Systems and methods for analyzing electrophysiological data from patients undergoing medical treatments

• US 10,786,168 B2
• Filed: 11/29/2017
• Issued: 09/29/2020
• Est. Priority Date: 11/29/2016
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology, The General Hospital Corporation (Mass General Brigham, Inc.)
• Original Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Inventors: Brown, Emery N., Kim, Seong-Eun, Behr, Michael, Ba, Demba
• Primary Examiner(s): Bertram, Eric D.
• Application Number:
  US15/826,182
• Publication Number:
  US 20180146876A1
• Time in Patent Office: 1,035 Days
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/375 using biofeedback
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7257 using Fourier transforms

Abstract

Systems and methods for analyzing electrophysiological signals acquired from a subject are provided. In some aspects, a method includes receiving electrophysiological signals acquired from a subject using one or more sensors, and assembling a set of time-series data using the acquired electrophysiological signals. The method also includes analyzing the set of time-series data using a state-space multi-taper framework to generate spectral information describing the electrophysiological signals, and determining a brain state of the subject using the spectral information. The method further includes generating a report indicative of the determined brain state.

Claims

1. A system for analyzing electrophysiological signals acquired from a subject, the system comprising:

an input configured to receive electrophysiological signals acquired from a subject; and

at least one processor, in communication with the input, configured to;

assemble a set of time-series data using the electrophysiological signals received by the input;

analyze the set of time-series data across a series of intervals dividing the time-series of data using a state-space multi-taper framework to generate spectral information describing the electrophysiological signals using information from more than a single interval dividing the time-series of data;

generate a report including the spectral information extending in time with the time-series of data; and

an output configured to provide the report.

2. The system of claim 1, wherein the system further comprises a sensor assembly in communication with the input that is configured to acquire the electrophysiological signals.

3. The system of claim 2, wherein the sensor assembly is configured to acquire at least one of electroencephalogram (“

EEG”

) signals, electromyography (“

EMG”

) signals, electrocorticography (“

ECoG”

) signals, local field potentials (“

LFP”

) signals, electrocardiography (“

ECG”

) signals, electrooculography (“

EOG”

) signals, galvanic skin response (“

GSR”

) signals, oxygen saturation (“

SAO₂”

) signals, and ocular microtremor (“

OMT”

) signals.

4. The system of claim 1, wherein the at least one processor is further configured to generate a time-frequency representation using the spectral information.

5. The system of claim 1, wherein the at least one processor is further configured to apply a Kalman filter in analyzing the set of time-series data.

6. The system of claim 1, wherein the at least one processor is further configured to apply an expectation maximization (“

EM”

) algorithm in analyzing the set of time-series data.

7. The system of claim 1, wherein the at least one processor is further configured to determine a depth of anesthesia, a level of sedation, or a change thereof, using the spectral information.

8. The system of claim 1, wherein the at least one processor is further configured to determine a sleep state of the subject using the spectral information.

9. The system of claim 1, wherein the at least one processor is further configured to identify and provide, based on the brain state determined, feedback for controlling the subject to reach a predetermined brain state.

10. The system of claim 1, wherein the at least one processor is further configured to apply a weighting when generating the spectral information that at least one of disfavors increases or fluctuations in low power or noise frequencies relative to at least one preceding interval or favors frequencies with high power relative to at least one preceding interval.

11. A method for analyzing electrophysiological signals acquired from a subject, the method comprising:

receiving electrophysiological signals acquired from a subject using one or more sensors;

assembling a set of non-stationary time-series data using the acquired electrophysiological signals;

analyzing the set of time-series data across a plurality of stationary intervals using a state-space multi-taper framework to generate spectral information describing the electrophysiological signals;

and

generating a report including the spectral information extending with the non-stationary, time-series data.

12. The method of claim 11, wherein the method further comprises acquiring at least one of electroencephalogram (“

EEG”

) signals, electromyography (“

EMG”

) signals, electrocorticography (“

ECoG”

) signals, local field potentials (“

LFP”

) signals, electrocardiography (“

ECG”

) signals, electrooculography (“

EOG”

) signals, galvanic skin response (“

GSR”

) signals, oxygen saturation (“

SAO₂”

) signals, and ocular microtremor (“

OMT”

) signals.

13. The method of claim 11, wherein the method further comprises generating a time-frequency representation using the determined spectral information.

14. The method of claim 11, wherein the method further comprises applying a Kalman filter in analyzing the set of time-series data.

15. The method of claim 11, wherein the method further comprises applying an expectation maximization (“

EM”

) algorithm in analyzing the set of time-series data.

16. The method of claim 11, wherein the method further comprises determining a depth of anesthesia, a level of sedation, or a change thereof.

17. The method of claim 11, wherein the method further comprises determining a sleep state of the subject using the spectral information.

18. The method of claim 11, wherein the method further comprises controlling the subject based on the determined brain state.

19. The method of claim 11, wherein the method further comprises identifying and providing, based on the brain state determined, feedback for controlling the subject to reach a predetermined brain state.

20. The method of claim 11, further comprising apply a weighting when generating the spectral information that at least one of disfavors increases or fluctuations in low power or noise frequencies relative to at least one preceding of the plurality of stationary intervals or favors frequencies with high power relative to at least one preceding of the plurality of stationary intervals.