WO2024178251A2 - Système et procédé de modulation de la survenue ou de réduction de la gravité du délire - Google Patents

Système et procédé de modulation de la survenue ou de réduction de la gravité du délire Download PDF

Info

Publication number
WO2024178251A2
WO2024178251A2 PCT/US2024/016946 US2024016946W WO2024178251A2 WO 2024178251 A2 WO2024178251 A2 WO 2024178251A2 US 2024016946 W US2024016946 W US 2024016946W WO 2024178251 A2 WO2024178251 A2 WO 2024178251A2
Authority
WO
WIPO (PCT)
Prior art keywords
eeg
stimulation
controller
signal
subject
Prior art date
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.)
Ceased
Application number
PCT/US2024/016946
Other languages
English (en)
Other versions
WO2024178251A3 (fr
Inventor
Patrick PURDON
Vitaly Napadow
Ronald Garcia
Alessandra ANZOLIN
Proloy DAS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
General Hospital Corp
Original Assignee
General Hospital Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by General Hospital Corp filed Critical General Hospital Corp
Publication of WO2024178251A2 publication Critical patent/WO2024178251A2/fr
Publication of WO2024178251A3 publication Critical patent/WO2024178251A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/0404—Electrodes for external use
    • A61N1/0408—Use-related aspects
    • A61N1/0456—Specially adapted for transcutaneous electrical nerve stimulation [TENS]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
    • A61N1/0526—Head electrodes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/36025—External stimulators, e.g. with patch electrodes for treating a mental or cerebral condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/3603—Control systems
    • A61N1/36031—Control systems using physiological parameters for adjustment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/3603—Control systems
    • A61N1/36034—Control systems specified by the stimulation parameters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36053—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for vagal stimulation

Definitions

  • Delirium is a serious form of brain dysfunction characterized by acute deficits in awareness, atention, and cognition. Delirium arises from an altered state of consciousness and is the most common neuropsychiatric disorder observed in hospitalized patients, especially the elderly. Delirium has been linked to specific disturbances in electroencephalography (EEG) rhythms. Delirium is marked by the presence of delta waves on EEG, a typical feature of non-rapid eye movement (NREM) sleep, that normally do not occur during the wakeful state. The presence of these delta waves reflects a periodic alternation between Up states, where underlying neural circuits are more active, and sustained Down states, where neuronal activity is suppressed.
  • EEG electroencephalography
  • EAVANS EEG-gated auricular vagal afferent nerve stimulation
  • the stimulation may be coordinated by a controller designed to modulate arousal as well as markers of impaired arousal such as delta power, by providing auricular vagal afferent nerve stimulation in a manner phase-locked to the delta or slow wave. Since cognitive impairment and neurocognitive disorders such as delirium can occur in part due to an impairment of arousal, this modulation of arousal can serve to prevent or ameliorate or counteract delirium.
  • a neuromodulation system for modulating delta power.
  • the system comprises an electrode configured to be electrically coupled to an auricular branch of a vagus nerve of a subject, a stimulation circuit connected to the electrode to deliver a stimulation signal to the electrode to stimulate the auricular branch of the vagus nerve, an EEG sensor configured to acquire an EEG signal of the subject.
  • the system further comprise a controller configured to receive the EEG signal from the EEG sensor, wherein the controller extracts one or more EEG characteristics of interest and sends control signals to the stimulation circuit to deliver the stimulation signal based on the one or more EEG characteristics to modulate arousal or reduce delirium severity.
  • a method for modulating delta power comprises positioning on a subject an electrode configured to be electrically coupled to an auricular branch of a vagus nerve of the subject, delivering, by a stimulation circuit connected to the electrode, a stimulation signal to the electrode, thereby stimulating the auricular branch of the vagus nerve, acquiring, via an electroencephalography (EEG) sensor, an EEG signal of the subject, and receiving, via a controller, the EEG signal from the EEG sensor.
  • EEG electroencephalography
  • the method further comprises extracting, via the controller, one or more EEG characteristics of interest, sending, via the controller, control signals to the stimulation circuit, and delivering, via the stimulation circuit, a stimulation signal based on the one or more EEG characteristics, wherein the stimulation signal is delivered to modulate arousal or reduce delirium sensitivity.
  • FIG. 1 is a schematic of the neuromodulation system, according to aspects of the present disclosure.
  • FIG. 2 is a schematic of an example neuromodulation system, according to aspects of the present disclosure.
  • FIG. 3 is a schematic of example components that can implement the neuromodulation system of FIG. 2.
  • FIG. 4 is a flow chart of the method for modulation of delta power, according to aspects of the present disclosure.
  • FIG. 5 is an illustration of the neuromodulation system, according to another aspect of the disclosure.
  • FIG. 7A shows scalp maps reporting the names and locations of the recording EEG electrodes, according to aspects of the present disclosure.
  • FIG. 7B shows a box plot and violin plot reporting delta power distribution (averaged across electrodes) in experimental conditions No-Stim, GANctrl and EAVANS.
  • FIG. 7C is a plot of the delta power distribution during EAVANS and GANctrl while stimulating during either Up or Down delta phases states.
  • FIG. 8 is a plot of 3-minute Diagnostic Interview for Confusion Assessment Method (3D-CAM) total score reported for three subjects for three time points.
  • FIG. 9 is a plot of the percentage of correct answers (CA%) reported for three subject for three time points.
  • VNS EEG-gated vagus nerve stimulation
  • taVNS transcutaneous auricular vagus nerve stimulation
  • taVNS transcutaneous auricular vagus nerve stimulation
  • Promising clinical outcomes have been shown for a range of disorders including depression, epilepsy and migraine.
  • a combination of la VNS with EEG is presented to modulate arousal as well as markers of impaired arousal such as delta power.
  • EAVANS EEG-gated auricular vagal afferent nerve stimulation
  • taVNS EEG-gated auricular vagal afferent nerve stimulation
  • taVNS could be imparted during a specific phase of the delta wave, thereby increasing arousal and activating ascending noradrenergic pathways via the nucleus tractus solitarius and potentially disrupting the cycle of Up/Down states that characterize periods of reduced or impaired arousal.
  • periodically gated stimulation paradigms circumvent the habituation that is commonly observed with more sustained, continuous peripheral nerve stimulation.
  • taVNS has been proposed to also modulate neuroinflammation, another key contributing factor to delirium and other neurocognitive disorders, by the cholinergic anti-inflammatoiy pathway and efferent feedback connections to the vagal innervation of the spleen.
  • an EAVANS neuromodulation system 100 is shown in FIG. 1 .
  • the system may include an electrode 102 configured to be electrically coupled to an auricular branch of the vagus nerve of a subject 104.
  • the electrode 102 is configured to engage within the cymba conchae of the ear 106.
  • the electrode 102 may be electrically coupled to an afferent nerve fiber of a vagus nerve of a subject 104.
  • the system 100 further includes a stimulation circuit 108 operably connected to the electrode 102 to deliver a stimulation signal to the auricular branch of the vagus nerve.
  • the stimulation signal may be provided by a current-constant stimulator (e.g., Urostim, schwa-medico GmbH, Ehringshausen, Germany).
  • the stimulation signal comprises, but is not limited to, a monophasic rectangular pulse burst train. Further, the stimulation signal may have a frequency of 100 Hz and a pulse width of 300 us.
  • the stimulation signal consists of biphasic rectangular pulse trains 1 to 1 .5 seconds in duration with 100 to 600 ps pulse width, delivered at a frequency 1 to 200 Hz, initiated 0.1 to 1 seconds following peak inspiration by the subject 104.
  • the stimulation signal causes a reduction of neuroinflammation coordinated via the activation of cholinergic anti-inflammatory pathways and efferent feedback connecti ons to the vagal innervation of the spleen .
  • the stimulation signal can modulate delta oscillations (0.5 to 4 Hz) in order to increase arousal or reverse impaired arousal.
  • the stimulation signal can modulate delta oscillations (0.5 to 4 Hz) in order to increase arousal or reverse impaired arousal and in turn improve cognitive function.
  • the stimulation signal delivered by the stimulation circuit 108 can be based on an EEG signal obtained from the subject 104 using an EEG system 110.
  • the EEG system 110 includes an EEG sensor 112 configured to acquire EEIG signal from the subject 104 to determine and extract one or more EEG characteristics.
  • an EEG characteristic may be related to the delta wave (0.5 to 4 Hz) phase.
  • the one or more EEG characteristic includes a phased of a delta wave embedded in the EEG signal.
  • the EEG system 110 may be wireless and receive EEG signal from the EEG sensor 1 12.
  • the EEG sensor 112 may be electrically connected to the EEG system 110 by wires.
  • the EEG system 110 may further include an amplifier 116 configured to amplify the EEG signal.
  • the EEG sensor 112 includes a plurality of sensor electrodes 114.
  • the plurality of sensor electrodes 114 may be configured to be placed on the subject’s scalp for detecting the EEG characteristic.
  • the EEG sensor 112 may include one to 64 channels.
  • the EEG sensor 112 includes at least eight channels.
  • the EEG sensor 112 may by arranged in a net or on a cap configured to be placed on the subject’s scalp.
  • the EEG sensor 112 may include two to 128 sensor electrodes 1 14.
  • the EEG sensor 112 include eight sensor electrodes 114.
  • the eight sensor electrodes 114 may be frontal electrodes
  • the system 100 further comprises a controller 118 for receiving the EEG signal from the EEG sensor 1 12 and sends control signals to the stimulation circuit 108 to the electrode 102 based on tire one or more EEG characteristics.
  • the controller 118 is configured to estimate instantaneous phase or power of the delta wave as EEG characteristics and determine control signals based on that EEG characteristics.
  • the stimulation signal is delivered during a selected phase of a delta wave to modulate arousal or reduce neuroinflammation associated with delirium.
  • the controller 1 18 is configured to cause the stimulation circuit 10 ⁇ to deliver the stimulation signal during a rising phase of the delta wave.
  • the controller 118 is configured to cause the stimulation circuit 108 to deliver the stimulation signal in the range of -a/2, 0 radians.
  • the controller 11 ⁇ is configured to control the stimulation circuit 108 to reduce delta power in the subject experiencing or at risk of delirium.
  • the controller 118 is configured to control the stimulation circuit to reduce delta power during non-rapid eye movement (NREM) sleep in the subject.
  • NREM non-rapid eye movement
  • the controller 118 is configured to control delirium severity in the subject during at least one of pre-operative and post-operative conditions.
  • the system 100 further includes a display 120.
  • the controller 118 includes integrated algorithm configured to provide real- time display of the acquired EEG signal and its spectral properties.
  • EEG-based features are estimated such as Power Spectral Density' (PSD) and phase synchronization-based Phase Locking Value (PLV) in 8, 6, and a frequency bands.
  • the integrated algorithm may detect in real-time Up and Down states, based on an adaptive threshold detection algorithm.
  • FIG. 2 an example of a system 200 for neuromodulation of delta power in accordance with some embodiments of the system s and methods described in the present disclosure is shown.
  • a computing device 250 can receive EEG data from data source 202.
  • computing device 250 can execute at least a portion of the EEG system 204 to extract one or more EEG characteristics from data received from the data source 202.
  • the computing device 250 can communicate information about data received from the data source 202 to a server 252 over a communication network 254, which can execute at least a portion of the EEG system 204.
  • the server 252 can return information to the computing device 250 (and/or any other suitable computing device) indicative of an output of tire EEG system 204.
  • computing device 250 and/or server 252 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on.
  • the computing device 250 and/or server 252 can also construct scalp maps from the data.
  • data source 202 can be any suitable EEG data source such as EEG sensor 112, another computing device (e.g., a server storing previously acquired EEG data), and so on.
  • the data source 202 may additionally include an electrocardiogram (ECG) data source, a galvanic skin response (GSR) data source, and/or a respiration data source.
  • ECG electrocardiogram
  • GSR galvanic skin response
  • respiration data source can be an electrocardiogram
  • data source 202 can be local to computing device 250.
  • data source 202 can be incorporated with computing device 250 (e.g., computing device 250 can be configured as part of a device for measuring, recording, estimating, acquiring, or otherwise collecting or storing data).
  • data source 202 can be connected to computing device 250 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, data source 202 can be placed locally and/or remotely from computing device 250 and can communicate data to computing device 250 (and/or server 252) via a communication network (e.g., communication network 254).
  • a communication network e.g., communication network 254
  • communication network 254 can be any suitable communication network or combination of communication networks.
  • communication network 254 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), other types of wireless network, a wired network, and so on.
  • Wi-Fi network which can include one or more wireless routers, one or more switches, etc.
  • peer-to-peer network e.g., a Bluetooth network
  • a cellular network e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.
  • communication network 254 can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks.
  • Communications links shown in FIG. 2 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on.
  • FIG. 3 an example of hardware 300 that can be used to implement data source 202, computing device 250, and server 252 in accordance with some embodiments of die systems and methods described in the present disclosure is shown.
  • computing device 250 can include a controller 302, a display 304, one or more inputs 306, one or more communication systems 308, and/or memory’ 310.
  • controller 302 can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), and so on.
  • display 304 can include any suitable display devices, such as a liquid cry stal display (“LCD”) screen, a light-emiting diode (“LED”) display, an organic LED (“OLED”) display, an electrophoretic display (e.g., an “e- ink” display), a computer monitor, a touchscreen, a television, and so on.
  • inputs 306 can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.
  • communications systems 308 can include any suitable hardware, firmware, and/or software for communicating information over communication network 254 and/or any other suitable communication networks.
  • communications systems 308 can include one or more transceivers, one or more communication chips and/or chip sets, and so on.
  • communications systems 308 can include hardware, firmware, and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on,
  • memory 310 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 302 to present content using display 304, to communicate with server 252 via communications system(s) 308, and so on.
  • Memory 310 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof.
  • memory 310 can include random-access memory (“RAM”), read-only memory (“ROM”), electrically programmable ROM (“EPROM”), electrically erasable ROM (“EEPROM”), other forms of volatile memory, other forms of non-volatile memory, one or more forms of semi- volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on.
  • memory 310 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device 250.
  • processor 602 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 252, transmit information to server 252, and so on.
  • the controller 302 and the memory 310 can be configured to perform the methods described herein
  • server 252 can include a processor 312, a display 314, one or more inputs 316, one or more communications systems 318, and/or memory 320.
  • processor 312 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on.
  • display 314 can include any suitable display devices, such as an LCD screen, LED display, OLED display, electrophoretic display, a computer monitor, a touchscreen, a television, and so on.
  • input 316 can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.
  • communications systems 318 can include any suitable hardware, firmware, and/or software for communicating information over communication network 254 and/or any other suitable communication networks.
  • communications systems 318 can include one or more transceivers, one or more communication chips and/or chip sets, and so on.
  • communications systems 318 can include hardware, firmware, and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.
  • memory 320 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 312 to present content using display 314, to communicate with one or more computing devices 250, and so on.
  • Memory 320 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof.
  • memory 320 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on.
  • memory 320 can have encoded thereon a server program for controlling operation of server 252.
  • processor 312 can execute at least a portion of the server program to transmit information and/or content (e.g., data, a user interface) to one or more computing devices 250, receive information and/or content from one or more computing devices 250, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.
  • the server 252 is configured to perform the methods described in the present disclosure.
  • processor 312 and memory 320 can be configured to perform the methods described herein (e.g., the method of FIG. 4).
  • data source 202 can include a controller 322, one or more data acquisition systems 324, one or more communications systems 326, and/or memory 328.
  • controller 322 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on.
  • the one or more data acquisition systems 324 are generally configured to acquire data and can include EEG data. Additionally or alternatively, in some embodiments, the one or more data acquisition systems 324 can include any suitable hardware, firmware, and/or software for coupling to and/or controlling operations of EEG sensor 1 12. In some embodiments, one or more portions of the data acquisition system(s) 324 can be removable and/or replaceable.
  • data source 202 can include any suitable inputs and/or outputs.
  • data source 202 can include input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on.
  • data source 202 can include any suitable display devices, such as an LCD screen, an LED display, an OLED display, an electrophoretic display, a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on.
  • communications systems 326 can include any suitable hardware, firmware, and/or software for communicating information to computing device 250 (and, in some embodiments, over communication network 254 and/or any other suitable communication networks).
  • communications systems 326 can include one or more transceivers, one or more communication chips and/or chip sets, and so on.
  • communications systems 326 can include hardware, firmware, and/or software that can be used to establish a wired connection using any suitable port and/or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.
  • memory 328 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by controller 322 to control the one or more data acquisition systems 324, and/or receive data from the one or more data acquisition systems 324; to present content (e.g., data, images, a user interface) using a display; communicate with one or more computing devices 250; and so on.
  • Memory 328 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof.
  • memory 328 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on.
  • memory 328 can have encoded thereon, or otherwise stored therein, a program for controlling operation of data source 202.
  • non-transitory computer-readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs, Blu-ray discs), semiconductor media (e.g., RAM, flash memory, EPROM, EEPROM), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and/or any suitable tangible media.
  • transitory computer-readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and/or any suitable intangible media.
  • devices or systems disclosed herein can be utilized or installed using methods embodying aspects of the disclosure.
  • description herein of particular features, capabilities, or intended purposes of a device or system is generally intended to inherently include disclosure of a method of using such features for the intended purposes, a method of implementing such capabilities, and a method of installing disclosed (or otherwise known) components to support these purposes or capabilities.
  • discussion herein of any method of manufacturing or using a particular device or system, including installing the device or system is intended to inherently include disclosure, as embodiments of the disclosure, of the utilized features and implemented capabilities of such device or system.
  • the controller sends a control signal to the stimulation circuit based on the extracted characteristic(s).
  • the method 400 operates as a feedback loop and returns to step 404 to control the stimulation circuit to deliver the stimulation signal based on the EEG characteristic at step 408.
  • the method 400 may be initiated at any of the steps 402-410 and need not be in the exact order depicted in FIG. 4.
  • the first step may be acquiring the EEG signal via the EEG sensor at step 406.
  • EAVANS is a closed-loop neuromodulatory device-based approach (FIG. 1 ) which targets a particular phase of the EEG-acquired delta wave (0.5 to 4 Hz).
  • the EEG gating was implemented by measuring electrical signals from subjects’ scalp using a wireless EEG system (Neuroelectrics Instrument Controller, NIC, Enobio 8).
  • An 8-channel montage was selected containing 7 frontal electrodes (Fpz, Fpl, Fp2, AF3, AF4, F7, F8) and Cz to facilitate Laplacian referencing of the signal recorded by the Fpz electrode.
  • An integrated software displayed in real-time the acquired signals and their main spectral properties enabling monitoring and online quality assessment.
  • Raw EEG signals were received from LabStreamingLayer (LSL).
  • LSL LabStreamingLayer
  • An in-house developed MATLAB code was used for online preprocessing (band-pass filtering between 0.5 and 40 Hz, and down-sampling from 500 to 250 Hz) as well as to compute instantaneous phase and power.
  • state space models were used to estimate the instantaneous phase. Given a single-channel neural signal recording y, represented as a univariate time-series, it may be determined which neural oscillations are present in the data and characterize the phases and amplitudes of these oscillations. For this purpose, it would be achieved in real-time, i.e., causally, as opposed to the existing data analysis technique that applies non-causal band-pass filtering and Hilbert transform.
  • x t can be seen as a generalization of phasor (with x t>1 and x t 2 are the real and imaginary components respectively) rotating in time, while a noisy version of its projection on the real line is observed (FIG. 6).
  • this a Gaussian linear state space representation of stochastic oscillators, developed based on Wiener’s random frequency modulation.
  • the instantaneous phase of the oscillator is defined as:
  • the model can be easily generalized for multiple oscillation components by treating oscillation components independently and the observation time series as a noisy superposition of the first coordinates of all the oscillators.
  • the oscillator parameters (f , a, cr 2 ) and the observation noise variance T 2 are learned using an instance of Expectation Maximization (EM) algorithm.
  • EM alternates between optimizing the distribution over the hidden oscillator states given the current parameters (the E-step) and updating the parameters given the distribution of hidden states (the M-step).
  • the E-step can be implemented efficiently using an instance of Kalman filter (KF) and fixed- interval smoothing (FIS).
  • KF provides the one-step-ahead prediction for the oscillation states, and time-series, thus is causal.
  • FIS is non-causal since it estimates the oscillation state, given the above mentioned one-step-ahead state prediction, and future observations within the given interval.
  • the M-step utilizes the results of FIS, to update the oscillator state-space model parameters by maximizing the Q-function, defined as the expectation of complete log-likelihood with respect to the FIS output.
  • the updates are stopped when the value of the likelihood of the observation stabilize.
  • the iterative algorithm identifies a set of oscillations that explicitly characterizes the underlying neural oscillations with stationary parameters that are present in EEG data, in an unsupervised manner. These parameters could be learned from scout or baseline data from a patient prior to stimulation, or could be learned from a representative data set and applied to patients in general.
  • the next step would be to decompose the EEG data into the constituent oscillation time-series. It turns out that can be accomplished by the abovementioned E-step.
  • the result of the FIS run with the identified model parameters is the best linear unbiased estimate of the oscillation states, and the real indices of the oscillation states are the constituent oscillation time series that are present in the EEG data.
  • the instantaneous phase estimate can then be defined for the oscillations as Eq. 2, with the quantities replaced by the FIS estimates.
  • running FIS requires time series recorded within an interval (i.e., in batches), so it is not useful in real-time applications.
  • a fixed lag smoother with lag 1 is used to identify the phase of the slow oscillation in real-time. This involves first running the Kalman filter update for the oscillation state, to obtain the best linear unbiased estimate of the oscillation states at present, given the past observations. Then the present observation is utilized to compute the one step ahead prediction error and update the estimate of the oscillation states to incorporate the information from the most recent time-point observed. It is noted that this smoothing horizon can be extended beyond lag 1 to any arbitrary horizon. Then these FLS output in Eq. 2 are used to estimate phase in real time.
  • the Kalman filter outputs could also be used, i.e., one step ahead forecast as the oscillation state estimate to estimate phase in a causal manner. Again, this can be generalized to multiple-step-ahead state forecast. However, this should be used with caution since the estimation performance degrades exponentially with the forecasting lead.
  • (1) the EEG recording is acquired in batches and sent to the computing node in packets of several milliseconds of recording.
  • the computing node then runs the Kalman filtering, followed by either, FIS within the entire batch or, FLS with the lag I.
  • FIS is the most computationally demanding, i.e., requires more clock cycles
  • the controller then can decide the status of the relay, i.e., if the relay will be ON or OFF, and sends that signal via COM port.
  • This estimated phase is then extrapolated by the anticipated processing delay, i.e., by to make the decision-making close to real-time. For the above example, the phase will be extrapolated by 19 ms, considering typical sampling rate of 500 samples/s.
  • a MATLAB-controlled device (National Instruments USB DAQCard 6009, 14-bit i/o) was used to generate a 5 Volt analog output, to be sent to a miniature high-frequency relay (G6Z-1 P-DC5, Omron Electronics Components, Schaumburg, IL, USA), thereby controlling the onset and offset of stimulation.
  • EAVANS taVNS stimuli consisted of monophasic rectangular pulse burst trains at 100Hz and with 300ps pulse width, provided by a constant-current electrostimulation unit (UROstim, schwa-medico GmbH, Ehringshausen, Germany).
  • Stimuli to the auricle were delivered using custom built, ergonomic electrodes (Bionik Medical Devices, Bucaramanga, Colombia) placed within the cymba conchae of the left ear.
  • the stimulus frequency of 100 Hz was chosen based on previous work.
  • each subject underwent a single sleep session.
  • the sleep duration varied from 70 to 180 minutes across subjects. Participant were instructed to press/ move a button at peak inhalation of every breath in order to track the onset of sleep and awakening.
  • the sleep session consisted of a passive control run (i.e., electrode placed within cymba conchae, but no electrical current passed, No-Stim) and four 5- minute EEG-gated stimulation runs including two active stimulation runs, and two active control runs.
  • taVNS stimulation was delivered to the auricular territory innervated by the auricular branch of the vagus nerve on the left ear during either the rising (Up) or the descending (Down) phase of the delta wave.
  • active control stimulation runs GANctrl
  • GAN greater auricular nerve
  • EEG scalp data were band-pass filtered between 0.5 and 45 Hz and re- referenced (average).
  • Independent Component Analysis (ICA) was employed to remove artifacts associated with the presence of the stimulation.
  • time series were segmented in 2-second epochs (number of trials per condition: 61.0 ⁇ 14, mean ⁇ SD). Residual artifacts were removed using a semi-automatic procedure based on a threshold criterion ( ⁇ 80 pV).
  • delta power estimate Power spectra were extracted from each 2-second epoch and averaged together using the multi-taper method with 3 Slepian tapers. For each run, spectral power was averaged across frequencies in the delta band (0.5 - 4 Hz) for comparison across stimulation conditions and subjects.
  • delirium is the most common neuropsychiatric disorder observed in hospitalized patients, with an incidence of approximately 25% in older surgical patients, there is a lack of effective treatment approaches that target the underlying neurophysiological mechanisms of this condition. Moreover, detection of delirium is typically based on measurements of patient arousal and cognitive state rather than objective metrics reflecting the underlying neurophysiology that, potentially, better predict future clinical phenotype.
  • EAVANS taVNS neuromodulatory approach
  • delirium i.e., the EEG delta wave (0.5 to 4 Hz).
  • EEG delta wave 0.5 to 4 Hz
  • NTS nucleus of the solitary tract
  • NTS projections are known to regulate the noradrenergic arousal systems [14].
  • closed-loop modulation of delta waves, and the known upstream NTS connections indicate that EAVANS stimulation is a promising for disrupting the neural alterations characterizing delirium.
  • EAVANS has the additional advantage of being relatively low cost, non-invasive, and highly portable, as feasibility was demonstrated with a low density (8 channel) EEG array that can be safely administered in a variety of clinical settings.
  • this technology has the potential to become widely adopted in both pre- and post-operative settings to address the current dearth of effective delirium treatments.
  • EEG-gated auricular vagal afferent nerve stimulation is a closed-loop device able to target the neural mechanisms underlying the presence of increased delta waves during sleep, which extend to pathological disorders such as delirium.
  • the present study demonstrated the ability of EAVANS to reduce delta power in healthy controls during NREM sleep, recognizing that reduced arousal marked by delta (0.5 to 4 Hz) waves in the EEG are a shared feature of sleep as well as impaired neurocognitive states such as delirium. The results indicate recruitment of neurophysiological pathways clinically relevant for altered states of consciousness such as delirium.
  • EEG-gated auricular vagal afferent nerve stimulation EAVANS.
  • PACU Post Anesthesia Care Unit
  • EEG data were recorded during three consecutive runs: resting state, aPVT without EAVANS stimulation, and aPVT with EAVANS stimulation.
  • the level of delirium was assessed before and after anesthesia as well as pre- and post-EAVANS stimulation. All study procedures were approved by the local Institutional Review Board, and written informed consent was provided and signed by all subjects.
  • EAVANS is a closed-loop neuromodulatory device-based approach that targets a particular phase of the EEG-acquired Delta wave.
  • the EEG gating was implemented by measuring electrical signals from the subjects’ scalp using a wireless EEG system (Neuroelectrics Instrument Controller, NIC, Enobio 8).
  • An 8-channel montage was selected containing 7 frontal electrodes (Fpz, Fpl, Fp2, AF3, AF4, F7, F8) and FCz to facilitate Laplacian referencing of the signal recorded by the Fpz electrode.
  • An integrated software displayed in real-time the acquired signals and their main spectral properties enabling monitoring and online quality assessment.
  • Raw EEG signals were received from LabStreamingLayer (LSL) to enable real-time or online EEG processing.
  • An in-house developed MATLAB code was used for online preprocessing (band-pass filtering between 0.5 and 40 Hz, and down-sampling from 500 to 250 Hz) as well as to compute instantaneous phase and power.
  • state space models were used to estimate the instantaneous delta phase for each streamed segment of data (Is) and delivered the stimulation when the phase was rising in the interval between -TC/2 and 0 radians, corresponding to an “Up” or “On” state in the brain.
  • a MATLAB-controlled device (National Instruments USB DAQCard 6009, 14-bit i/o) was used to generate a 5 Volt analog output, to be sent to a miniature high- frequency relay (G6Z-1P-DC5, Omron Electronics Components, Schaumburg, IL, USA), thereby controlling the onset and offset of stimulation.
  • EAVANS stimuli consisted of monophasic rectangular pulse burst trains at 100Hz and with 300ps pulse width, provided by a constant-current electrostimulation unit (UROstim, schwa-medico GmbH, Ehringshausen, Germany).
  • Stimuli to the auricle were delivered for 5 minutes using custom-built, ergonomic electrodes (Bionik Medical Devices, Bucaramanga, Colombia) placed within the cymba conchae of the left ear.
  • a 100Hz stimulus frequency was chosen based on prior research suggesting optimal brainstem targeting.
  • the 3D-CAM (for the Intensive Care Unit) is a validated scale to evaluate the presence and severity of delirium, characterizing patients' cognitive states following anesthesia and surgery.
  • the 3D-CAM is a brief verbal assessment tool that can be completed in an average of 3 minutes.
  • the scoring system for delirium severity based on the CAM (the CAM-S) has been proven to have predictive validity for important clinical outcomes.
  • delirium is scored as present/absent based on the presence of 1) acute or fluctuating course, 2) inattention, and either 3) disorganized thinking or 4) altered level of consciousness.
  • the CAM-S was created based on the severity rating of these 4 CAM diagnostic features. Acute change or fluctuating course was scored 0 points if absent or 1 if present.
  • the remaining three CAM diagnostic features were scored as 0 points for no symptoms, 1 point for mild symptoms, and 2 points for severe symptoms.
  • the CAM-S score represents a sum of points assigned for the four features and ranges between 0 and 7 (most severe). 3D-CAM assessment was performed before and after surgery, pre- and post- stimulation.
  • aPVT auditory Psychomotor Vigilance Task
  • the auditory Psychomotor Vigilance Task was employed to evaluate patients' vigilance, sustained attention, and reaction times.
  • the aPVT required patients to respond by clicking a mouse to a sound played over a set of headphones at random intervals.
  • This task was a complementary tool to the 3D-CAM scale, allowing for a comprehensive evaluation of the patient's level of arousal and cognitive and behavioral responses during the post- anesthesia awakening phase.
  • the patient’s performance was assessed in terms of correct answer rate (%) and reaction time, pre- and post-anesthesia, with and without EAVANS stimulation.
  • Visit description Patient MGH-1 , a 60-year-old male undergoing laparoscopic low anterior colon resection and splenic flexure takedown, demonstrated improvement in cognitive function following the application of EAVANS during the post-anesthesia awakening phase.
  • the patient spontaneously expressed a history of multiple surgeries and having “a suspected time waking up from anesthesia”. Consequently, the patient stated to be “open to anything that could help recover from anesthesia faster ” and was excited to try EAVANS.
  • the patient arrived at the post-operative area in a confused state. The patient was more responsive after EAVANS stimulation started.
  • the use of EAVANS did not impact the work of the clinical team, which was open to its use given the small size of the stimulator which did not get in the way of clinical care. No adverse events or discomfort were reported by the patient.
  • Clinical scale Prior to EAVANS stimulation, the patient exhibited signs of disorientation and confusion, as evidenced by a score of 4 on the 3D-CAM scale, indicative of delirium. Following a 5-minute stimulation there was a reduction in the level of distraction, with a total score equal to 3 (see Table 2 and FIG. 8).
  • Behavioral outcome (aPVT): Patient MGH-1 performance showed an increased percentage of correct answers and no significant difference in reaction times during aPVT with EAVANS stimulation compared to aPVT without EAVANS intervention (see Table 3 and FIG. 9).
  • Clinical scale Prior to EAVANS stimulation, the patient exhibited signs of disorientation and confusion, as well as a mild level of inattention with a total score of 2 on the 3D-CAM scale (see Table 2). Following a 5-minute stimulation there was a reduction in the level of distraction, with a total score equal to 1 (see FIG. 8).
  • Behavioral outcome (aPVT): Patient SHC-1 performance showed an increased percentage of correct answers and no significant difference in reaction times during aPVT with EAVANS stimulation compared to aPVT without EAVANS intervention (see Table 3 and FIG. 9).
  • Visit description Patient SHC-2, a 69-year-old male with clinically localized prostate cancer who underwent a robotic-assisted laparoscopic radical prostatectomy with bilateral pelvic lymph node dissection, arrived in the post-operative area after a minor medical complication delayed his exit from the operating room. Upon arrival to the post-operative area, he repeatedly stated that he wished "to go back to sleep.” He tolerated the study procedures without issue. By the end of the testing period, he remained sleepy but increasingly oriented in response to questions. He reported no adverse events. [0108] Clinical scale: Prior to EAVANS stimulation, the patient exhibited signs of disorientation and confusion, as well as a mild level of inattention and a severe level of disorganized thinking (see Table 2). The 3D-CAM total score for Patient SHC-2 went from 4 (presence of delirium) before stimulation to a score of 1 (no delirium) after a 5-minute EAVANS session (see FIG. 8).
  • Behavioral outcome (aPVT): Patient SHC-1 performance did not show differences in the percentage of correct answers and reaction times during aPVT with EAVANS stimulation compared to aPVT without EAVANS intervention (see Table 3 and FIG. 9).
  • the terms “include” and “including” have the same meaning as the terms “comprise” and “comprising.”
  • the terms “comprise” and “comprising” should be interpreted as being “open” transitional terms that permit the inclusion of additional components further to those components recited in the claims.
  • the terms “consist” and “consisting of’ should be interpreted as being “closed” transitional terms that do not permit the inclusion of additional components other than the components recited in the claims.
  • the term “consisting essentially of’ should be interpreted to be partially closed and allowing the inclusion only of additional components that do not fundamentally alter the nature of the claimed subject matter.
  • the modal verb “may” refers to the preferred use or selection of one or more options or choices among the several described embodiments or features contained within the same. Where no options or choices are disclosed regarding a particular embodiment or feature contained in the same, the modal verb “may” refers to an affirmative act regarding how to make or use an aspect of a described embodiment or feature contained in the same, or a definitive decision to use a specific skill regarding a described embodiment or feature contained in the same. In this latter context, the modal verb “may” has the same meaning and connotation as the auxiliary verb “can.”

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Veterinary Medicine (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Biophysics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Neurology (AREA)
  • Physiology (AREA)
  • Neurosurgery (AREA)
  • Cardiology (AREA)
  • Child & Adolescent Psychology (AREA)
  • Developmental Disabilities (AREA)
  • Hospice & Palliative Care (AREA)
  • Psychiatry (AREA)
  • Psychology (AREA)
  • Social Psychology (AREA)
  • Electrotherapy Devices (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention concerne des systèmes et des procédés de neurostimulation pour fournir des résultats thérapeutiques, tels que le traitement du délire, de troubles de la conscience, de la narcolepsie et de troubles inflammatoires, avec des signaux qui sont ajustés sur la base de signaux électroencéphalographiques (EEG). La neurostimulation peut cibler le nerf vague ou ses branches. Les systèmes et les procédés utilisent un dispositif de détection pour détecter des caractéristiques d'EEG où des noyaux autonomes centraux peuvent être plus réceptifs à une entrée afférente du nerf vague. Un stimulateur est commandé pour fournir une neurostimulation à la branche auriculaire d'un nerf vague du sujet.
PCT/US2024/016946 2023-02-22 2024-02-22 Système et procédé de modulation de la survenue ou de réduction de la gravité du délire Ceased WO2024178251A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202363486259P 2023-02-22 2023-02-22
US63/486,259 2023-02-22

Publications (2)

Publication Number Publication Date
WO2024178251A2 true WO2024178251A2 (fr) 2024-08-29
WO2024178251A3 WO2024178251A3 (fr) 2024-10-31

Family

ID=92501805

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2024/016946 Ceased WO2024178251A2 (fr) 2023-02-22 2024-02-22 Système et procédé de modulation de la survenue ou de réduction de la gravité du délire

Country Status (1)

Country Link
WO (1) WO2024178251A2 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119034070A (zh) * 2024-11-04 2024-11-29 首都医科大学宣武医院 基于无监督学习模型的电刺激促觉醒治疗系统及设备

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2185237A1 (fr) * 2007-09-13 2010-05-19 Cardiac Pacemakers, Inc. Systèmes pour éviter une accoutumance à une stimulation neurale
US20190240486A1 (en) * 2009-03-20 2019-08-08 Electrocore, Inc. Non-invasive treatment of neurodegenerative diseases
US11051744B2 (en) * 2009-11-17 2021-07-06 Setpoint Medical Corporation Closed-loop vagus nerve stimulation
US20160279021A1 (en) * 2015-03-27 2016-09-29 Elwha Llc Vibratory ear stimulation system and method
US10688274B1 (en) * 2016-05-26 2020-06-23 Board Of Trustees Of The University Of Alabama, For And On Behalf Of The University Of Alabama In Huntsville Systems and methods for multi-modal and non-invasive stimulation of the nervous system
US10953192B2 (en) * 2017-05-18 2021-03-23 Advanced Brain Monitoring, Inc. Systems and methods for detecting and managing physiological patterns
JP7287941B2 (ja) * 2017-07-17 2023-06-06 エスアールアイ インターナショナル 支配末梢神経系振動に基づく徐波活動最適化
AU2021282379A1 (en) * 2020-05-27 2023-02-02 Attune Neurosciences, Inc. Ultrasound systems and associated devices and methods for modulating brain activity

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119034070A (zh) * 2024-11-04 2024-11-29 首都医科大学宣武医院 基于无监督学习模型的电刺激促觉醒治疗系统及设备

Also Published As

Publication number Publication date
WO2024178251A3 (fr) 2024-10-31

Similar Documents

Publication Publication Date Title
US11857794B2 (en) Pain management based on brain activity monitoring
US11517743B2 (en) Devices and methods for delivering mechanical stimulation to nerve, mechanoreceptor, and cell targets
US20220296155A1 (en) Responsiveness testing of a patient having brain state changes
US10537703B2 (en) Systems and methods for transdermal electrical stimulation to improve sleep
US8918176B2 (en) Assessing cognitive disorders based on non-motor epileptiform bioelectrical brain activity
US9669214B2 (en) Electrical stimulation of the claustrum/piriform cortex for treatment of epilepsy
D'Agostini et al. No evidence for a modulating effect of continuous transcutaneous auricular vagus nerve stimulation on markers of noradrenergic activity
US12023500B2 (en) Contingent cardio-protection for epilepsy patients
JP2023153785A (ja) 治療装置
WO2024178251A2 (fr) Système et procédé de modulation de la survenue ou de réduction de la gravité du délire
JP2025537466A (ja) 仮想現実を用いた健康状態治療システムおよび方法
US20250339689A1 (en) Contingent cardio-protection for epilepsy patients
Anzolin et al. Delta power during sleep is modulated by EEG-gated auricular vagal afferent nerve stimulation (EAVANS)
US20240399151A1 (en) Contingent Cardio-Protection For Epilepsy Patients
US12582820B2 (en) Neurostimulation therapy for treating uterine bleeding
WO2024195551A1 (fr) Procédé de contrôle de l'état de veille, système de commande de l'état de veille, dispositif de traitement d'informations et dispositif portable
Tahmasebi et al. Variability-dominated auditory cortical dysfunction and targeted VNS modulation in a Mecp2+/− model of Rett syndrome
Slade The Autonomic Correlates of Listening Effort

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 24761014

Country of ref document: EP

Kind code of ref document: A2