FIG. 45(D). Clear respiratory sound signals were observed on the spectrogram even in the presence of ambient noise after applying the sound separation process. The SNR of the sound intensity was maintained at 26 dB.
  • the data were processed using a two-step adaptive filtering method and subjected to low- pass and high-pass filtering (third order, with an attenuation rate of -58 dB/decade) with a cutoff frequency of 150 Hz.
  • This filtering process effectively distinguished between respiratory and cardiac sounds based on their frequency characteristics.
  • the analysis revealed that 76% of the total signal for respiratory sounds exists above 150 Hz, while 81% of the total signal for cardiac sounds exists below 150 Hz, as shown in FIG. 46.
  • Short-time Fourier transform (STFT) yielded power spectral density information for each frequency of the filtered signal, with a window size of 0.03 seconds and overlap length of 0.027 seconds.
  • STFT Short-time Fourier transform
  • the 3-axis acceleration signal obtained from the IMU was filtered using a Butterworth low-pass filter (third order) with a cut-off frequency of 0.1 Hz.
  • the body orientation was calculated from the filtered signals via simple trigonometry.
  • the chest movement signal was obtained by applying a bandpass filter (third order) with a frequency range between 0.1 Hz and 1 Hz.
  • the chest movement signal correlated well with the detected respiratory sounds from the microphone, even under low-frequency movements such as resting (near 0 Hz movement), walking (0.8 Hz movement), and squatting (0.2 Hz movement), as shown in FIG. 48.
  • Physical activity levels were monitored using the root mean square of the acceleration values along the x, y, and z axes, processed with a Butterworth bandpass filter (third order) between 1-10 Hz.
  • the data were processed using a two-step adaptive filtering method with a bandpass filtered between 150 Hz and 400 Hz to eliminate heart sound.
  • a short-time Fourier transform (STFT) of the filtered signal with a window size of 0.03 seconds and overlap length of 0.027 seconds, yielded the power spectral density. Integration of these data over frequencies higher than 150 Hz yielded bowel sound intensity data. Sound peaks with widths of less than 100 ms and intensities greater than 20 dB were then identified.
  • STFT short-time Fourier transform
  • a medical-grade adhesive (2477P, 3M Medical Materials & Technologies, NM,USA) is used as interface between BAMS and skin body that is widely recognized and approved for use in the context of bandages (ISO 10993-5) and for the fragile skin of preterm infants.
  • This adhesive allowed for reliable and secure fixation of the device to the infants in this study, with a median gestational age of 28 weeks (min 25 to max 31 weeks) and a median postmenstrual age of 35 weeks (min 33 to max 36 weeks), with no adverse skin reactions during placement or after removal of the sensors.
  • the study protocol was approved by the Northwestern Medicine Institutional Review Board (STU00218021) and the McGill University Health Center Research Ethics Board (IRB00010120). Informed consent was obtained from all participants or their guardians. Trained research staff placed BAMS devices on the participants in a location that did not interfere with clinical monitoring equipment. Monitoring in the neonatal intensive care unit included ECG, nasal temperature, chest and abdomen movements using respiratory inductance plethysmography (RIP), and a pneumotachograph device. Research staff recorded additional information such as clinical data, infant movement, and fussing during data collection. For the lung sound patient study, the research staff attached 13 devices to the anterior and posterior chest as follows.
  • Patient information was obtained retrospectively from the medical records, including demographic data, smoking status, medical history, spirometry data, vital signs, and results from diagnostic tests, including computed tomography images.
  • FIG. 52 shows an example of periodic breathing detected using a wireless acoustic sensor.
  • the data as shown in FIG. 52 was obtained from a sample recording of a preterm infant in the pilot project. Specifically, the following signals are presented in FIG. 52, from top to bottom: (1) airflow signal derived from the microphone component of the wireless acoustic sensor; (2) abdominal excursions derived from respiratory inductance plethysmography; (3) chest wall movements derived from the inertial measurement unit component of the wireless acoustic sensor; and (4) oxygen saturation signal.
  • the recording shows an example of periodic breathing, as there are three normal breathing cycles separated by two brief central apneas (absence of airflow and chest or abdominal wall excursions). This respiratory event led to an oxygen desaturation.
  • FIG. 53 shows an example of hypopnea and central apnea detected using the wireless acoustic sensor.
  • the data as shown in FIG. 53 was obtained from a sample recording of a preterm infant in the pilot project.
  • the following signals are presented in FIG. 53, from top to bottom: (1) airflow signal derived from a nasal thermistor; (2) airflow signal derived from the microphone component of the wireless acoustic sensor; (3) abdominal excursions derived from respiratory inductance plethysmography; (4) chest wall movements derived from the inertial measurement unit component of the wireless acoustic sensor; and (5) oxygen saturation signal.
  • the recording shows an example of a hypopnea, characterized by a partial reduction in the amplitude of the airflow signal and chest and abdominal wall excursions (red dotted rectangle). This is followed by a brief period of regular breathing and then a brief central apnea, characterized by absence of airflow and chest or abdominal wall excursions (green dotted rectangle).
  • Pharyngoesophageal and cardiorespiratory interactions potential implications for premature infants at risk of clinically significant cardiorespiratory events.

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Abstract

Cette invention concerne des systèmes et des procédés de détection acousto-mécanique à large bande (BAMS) destinés à surveiller des signaux physiologiques d'un sujet vivant, et leurs applications. Plus précisément, le système comprend un ou plusieurs dispositifs BAMS interfacés avec la peau et sans fil disposés sur le sujet vivant pour former un réseau corporel sans fil. Les dispositifs BAMS sont synchronisés dans le temps et communiquent sans fil entre eux, et chaque dispositif BAMS comprend un accéléromètre, tel qu'une unité de mesure inertielle (IMU), pour capturer des données d'accélération provenant du sujet vivant et un dispositif mécanique acoustique configuré pour capturer des sons corporels provenant du sujet vivant. Un dispositif de commande synchronisé dans le temps et communiquant sans fil avec les dispositifs BAMS est utilisé pour gérer et traiter les données d'accélération et les sons corporels provenant du ou des dispositifs BAMS afin de générer des informations de mouvements corporels et de sons corporels du sujet vivant.
PCT/US2024/054723 2023-11-06 2024-11-06 Capteurs acousto-mécaniques à large bande sans fil en tant que réseaux corporels sans fil aux fins d'une surveillance physiologique continue Pending WO2025101606A1 (fr)

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Citations (5)

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Publication number Priority date Publication date Assignee Title
US20200000371A1 (en) * 2018-04-27 2020-01-02 Respira Labs Llc Systems, Devices, and Methods for Performing Active Auscultation and Detecting Sonic Energy Measurements
CN112336320A (zh) * 2019-08-09 2021-02-09 陈汝建 一种智能项链及心音、肺音、颈大血管音的监测方法
US20210386300A1 (en) * 2018-10-31 2021-12-16 Northwestern University Apparatus and method for non-invasively measuring physiological parameters of mammal subject and applications thereof
WO2023043866A1 (fr) * 2021-09-15 2023-03-23 Northwestern University Appareil et procédé de mesure de paramètres physiologiques d'un sujet mammifère à l'aide d'une électronique flexible facilement amovible et leurs applications
US20230148954A1 (en) * 2020-03-31 2023-05-18 Resmed Sensor Technologies Limited System And Method For Mapping An Airway Obstruction

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200000371A1 (en) * 2018-04-27 2020-01-02 Respira Labs Llc Systems, Devices, and Methods for Performing Active Auscultation and Detecting Sonic Energy Measurements
US20210386300A1 (en) * 2018-10-31 2021-12-16 Northwestern University Apparatus and method for non-invasively measuring physiological parameters of mammal subject and applications thereof
CN112336320A (zh) * 2019-08-09 2021-02-09 陈汝建 一种智能项链及心音、肺音、颈大血管音的监测方法
US20230148954A1 (en) * 2020-03-31 2023-05-18 Resmed Sensor Technologies Limited System And Method For Mapping An Airway Obstruction
WO2023043866A1 (fr) * 2021-09-15 2023-03-23 Northwestern University Appareil et procédé de mesure de paramètres physiologiques d'un sujet mammifère à l'aide d'une électronique flexible facilement amovible et leurs applications

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