SYSTEM AND METHOD FOR TRACKING BRAIN STATES DURING ADMINISTRATION OF ANESTHESIA

• US 20140187973A1
• Filed: 05/07/2012
• Published: 07/03/2014
• Est. Priority Date: 05/06/2011
• Status: Abandoned Application

• Current Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Original Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Inventors: Brown, Emery N., Purdon, Patrick L., Cimenser, Aylin, Mukamel, Eran A., Prerau, Michael J.
• Application Number:
  US14/115,682
• Publication Number:
  US 20140187973A1
• Time in Patent Office: Days
• US Class Current: 600/483
• CPC Class Codes: A61B 5/02 Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• 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 5/38 Acoustic or auditory stimuli
• 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 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/40 : relating to drugs, e.g. their side effects or intended usage

Abstract

A method and system for determining the state of a patients brain under anesthesia. The present invention recognizes that anesthesia compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of physiological characteristics and signatures therein based on a selected anesthesia compound. The present invention aids the monitoring and/or interpreting of the physiological characteristics to a state of the patient'"'"'s brain.

Claims

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

a plurality of sensors configured to acquire physiological data from the patient;

at least one processor configured to;

assemble the physiological data into sets of time-series data associated with an origin location of the patient;

transform each set of time-series data into spectrum information;

determine coherence information with respect to the associated origin locations associated with the time-series of data;

identify signatures within at least one of the spectrum information and the coherence information indicative of at least one of a current state and a predicted future state of the patient; and

generate a report using the signatures including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.

2. The system of claim 1 wherein the report indicates spatiotemporal activity at different states of the patient receiving the drug.

3. The system of claim 1 wherein the at least one processor is further configured to analyze an amplitude envelope of activity in at least one set of time-series data to identify a maxima at a selected frequency based on a phase of a lower frequency rhythm.

4. The system of claim 1 wherein the processor is configured to perform a modulogram analysis to measure a phase-amplitude coupling in a time-resolved fashion to identify modes of phase-amplitude coupling corresponding to states of the patient receiving the drug.

5. The system of claim 4 wherein the processor is further configured to determine at least one of a spatial and a local average of data acquired by several of the plurality of sensors to perform the modulogram analysis.

6. The system of claim 1 wherein the processor is further configured to receive an indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties and identify the signatures based on the indication.

7. The system of claim 6 wherein the indication includes at least one of an age of the patient, a drug selecting from the list consisting essentially of Propofol, Etomidate, Barbiturates, Thiopental, Pentobarbital, Phenobarbital, Methohexital, Benzodiazepines, Midazolam, Diazepam, Lorazepam, Dexmedetomidine, Ketamine, Sevoflurane, Isoflurane, Desflurane, Remifenanil, Fentanyl, Sufentanil, Alfentanil, and drug administration information including at least one of drug timing, drug dose, and drug administration rate.

8. The system of claim 1 wherein the processor is configured to implement a dynamic Bayesian processing method to characterize the patient as exhibiting a predetermined behavioral dynamic based on the spectrum information and the coherence information.

9. The system of claim 8 wherein the behavioral dynamic includes at least one of a loss consciousness and recovery of consciousness.

10. The system of claim 1 wherein the plurality of sensors include at least one of electroencephalography (EEG) sensors, heart rate monitors, blood pressure monitors, and sensors configured to monitor galvanic skin response (GSR).

11. The system of claim 1 wherein the processor is further configured to transform each set of time-series data into a spectrogram and identify signatures within at least one of the spectrogram and the coherence information indicative of at least one of a current state and a predicted future state of the patient.

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

a plurality of sensors configured to acquire physiological data from the patient;

a user interface configured to receive an indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties;

at least one processor configured to;

identify signature profiles indicative of at least one of a current state and a predicted future state of the patient based on the indication;

assemble the physiological data into sets of time-series data;

analyze the sets of time-series data using the identified signature profiles; and

generate a report including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.

13. The system of claim 12 wherein the processor is configured to assemble the physiological data into sets of time-series data based on an origin location of the patient.

14. The system of claim 12 wherein the processor is further configured to determine coherence information with respect to the associated origin locations associated with the sets of time-series data and analyze the coherence information using the identified signature profiles to determine at least one of the current state and the predicted future state of the patient.

15. The system of claim 12 wherein the processor is configured to transform each set of time-series data into a spectrogram and analyze the spectrogram using the identified signature profiles to determine at least one of the current state and the predicted future state of the patient.

16. The system of claim 12 wherein the report indicates spatiotemporal activity at different states of the patient receiving the drug.

17. The system of claim 12 wherein the at least one processor is further configured to analyze an amplitude envelope of activity in at least one set of time-series data to identify a maxima at a selected frequency based on a phase of a lower frequency rhythm.

18. The system of claim 12 wherein the processor is configured to perform a modulogram analysis to measure a phase-amplitude coupling in a time-resolved fashion to identify modes of phase-amplitude coupling corresponding to states of the patient receiving the drug.

19. The system of claim 12 wherein indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties includes at least one of an age of the patient, a drug selecting from the list consisting essentially of Propofol, Etomidate, Barbiturates, Thiopental, Pentobarbital, Phenobarbital, Methohexital, Benzodiazepines, Midazolam, Diazepam, Lorazepam, Dexmedetomidine, Ketamine, Sevoflurane, Isoflurane, Desflurane, Remifenanil, Fentanyl, Sufentanil, Alfentanil, and drug administration information including at least one of drug timing, drug dose, and drug administration rate.

20. The system of claim 12 wherein the processor is configured to implement a dynamic Bayesian processing method to characterize the patient as exhibiting a predetermined behavioral dynamic.

21. The system of claim 19 wherein the behavioral dynamic includes at least one of a loss consciousness and recovery of consciousness.