SYSTEM AND METHOD FOR ESTIMATING HIGH TIME-FREQUENCY RESOLUTION EEG SPECTROGRAMS TO MONITOR PATIENT STATE

• US 20140323897A1
• Filed: 04/24/2014
• Published: 10/30/2014
• Est. Priority Date: 04/24/2013
• Status: Abandoned Application

• Current Assignee: Behtash Babadi, Demba Ba, Emery N. Brown, Patrick L. Purdon
• Original Assignee: Behtash Babadi, Demba Ba, Emery N. Brown, Patrick L. Purdon
• Inventors: Brown, Emery N., Purdon, Patrick L., Ba, Demba, Babadi, Behtash
• Application Number:
  US14/261,166
• Publication Number:
  US 20140323897A1
• Time in Patent Office: Days
• US Class Current: 600/544
• 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 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence

Abstract

A system and method for monitoring a patient includes a sensor configured to acquire physiological data from a patient and a processor configured to receive the physiological data from the at least one sensor. The processor is also configured to apply a spectral estimation framework that utilizes structured time-frequency representations defined by imposing, to the physiological data, a prior distributions on a time-frequency plane that enforces spectral estimates that are smooth in time and sparse in a frequency domain. The processor is further configured to perform an iteratively re-weighted least squares algorithm to perform yield a denoised time-varying spectral decomposition of the physiological data and generate a report indicating a physiological state of the patient.

Claims

1. A system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties, the system comprising:

at least one sensor configured to acquire physiological data from a patient;

a processor configured to;

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

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

(iii) apply a state-space model for the time-frequency representation of the signals to enforce spectral estimates that are smooth in time and sparse in a frequency domain;

(iv) iteratively adjust weightings associated with the spectral estimates to converge the data toward a desired outcome; and

(v) generate a report indicating a physiological state of the patient.

2. The system of claim 1 wherein the processor is further configured to use a Bayesian formulation to perform (iii).

3. The system of claim 1 wherein the processor is further configured to perform iteratively re-weighted least squares (IRLS) algorithm to perform (iv).

4. The system of claim 1 wherein the desired outcome includes determining a global maximum to a maximum a-posterior (MAP) estimation problem.

5. The system of claim 1 wherein the processor, to perform (iii), is further configured to impose a prior distribution on a time-frequency plane, which enforces spectral estimates that are smooth in time, and sparse in the frequency domain

6. The system of claim 1 wherein the report includes a spectrogram.

7. The system of claim 1 wherein the processor is further configured to perform a Bayesian estimation the state-space model to compute a highly-structured spatio-temporal decomposition with respect to the spectral estimates.

8. A system for monitoring a patient, the system comprising:

at least one sensor configured to acquire physiological data from a patient;

a processor configured to;

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

(ii) apply a spectral estimation framework that utilizes structured time-frequency representations defined by imposing, to the physiological data, a prior distributions on a time-frequency plane that enforces spectral estimates that are smooth in time and sparse in a frequency domain;

(iii) perform an iteratively re-weighted least squares algorithm to perform yield a denoised time-varying spectral decomposition of the physiological data; and

(iv) generate a report indicating a physiological state of the patient.

9. The system of claim 8 wherein the processor is further configured to assemble the physiological data into a time-series of signals and to decompose the time-series of signals into a small number of harmonic components to perform (iii).

10. The system of claim 9 wherein the processor is further configured use a regularization parameter estimated from the time-series of signals using cross-validation to decompose the time-series of signals into a small number of harmonic components

11. The system of claim 8 wherein the processor is further configured to perform iteratively re-weighted least squares (IRLS) algorithm to perform (iii).

12. The system of claim 8 wherein the processor is further configured to perform a Bayesian estimation a state-space model to compute the decomposition of the physiological data.

13. The system of claim 8 wherein the report includes a spectrogram.

14. The system of claim 8 further comprising an input configured to receive input about an administration of at least one drug having anesthetic properties to the patient.

15. A method of processing a time-series of data comprising:

applying a spectral estimation framework that utilizes structured time-frequency representations (x) of the time-series of data (y) defined by;

imposing, on the time-series of data, a prior distribution in a time-frequency plane that enforces spectral estimates that are smooth in time and sparse in the frequency domain;

determining, using an iteratively re-weighted least squares (IRLS) algorithm, spectral estimates that are maximum a posteriori (MAP) spectral estimates; and

generating a report indicating using the spectral estimates that are maximum a posteriori (MAP) spectral estimates.

16. The method of claim 15 wherein the report includes a spectrogram.

17. The method of claim 15 wherein the time-series of data includes electroencephalogram (EEG) data.

18. The method of claim 15 wherein the MAP spectral estimates are calculated using the following:

19. The method of claim 15 further comprising acquiring the time-series of data from at least one sensor coupled to a patient experiencing an administration of at least one drug having anesthetic properties.

20. The method of claim 19 wherein the at least one drug having anesthetic properties includes at least one of Propofol, Etomidate, Barbiturates, Thiopental, Pentobarbital, Phenobarbital, Methohexital, Benzodiazepines, Midazolam, Diazepam, Lorazepam, Dexmedetomidine, Ketamine, Sevoflurane, Isoflurane, Desflurane, Remifenanil, Fentanyl, Sufentanil, Alfentanil.