Systems and methods for tracking non-stationary spectral structure and dynamics in physiological data

• US 10,314,503 B2
• Filed: 06/27/2014
• Issued: 06/11/2019
• Est. Priority Date: 06/27/2013
• Status: Active Grant

• Current Assignee: The General Hospital Corporation (Mass General Brigham, Inc.), Trustees of Boston University
• Original Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Inventors: Prerau, Michael J., Purdon, Patrick L., Eden, Uri
• Primary Examiner(s): Weston, Tiffany
• Assistant Examiner(s): Tran, Tho Q
• Application Number:
  US14/900,805
• Publication Number:
  US 20160150988A1
• Time in Patent Office: 1,810 Days
• 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 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• 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 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7253 characterised by using transforms

Abstract

Systems and methods for tracking dynamic structure in physiological data are provided. In some aspects, the method includes providing physiological data, including electroencephalogram (“EEG”) data, acquired from a subject and assembling a time-frequency representation of signals from the physiological data. The method also includes generating a dynamic model of at least one non-stationary spectral feature, such as at least one non-stationary spectral peak, using the time-frequency representation and a user indication, and applying a dynamic model of at least one non-stationary spectral feature in a parameter estimation algorithm to compute concurrent estimates of spectral parameters describing the at least one non-stationary spectral feature. The method also includes tracking the spectral parameters of the at least one spectral feature over time.

Claims

1. A system for tracking dynamic structure in physiological data, the system comprising:

at least one input configured to receive electroencephalography (“

EEG”

) data acquired from a subject;

a processor configured to;

(i) receive the EEG data from the at least one input;

(ii) assemble a time-frequency representation of signals from the EEG data;

(iii) generate a dynamic model of at least one non-stationary spectral peak using the time-frequency representation and a user indication;

(iv) apply the dynamic model in a parameter estimation algorithm to compute concurrent estimates of peak parameters describing the at least one non-stationary spectral peak, the peak parameters including a peak frequency, a peak bandwidth and a peak amplitude;

(v) generate a report tracking the peak parameters of the at least one non-stationary spectral peak over time; and

a display providing the report to a user.

2. The system of claim 1, wherein the time-frequency representation includes a spectrogram representation indicative of a time variation in a spectral power distribution describing the signals.

3. The system of claim 1, wherein the dynamic model characterizes dynamics of the at least one non-stationary spectral peak using at least one spectral decomposition function.

4. The system of claim 1, wherein the processor is further configured to compute a posterior probability distribution, at a time t, of the peak parameters of the at least one non-stationary spectral peak given the physiological data, the posterior probability distribution being proportional to an instantaneous likelihood.

5. The system of claim 4, wherein the processor is further configured to construct a set of particles in the parameter estimation algorithm, using the instantaneous likelihood and peak parameters.

6. The system of claim 5, wherein the processor is further configured to initialize the set of particles from a proposal density determined using information in accordance with one or both of the user indication or a physiological precedent.

7. The system of claim 5, wherein the processor is further configured to sample a new value for each of the set of particles at a time t in accordance with a prediction density.

8. The system of claim 5, wherein the processor is further configured to resample the set of particles according to normalized weights computed using the instantaneous likelihood.

9. The system of claim 5, wherein an estimate of peak parameters at a time t is defined as a component-wise median of the set of particles.

10. The system of claim 5, wherein the processor is further configured to determine confidence values for the peak parameters by computing component-wise percentile values of the set of particles using a predetermined significance.

11. The system of claim 1, wherein the report is indicative of a physiological state of the subject using the tracked peak parameters.

12. A method for tracking dynamic structure in physiological data comprising:

providing electroencephalogram (“

EEG”

) data acquired from a subject using a plurality of EEG sensors;

using at least one processor configured for;

assembling a time-frequency representation of signals from the EEG data;

generating a dynamic model of at least one non-stationary spectral peak using the time-frequency representation and a user indication;

applying the dynamic model of at least one non-stationary spectral peak in a parameter estimation algorithm to compute concurrent estimates of peak parameters describing the at least one non-stationary spectral peak, the peak parameters including a peak frequency, a peak bandwidth and a peak amplitude;

generating a report tracking the peak parameters of the at least one non-stationary spectral peak over time; and

providing the report to a user.

13. The method of claim 12, wherein the time-frequency representation includes a spectrogram representation indicative of a time variation in a spectral power distribution describing the signals.

14. The method of claim 12, wherein the dynamic model characterizes dynamics of the at least one non-stationary spectral peak using at least one spectral decomposition function.

15. The method of claim 13, further comprising computing a posterior probability distribution, at a time t, of the peak parameters of the at least one non-stationary spectral peak given the physiological data, the posterior probability distribution being proportional to an instantaneous likelihood.

16. The method of claim 15, further comprising constructing a set of particles in the parameter estimation algorithm, using the instantaneous likelihood and peak parameters.

17. The method of claim 16 further comprising initializing the set of particles from a proposal density determined using information in accordance with one or both of the user indication or a physiological precedent.

18. The method of claim 16 further comprising sampling a new value for each of the set of particles at a time t in accordance with a prediction density.

19. The method of claim 16 further comprising resampling the set of particles according to normalized weights computed using the instantaneous likelihood.

20. The method of claim 16, wherein an estimate of peak parameters at a time t is defined as a component-wise median of the set of particles.

21. The method of claim 16 further comprising determining confidence values for the peak parameters by computing component-wise percentile values of the set of particles using a predetermined significance.

22. The method of claim 12, further comprising acquiring the time-series of EEG data using at least one sensor coupled to the subject experiencing an administration of at least one drug having anesthetic properties, a psychiatric condition, a neurological condition, sleep, or any combination thereof.

23. The method of claim 12, wherein the report is indicative of a physiological state of the subject using the tracked peak parameters.

24. A system for tracking dynamic structure in physiological data, the system comprising:

at least one input configured to receive physiological data acquired from a subject;

a processor configured to;

(i) receive the physiological data from the at least one input;

(ii) assemble a time-frequency representation of signals from the physiological data;

(iii) generate a dynamic model of at least one non-stationary spectral feature using the time-frequency representation and a user indication;

(iv) apply the dynamic model in a parameter estimation algorithm to compute concurrent estimates of spectral parameters describing the at least one non-stationary spectral feature;

(v) generate a report tracking the spectral parameters of the at least one non-stationary spectral feature over time; and

(vi) communicate the report to one of a user via a display or to a drug delivery system to control operation of the drug delivery system.

25. The system of claim 24, wherein the physiological data includes electroencephalography (“

EEG”

) data.

26. The system of claim 24, wherein the time-frequency representation includes a spectrogram representation indicative of a time variation in a spectral power distribution describing the signals.

27. The system of claim 24, wherein the dynamic model characterizes dynamics of the at least one non-stationary spectral feature using at least one spectral decomposition function.

28. The system of claim 24, wherein the processor is further configured to compute a posterior probability distribution, at a time t, of the peak parameters of the at least one non-stationary spectral peak given the physiological data, the posterior probability distribution being proportional to an instantaneous likelihood.

29. The system of claim 24, wherein the processor is further configured to apply a statistical sampling technique, a Kalman filtering technique, a variational Bayes estimator technique, and an Expectation-Maximization (“

EM”

) technique in the parameter estimation algorithm.

30. The system of claim 24, wherein the processor is further configured to initialize parameter values from a proposal density determined using information in accordance with one or both of the user indication or a physiological precedent.

31. The system of claim 24, wherein the spectral parameters include a peak frequency, a peak bandwidth and a peak amplitude.

32. The system of claim 24, wherein the processor is further configured to determine confidence values for the spectral parameters.

33. The system of claim 24, wherein the processor is further configured to generate multiple dynamic models related to dynamic features of the physiological data, and compare the dynamic models using a relative goodness-of-fit indicator.

34. The system of claim 24, wherein the processor is further generate the dynamic model using information related to physiological correlates including a drug level, a drug concentration, and a behavior, or any combination thereof.

35. The system of claim 24, wherein the report is indicative of a physiological state of the subject using the tracked spectral parameters.