SYSTEM AND METHOD FOR MONITORING ANESTHESIA AND SEDATION USING MEASURES OF BRAIN COHERENCE AND SYNCHRONY

• US 20140316217A1
• Filed: 04/23/2014
• Published: 10/23/2014
• Est. Priority Date: 04/23/2013
• Status: Abandoned Application

• Current Assignee: Massachusetts Institute of Technology, The General Hospital Corporation (Mass General Brigham, Inc.)
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Purdon, Patrick L., Brown, Emery N., Lewis, Laura D., Akeju, Oluwaseun
• Application Number:
  US14/260,006
• Publication Number:
  US 20140316217A1
• Time in Patent Office: Days
• US Class Current: 600/301
• 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 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/37 Intracranial electroencephalography [IC-EEG], e.g. electrocorticography [ECoG]
• 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 5/384 Recording apparatus or displays specially adapted therefor
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• 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: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system and method for monitoring and controlling the administration of at least one drug having anesthetic properties are provided. In certain embodiments, the method includes assembling physiological data, obtained from a plurality of sensors placed on a subject, into sets of time-series data, separating, from the sets of time-series data, a plurality of low frequency signals, and determining, from the plurality of low frequency signals, at least one of coherence information and synchrony information. The method can also include identifying, using the at least one of the coherence information and the synchrony information, spatiotemporal signatures indicative of at least one of a current state and a predicted future state of the patient consistent with the administration of at least one drug having anesthetic properties and generating a report indicating at least one of the current state and the predicted future state of the patient induced by the drug.

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 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;

receive the physiological data from the plurality of sensors and the indication from the user interface;

separate, from the physiological data, a plurality of low frequency signals;

determine, from the plurality of low frequency signals, at least one of coherence information and synchrony information;

identify, using the at least one of the coherence information and the synchrony information, spatiotemporal signatures indicative of at least one of a current state and a predicted future state of the patient consistent with administration of at least one drug having anesthetic properties; and

generate a report indicating 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 plurality of low frequency signals are within a frequency range between 0.1 Hz and 1 Hz.

3. The system of claim 1 wherein the at least one processor is further configured to use the current state, determined spatiotemporal signatures, and indication are in a model to determine the predicted future state of the patient.

4. The system of claim 1 wherein the processor is configured to assemble the physiological data into sets of time-series data and transform each set of time-series data into a spectrogram to determine at least one of the current state and the predicted future state of the patient.

5. The system of claim 1 wherein the processor is configured to assemble the physiological data into sets of time-series data and each set of time-series data is transformed into a coherogram to determine at least one of the current state and the predicted future state of the patient.

6. The system of claim 1 wherein the processor is configured to perform a phase analysis on the plurality of low frequency signals to measure a time-resolved phase-coupling and identify synchrony information corresponding to at least one of the current state and the predicted future state of the patient.

7. The system of claim 1 wherein the processor is configured to perform a coherence analysis on the plurality of low frequency signals to measure a frequency-dependent covariance and identify coherence information corresponding to at least one of the current state and the predicted futures state of the patient.

8. The system of claim 1 wherein the indication of at least one of a characteristic of the patient includes at least one of an age of the patient, drug administration information including at least one of drug timing, drug dose, and drug administration rate, and the at least one drug having anesthetic properties is selected 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, and Alfentanil.

9. The system of claim 1 wherein the processor is configured to perform a dynamic processing method to characterize the patient as exhibiting a predetermined behavioral dynamic, wherein the behavioral dynamic includes at least one of a loss of consciousness and recovery of consciousness.

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

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

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

reviewing the physiological data from the at least one sensor;

identifying, from the physiological data, a plurality of low frequency signals;

determining, from the plurality of low frequency signals, at least one of coherence information and synchrony information;

identifying, using the at least one of the coherence information and the synchrony information, spatiotemporal signatures indicative of at least one of a current state and a predicted future state of the patient consistent with the administration of at least one drug having anesthetic properties; and

generating a report indicating at least one of the current state and the predicted future state of the patient induced by the drug.

12. The method of claim 11 wherein the low frequency signals are within a frequency range between 0.1 Hz and 1 Hz.

13. The method of claim 11 further comprising using the current state, determined spatiotemporal signatures, and the indication in a model to determine the predicted future state of the patient.

14. The method of claim 11 further comprising transforming the physiological data into a spectrogram and analyzing the spectrogram to determine at least one of the current state and the predicted future state of the patient.

15. The method of claim 11 further comprising transforming the physiological data into a coherogram and analyzing the coherogram to determine at least one of the current state and the predicted future state of the patient.

16. The method of claim 11 further comprising performing a phase analysis on the plurality of low frequency signals to measure a phase-coupling to identify synchrony information corresponding to at least one of the current state and the predicted future state of the patient.

17. The method of claim 11 further comprising performing a coherence analysis on the plurality of low frequency signals to measure a frequency-dependent covariance to identify coherence information corresponding to at least one of the current state and the predicted futures state of the patient.

18. The method of claim 11 wherein the at least one drug having anesthetic properties is selected 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, and Alfentanil.

19. The method of claim 11 further comprising implementing a dynamic processing method to characterize the patient as exhibiting a predetermined behavioral dynamic, and wherein the behavioral dynamic includes at least one of a loss consciousness and recovery of consciousness.

20. The method of claim 11 wherein the report indicates spatiotemporal activity at different states of the patient receiving the drug.

21. The method of claim 11 wherein identifying spatiotemporal signatures includes generating at least one of a spectrogram and a coherogram using multitaper method.