Systems and Methods for Tracking Non-Stationary Spectral Structure and Dynamics in Physiological Data

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.

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; 
 (iii) 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; and 
 (iv) track the peak parameters of the at least one non-stationary spectral peak over time. 
   
     
     
         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 tin 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 processor is further configured to generate a report 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;   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 a 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; and   tracking the peak parameters of the at least one spectral peak over time.   
     
     
         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 tin 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 , further comprising generating a report 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; 
 (iii) 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; and 
 (iv) track the spectral parameters of the at least one non-stationary spectral feature over time. 
   
     
     
         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 processor is further configured to generate a report indicative of a physiological state of the subject using the tracked spectral parameters.