Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient; User input means
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient; User input means
  • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms

Definitions

• a computing device that provides such alarms determines an alarm context, and configures the alarm based on the alarm context.
• the duration and/or intensity of the alarm may be relatively greater when the patient is less likely to be alert or otherwise quickly able to cancel the alarm and/or when the acute health event is less severe and/or less likely to be actually occurring.
• the duration of the alarm may be relatively shorter when the patient is more likely to be alert or otherwise quickly able to cancel the alarm and/or when the type of acute health event is more se vere and/or more likely to be actually occurring.
• Adaptive alarms according to the techniques of this disclosure may advantageously improve the ability of the user to override alarms when appropriate, while shortening an alarm period when the acute health event is se vere and/or likely, e.g., a high probability of lethal tachyarrhythmia which may lead to SCA.
• a variety of types of implantable and external devices are configured to detect arrhythmia episodes and other acute health events based on sensed ECGs and, in some cases, other physiological signals.
• External devices that may be used to non-invasively sense and monitor ECGs and other physiological signals include wearable devices with electrodes configured to contact the skin of the patient, such as patches, watches, rings, necklaces, hearing aids, a wearable cardiac monitor or automated external defibrillator (AED), clothing, car seats, orbed linens.
• Such external devices may facilitate relatively longer-term monitoring of patient health during normal daily activities.
• only one of computing devices 12, e.g., computing device 12A, is configured for communication with IMD 10, e.g., due to execution of software (e.g., part of a health monitoring application as described herein) enabling communication and interaction with an IMD.
• software e.g., part of a health monitoring application as described herein
• IMD 10 may complete an initial detection of the acute health event, e.g., SCA or tachyarrhythmia, and initiate wireless communication, e.g., Bluetooth® or Bluetooth Low Energy®, with computing device(s) 12 in response to the initial detection.
• the initial detection may occur five to ten seconds after onset of the acute health event, for example.
• IMD 10 may continue monitoring to determine whether the acute health event is sustained, e.g., a sustained detection of SCA or tachyarrhythmia.
• IMD 10 may use more patient parameters and/or different rules to determine whether event is sustained or otherwise confirm detection.
• loT devices 30 may provide audible and/or visual alarms when configured with output devices to do so. As other examples, loT devices 30 may cause smart lights throughout environment 28 to flash or blink and unlock doors. In some examples, loT devices 30 that include cameras, microphones, or other sensors may activate those sensors to collect data regarding patient 4, e.g., for evaluation of the condition of patient 4.
• Environment 28 includes computing facilities, e.g., a local network 32, by which computing devices 12, loT devices 30, and other devices within environment 28 may communicate via network 16, e.g., with HMS 22.
• environment 28 may be configured with wireless technology, such as IEEE 802.11 wireless networks, IEEE 802. 15 ZigBee networks, an ultra-wideband protocol, near-field communication, or the like.
• Environment 28 may include one or more wireless access points, e.g., wireless access points 34A and 34B (collectively, “wireless access points 34”) that provide support for wireless communications throughout environment 28.
• wireless access points 34A and 34B collectively, “wireless access points 34”
• computing devices 12, loT devices 30, and other devices within environment 28 may be configured to communicate with network 16, e.g., with HMS 22, via a cellular base station 36 and a cellular network.
• Responses from the user may be used to confirm or override detection of the acute health event by IMD 10, or to provide additional information about the acute health event or the condition of patient 4 more generally that may improve the efficacy of the treatment of patient 4.
• information received by the event assistant may be used to provide an indication of severity or type (differential diagnosis) for the acute health event.
• the event assistant may use natural language processing and context data to interpret utterances by the user.
• the event assistant in addition to receiving responses to queries posed by the assistant, the event assistant may be configured to respond to queries posed by the user. For example, patient 4 may indicate that they feel dizzy and ask the event assistant, “how am I doing?”.
• computing device(s) 12 and/or HMS 22 may implement one or more techniques to evaluate the sensed physiological data received from IMD 10, and in some cases additional physiological or other patient parameter data sensed or otherwise collected by the computing device(s) or loT devices 30, to confirm or override the detection of the acute health event by IMD 10.
• computing device(s) 12 and/or computing system(s) 20 may have greater processing capacity than IMD 10, enabling more complex analysis of the data.
• the computing device(s) 12 and/or HMS 22 may apply the data to one or more machine learning models or other artificial intelligence developed algorithms, e.g., to determine whether the data is sufficiently indicative of the acute health event.
• computing device(s) 12 may output alert messages and/or transmit alert messages to HMS 22 and/or loT devices 30 in response to confirming the acute health event.
• computing device(s) 12 may be configured to output/transmit the alert messages prior to completing the confirmation analysis, and output/transmit cancellation messages in response to the analysis overriding the detection of the acute health event by IMD 10.
• HMS 22 may be configured to perform a number of operations in response to receiving an alert message from computing device(s) 12 and/or loT device(s) 30.
• HMS 22 may be configured to cancel such operations in response to receiving a cancellation message from computing device(s) 12 and/or loT device(s) 30.
• HMS 22 may be configured to transmit an alert message to computing device 42 of bystander 26, which may improve the timeliness and effectiveness of treatment of the acute health event of patient 4 by bystander 26.
• Computing device 42 may be similar to computing devices 12 and computing devices 38, e.g., a smartphone.
• HMS 22 may determine that bystander 26 is proximate to patient 4 based on a location of patient 4, e.g., received from computing device(s) 12, and a location of computing device 42, e.g., reported to HMS 22 by an application implemented on computing device 42.
• HMS 22 may transmit the alert message to any computing devices 42 in an alert area determined based on the location of patient 4, e.g., by transmitting the alert message to all computing devices in communication with base station 36, using any of the networking methods described herein.
• the alert message to bystander 26 may be configured to assist a layperson in treating patient.
• the alert message to bystander 26 may include a location (and in some cases a description) of patient 4, the general nature of the acute health event, directions for providing care to patient 4, such as directions for providing cardiopulmonary resuscitation (CPR), a location of nearby medical equipment for treatment of patient 4, such as an automated external defibrillator (AED) 44 or life vest, and instructions for use of the equipment.
• computing device(s) 12, loT device(s) 30, and/or computing device 42 may implement an event assistant configured to use natural language processing and context data to provide a conversational interface for bystander 42. The assistant may provide bystander 26 with directions for providing care to patient 4 and respond to queries from bystander 26 about how to provide care to patient 4.
• HMS 22 may mediate bi-directional audio (and in some cases video) communication between care providers 40 and patient 4 or bystander 26.
• Such communication may allow care providers 40 to evaluate the condition of patient 4, e.g., through communication with patient 4 or bystander 26, or through use of a camera or other sensors of the computing device or loT device, in advance of the time they will begin caring for the patient, which may improve the efficacy of care delivered to the patient.
• Such communication may also allow the care providers to instruct bystander 42 regarding first responder treatment of patient 4.
• HMS 22 may control dispatch of a drone 46 to environment 28, or a location near environment 28 or patient 4.
• Drone 46 may be a robot and/or unmanned aerial vehicle (UAV).
• Drone 46 may be equipped with a number of sensors and/or actuators to perform a number of operations.
• drone 46 may include a camera or other sensors to navigate to its intended location, identify patient 4 and, in some cases, bystander 26, and to evaluate a condition of patient.
• drone 46 may include user interface devices to communicate with patient 4 and/or bystander 26.
• drone 46 may provide directions to bystander 26, to the location of patient 4 and regarding how to provide first responder care, such as CPR, to patient 4.
• drone 46 may carry medical equipment, e.g., AED 44, and/or medication to the location of patient 4.
• Any of IMD 10, computing device(s) 12, loT device(s) 30, computing device(s) 38 and 42, AED 44, drone 46, or HMS 22 may, individually or in any combination, perform the operations described herein for detection of acute health events, such as SCA, by applying rules, which may include one or more machine learning models, to patient parameter data to detect acute health events.
• rules which may include one or more machine learning models
• one of these devices, or more than one of them in cooperation may apply a first set of rules to patient parameter data for a first determination of whether an acute health event is detected and, based on whether one or more context criteria associated with the first determination are satisfied, determine whether to apply a second set of rules to patient parameter data to determine whether the acute health event is detected.
• FIG. 2 is a block diagram illustrating an example configuration of IMD 10 of FIG.
• IMD 10 includes processing circuitry 50, memory 52, sensing circuitry 54 coupled to electrodes 56A and 56B (hereinafter, “electrodes 56”) and one or more sensor(s) 58, and communication circuitry 60.
• Processing circuitry 50 may include fixed function circuitry and/or programmable processing circuitry.
• Processing circuitry 50 may include any one or more of a microprocessor, a controller, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry.
• processing circuitry 50 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more GPUs, one or more TPUs, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry.
• memory 53 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed herein to IMD 10 and processing circuitry 50.
• Memory 53 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random-access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically- erasable programmable ROM (EEPROM), flash memory, or any other digital media.
• RAM random-access memory
• ROM read-only memory
• NVRAM non-volatile RAM
• EEPROM electrically- erasable programmable ROM
• flash memory or any other digital media.
• Sensing circuitry 54 may monitor signals from electrodes 56 in order to, for example, monitor electrical activity of a heart of patient 4 and produce ECG data for patient 4.
• processing circuitry 50 may identify features of the sensed ECG, such as heart rate, heart rate variability, T-waveretemans, intra-beat intervals (e.g., QT intervals), and/or ECG morphologic features, to detect an episode of cardiac arrhythmia of patient 4.
• Example Processing circuitry 50 may store the digitized ECG and features of the ECG used to detect the arrhythmia episode in memory 52 as episode data for the detected arrhythmia episode.
• sensing circuitry 54 measures impedance, e.g., of tissue proximate to IMD 10, via electrodes 56.
• the measured impedance may vary based on respiration, cardiac pulse or flow, and a degree of perfusion or edema.
• Processing circuitry 50 may determine physiological data relating to respiration, cardiac pulse or flow, perfusion, and/or edema based on the measured impedance.
• IMD 10 includes one or more sensors 58, such as one or more accelerometers, gyroscopes, microphones, optical sensors, temperature sensors, pressure sensors, and/or chemical sensors.
• sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from one or more of electrodes 56 and/or sensors 58.
• sensing circuitry 54 and/or processing circuitry 50 may include a rectifier, filter and/or amplifier, a sense amplifier, comparator, and/or analog-to-digital converter.
• Processing circuitry 50 may determine physiological data, e.g., values of physiological parameters of patient 4, based on signals from sensors 58, which may be stored in memory 52.
• Patient parameters determined from signals from sensors 58 may include oxygen saturation, glucose level, stress hormone level, heart sounds, body motion, body posture, or blood pressure.
• Memory 52 may store applications 70 executable by processing circuitry 50, and data 80.
• Applications 70 may include an acute health event surveillance application 72.
• Processing circuitry 50 may execute event surveillance application 72 to detect an acute health event of patient 4 based on combination of one or more of the types of physiological data described herein, which may be stored as sensed data 82.
• sensed data 82 may additionally include patient parameter data sensed by other devices, e.g., computing device(s) 12 or loT device(s) 30 and received via communication circuitry 60.
• Event surveillance application 72 may be configured with a rules engine 74.
• Rules engine 74 may apply rules 84 to sensed data 82.
• Rules 84 may include one or more models, algorithms, decision trees, and/or thresholds. In some cases, rules 84 may be developed based on machine learning, e.g., may include one or more machine learning models.
• Memory 132 may be configured to store information within computing device 12, for processing during operation of computing device 12.
• Memory 132 in some examples, is described as a computer-readable storage medium.
• memory 132 includes a temporary memory or a volatile memory. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.
• RAM random access memories
• DRAM dynamic random access memories
• SRAM static random access memories
• Memory 132 in some examples, also includes one or more memories configured for long-term storage of information, e.g., including non-volatile storage elements.
• the processing circuitry may determine whether the one or more context criteria are satisfied in the manner described with respect to FIG. 5.
• the first and second patient parameter data may be determined from the same patient parameters or (with respect to at least one parameter) different patient parameters.
• the first patient parameter data and the second patient parameter data include at least one common patient parameter, and the processing circuitry may change a mode sensing for the common patient parameter between the first patient parameter data and the second patient parameter data in response to satisfaction of the one or more context criteria. For example, the processing circuitry may change a sampling frequency for the common patient parameter.
• the processing circuitry may determine that a context criterion is satisfied by detecting that IMD 10 has flipped or otherwise migrated within patient 4. Such migration may lead to significant changes in patient parameter data, e.g., ECG data, impedance data, or heart sound data. Changing a mode employed by IMD 10 to sense one or more patient parameters or changing rules to account for changes in patient parameter data resulting from device migration, may help ameliorate the device migration and maintain effective acute health event detection. In addition to the mode of sensing and/or rules, the processing circuity may adjust other aspects of system, such mode of wireless communication between the IMD and other devices. Techniques for detecting and mitigating migration of IMD 10 are described in commonly-assigned U.S. Patent Application No.
• the processing circuitry determines that the one or more context criteria are satisfied when the processing circuitry determines that the acute health event, e.g., ventricular tachyarrhythmia or SCA, is detected, but the patient or another user cancels the alarm or otherwise provides user input contradicting the determination.
• the processing circuitry may modify one or both of the sensed patient parameters or the rules applied to the patient parameter data.
• the patient may have tolerated a rapid ventricular tachycardia that lasted for a sustained period (e.g., a programmed 10 or 20 seconds), but could experience another arrhythmia, e.g., syncope, soon even though the patient believes they are OK.
• the modification may include adapting the rules based on the rhythm. Sometimes a long duration episode accelerates to ventricular fibrillation or more rapid ventricular tachycardia.
• the modification could include changing a heart rate threshold, e.g., applying hysteresis to the heart rate threshold.
• ventricular fibrillation becomes difficult to sense.
• the modification may include changing a ventricular depolarization detection threshold to allow more undersensing of depolarizations.
• an ensemble of neural networks may include CNNs and/or recurrent neural networks.
• One or more neural networks of the ensemble may be trained to discriminate or classify based on raw ECG data collected by IMD 10 as an input.
• One or more networks of the ensemble may be trained to discriminate or classify based on custom features determined by IMD 10 from the ECG or other signals sensed by IMD 10 or determined by the processing circuitry implementing the second set of rules (e.g., processing circuitry of any of, or any combination of, the devices of system 2).
• FIG. 9 is a block diagram illustrating an example of an ensemble 400 of neural networks configured to classify ventricular tachyarrhythmias.
• Processing circuitry e.g., processing circuitry 130 of computing device 12 or loT device 30, may apply a plurality of inputs 402 to a plurality of neural networks 404 of ensemble 400.
• Inputs 402 include raw signal inputs 406A or other raw parameter data of patient 4, e.g., from IMD 10 or other devices as described herein, and inputs 406B comprising features derived from the raw data.
• Inputs 406A may include a raw ECG segment sensed by IMD 10 including a ventricular tachyarrhythmia onset detected by IMD 10 based on the ECG, and a raw ECG segment sensed by IMD 10 including a portion of the ECG by which IMD 10 determined the ventricular tachyarrhythmia was sustained.
• the features may include a sequence of R-R intervals during, and in some cases prior to, detection of the ventricular tachyarrhythmia by IMD 10, an overly of raw ECG data and R- sense timing information, autocorrelation, cross-correlation, and/or wavelet transformation of ECG signal data, a histogram of R-R intervals, and a temporal history of prior ventricular tachyarrhythmia episodes detected by IMD 10 and their adjudication by the processing circuitry applying the ensemble 400.
• Inputs 402 may also include any other sensed parameters of patient 4, e.g., sensed by IMD 10 or other devices as described above.
• inputs 406B may include a feature determined by the processing circuitry based on a temporal history of other sensed parameters of patient 4.
• one or more inputs 402 or portions thereof may be fed into separate individual neural networks 404, which may include 1 or 2-dimensional CNNs, RNNs, or long short-term memory (LSTM) memory networks (which may be a type of RNN).
• the processing circuitry may flatten 408 and concatenate 410 the outputs from the plurality of neural networks to provide ensemble 400.
• the processing circuitry may apply the flattened and concatenated outputs to a fully connected layer 412, and the outputs of the fully connected layer to one or more SoftMax functions 414.
• the outputs of the one or more SoftMax functions 414 are probabilities 416, e.g., respective probabilities of different classifications of the data for the episode of ventricular tachyarrhythmia detected by IMD 10.
• the classifications are different classifications are PVT, MVT, SVT, noise, and oversensing.
• the processing circuitry e.g., processing circuitry 130 of computing device 12 or loT device 30, may determine a classification of the episode based on probabilities 416. In this manner processing circuitry may confirm or overrule the detection of a ventricular tachyarrhythmia by IMD 10.
• Ensemble 400 may be an example of a second set of rules as described above.
• the processing circuitry may combine the raw signals and derived features in a 2D array format (to form an input ensemble) for a single CNN or other neural network.
• FIG. 10 is a block diagram illustrating an example of a single classifier 430 utilizing raw signals and derived features as inputs 432. Inputs 432 of FIG. 10 may be substantially similar to inputs 406B of FIG. 9.
• the processing circuitry may concatenate 434 inputs 432.
• the processing circuitry may concatenate 434 inputs 432 to form a concatenated 2D array 436 of input values to be applied to a neural network 438 including one or more of an LSTM/RNN, rectifier function, and/or multiplex pooling layers.
• the processing circuitry may concatenate 440 the output of neural network 438 for application to a fully connected layer 442 and SoftMax function 444 to produce probabilities 446 in the manner described above with respect to FIG. 9.
• Classifier 430 may be an example of a second set of rules as described above.
• FIG. 16A is a perspective drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIGS. 1 and 2 as an ICM.
• IMD 10A may be embodied as a monitoring device having housing 812, proximal electrode 816A and distal electrode 816B.
• Housing 812 may further comprise first major surface 814, second major surface 818, proximal end 820, and distal end 822.
• Housing 812 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids.
• Housing 812 may be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection of electrodes 816A and 816B.
• proximal electrode 816A is at or in close proximity to the proximal end 820 and distal electrode 816B is at or in close proximity to distal end 822.
• distal electrode 816B is not limited to a flattened, outward facing surface, but may extend from first major surface 814 around rounded edges 824 and/or end surface 826 and onto the second major surface 818 so that the electrode 816B has a three- dimensional curved configuration.
• electrode 816B is an uninsulated portion of a metallic, e.g., titanium, part of housing 812.
• proximal electrode 816A is located on first major surface 814 and is substantially flat, and outward facing.
• proximal electrode 816A may utilize the three-dimensional curved configuration of distal electrode 816B, providing a three-dimensional proximal electrode (not shown in this example).
• distal electrode 816B may utilize a substantially flat, outward facing electrode located on first major surface 814 similar to that shown with respect to proximal electrode 816A.
• the various electrode configurations allow for configurations in which proximal electrode 816A and distal electrode 816B are located on both first major surface 814 and second major surface 818. In other configurations, such as that shown in FIG.
• IMD 10A may include electrodes on both major surface 814 and 818 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A.
• Electrodes 816A and 816B may be formed of a plurality of different types of biocompatible conductive material, e.g., stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.
• biocompatible conductive material e.g., stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.
• proximal end 820 includes a header assembly 828 that includes one or more of proximal electrode 816A, integrated antenna 830A, antimigration projections 882, and/or suture hole 834.
• Integrated antenna 830A is located on the same major surface (i.e., first major surface 814) as proximal electrode 816A and is also included as part of header assembly 828.
• Integrated antenna 830A allows IMD 10A to transmit and/or receive data.
• integrated antenna 830A may be formed on the opposite major surface as proximal electrode 816A or may be incorporated within the housing 812 of IMD 10A. In the example shown in FIG.
• anti-migration projections 832 are located adjacent to integrated antenna 830A and protrude away from first major surface 814 to prevent longitudinal movement of the device.
• anti-migration projections 832 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 814.
• antimigration projections 832 may be located on the opposite major surface as proximal electrode 816A and/or integrated antenna 830A.
• header assembly 828 includes suture hole 834, which provides another means of securing IMD 10A to the patient to prevent movement following insertion.
• header assembly 828 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10A.
• FIG. 16B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIGS. 1 and 2 as an ICM.
• IMD 10B of FIG. 16B may be configured substantially similarly to IMD 10A of FIG. 16A, with differences between them discussed herein.
• insulative cover 842 may be positioned over an open base 840 such that base 840 and cover 842 enclose the circuitries and other components and protect them from fluids such as body fluids.
• the housing including base 840 and insulative cover 842 may be hermetically sealed and configured for subcutaneous implantation.
• Circuitries and components may be formed on the inner side of insulative cover 842, such as by using flip-chip technology.
• Insulative cover 842 may be flipped onto a base 840. When flipped and placed onto base 840, the components of IMD 10B formed on the inner side of insulative cover 842 may be positioned in a gap 844 defined by base 840.
• Electrodes 816C and 816D and antenna 830B may be electrically connected to circuitry formed on the inner side of insulative cover 842 through one or more vias (not shown) formed through insulative cover 842.
• Insulative cover 842 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material.
• Base 840 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 816C and 846D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 846C and 846D may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.
• a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.
• the housing of IMD 10B defines a length L, a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 16A.
• the spacing between proximal electrode 816C and distal electrode 816D may range from 5 mm to 50 mm, from 30 mm to 50 mm, from 35 mm to 45 mm, and may be any single spacing or range of spacings from 5 mm to 50 mm, such as approximately 40 mm.
• IMD 10B may have a length L that ranges from 5 mm to about 70 mm.
• the length L may range from 30 mm to 70 mm, 40 mm to 60 mm, 45 mm to 55 mm, and may be any single length or range of lengths from 5 mm to 50 mm, such as approximately 45 mm.
• the width W may range from 3 mm to 15 mm, 5 mm to 15 mm, 5 mm to 10 mm, and may be any single width or range of widths from 3 mm to 15 mm, such as approximately 8 mm.
• the thickness or depth D of IMD 10B may range from 2 mm to 15 mm, from 5 mm to 15 mm, or from 3 mm to 5 mm, and may be any single depth or range of depths between 2 mm and 15 mm, such as approximately 4 mm.
• IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic cm.
• outer surface of cover 842 faces outward, toward the skin of the patient.
• proximal end 846 and distal end 848 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
• edges of IMD 10B may be rounded.
• the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware -based processing unit.
• Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).
• Example 1 A computing device comprising: communication circuitry configured to wirelessly communicate with a sensor device on a patient or implanted within the patient; one or more output devices; and processing circuitry configured to: receive episode data for an acute health event detected by the sensor device via the communication circuitry, the episode data transmitted by the sensor device in response to detecting the acute health event; determine an alarm context in response to receiving the episode data; configure an alarm for the acute health event based on the alarm context; and control the one or more output devices to output the alarm that is configured based on the alarm context.
• Example 2 The computing device of example 1, wherein, to configure the alarm, the processing circuitry is configured to select a duration of the alarm based on the alarm context.
• Example 3 The computing device of example 1 or 2, wherein, to configure the alarm, the processing circuitry is configured to select an intensity of the alarm based on the alarm context.
• Example 4 The computing device of example 3, wherein the intensity comprises a volume of an audible alarm.

Landscapes

• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Medical Informatics (AREA)
• Molecular Biology (AREA)
• Pathology (AREA)
• Engineering & Computer Science (AREA)
• Biomedical Technology (AREA)
• Heart & Thoracic Surgery (AREA)
• Physics & Mathematics (AREA)
• Biophysics (AREA)
• Surgery (AREA)
• Animal Behavior & Ethology (AREA)
• General Health & Medical Sciences (AREA)
• Public Health (AREA)
• Veterinary Medicine (AREA)
• Cardiology (AREA)
• Physiology (AREA)
• Measuring And Recording Apparatus For Diagnosis (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=88373823

Family Applications (1)

Country Status (3)

Family Cites Families (5)

• 2023
  • 2023-09-13 EP EP23789399.5A patent/EP4586912A1/de active Pending
  • 2023-09-13 CN CN202380065686.XA patent/CN119894447A/zh active Pending
  • 2023-09-13 WO PCT/US2023/032589 patent/WO2024059101A1/en not_active Ceased

Also Published As

Similar Documents

Legal Events