Anesthesia monitoring system based on electroencephalographic signals

An apparatus for classifying the level of awareness of a patient using electroencephalograph (EEG) signals, comprising:

• a. a plurality of patient electrodes whereby a plurality of patient EEG signals is acquired;
• b. a patient module connected to the patient electrode set;
• and
• c. an analysis unit connected to the patient module,
• in which the patient module and the analysis unit each comprises a plurality of channels corresponding to the plurality of patient electrodes;
• in which each of the plurality of channels in the analysis unit comprises a plurality of modules comprising observers tuned to produce measures of specific characteristics in the plural EEG signals, the output of which observers is directed to a single observer mediator which combines the output of the plural observers in the plural channels into a single derived parameter;
• and
• in which the observer mediator weighs the output of the plural observers in the plural channels by monitoring each observer's input signal quality and the context of the observation based on the patient's state of awareness or anesthesia.

An apparatus for electronically estimating the level of a patient's awareness using electroencephalograph (EEG) signals comprising

• a. a predetermined number of plural channels for EEG data;
• b. a corresponding plurality of electrodes which acquire and transmit from the patient's head a plurality of EEG signals corresponding in number to the plural channels;
• c. an additional channel into which a duplicate signal of the signal in one of the predetermined plurality of channels is transmitted;
• d. in the additional channel, an overlapped epoch buffer;
• e. in the additional channel connected to the overlapped epoch buffer, a module producing the Beta 5 band power using a single channel fast fourier transform (FFT) generator and Beta 5 band binding;
• f. a decimator, and a high pass filter in each of the plural channels of EEG signals;
• g. a corresponding plurality of artifacter banks, one in each of the plurality of channels, each bank comprising a plurality of time domain artifact detectors;
• h. a Raw Time Series Data Buffer comprising a corresponding plurality of buffers storing a corresponding plurality of epochs of signals from the plural channels, each of the plurality of epochs of signals comprising overlapped partial epochs of partial epoch length signals from the corresponding plural channels;
• i. in each of the plural channels, an artifact type-categorizer;
• j. in each of the plural channels, a magnitude artifact detector as one of the plurality of time domain artifact detectors;
• k. in each of the plural channels, at least one eye-blink detector as at least one of the plurality of time domain artifact detectors;
• l. in each of the plural channels, a stationarity/RMS detector as one of the plurality of time domain artifact detectors;
• m. in each of the plural channels, a slew rate artifact detector as one of the plurality of time domain artifact detectors;
• n. in each of the plural channels, a suppression detector/classifier as one of the plurality of time domain artifact detectors;
• o. in each of the plural channels, an artifact index generator;
• p. in each of the plural channels, an FFT generator producing a corresponding plurality of channels of complex frequency domain parameters;
• q. an EMG Index generator comprising a Beta 2z band processor;
• r. an EMG Beta-5 Observer;
• s. an Artifact Index generator;
• t. a Suppression Ratio generator;
• u. a discriminant processor;
• v. an observer-mediation module producing a patient state index (PSI) from the outputs of other observers;
• w. a PSI trend averaging module;
• x. a display apparatus displaying the Artifact Index, the PSI Trend Value, the EMG Index, and the Suppression Ratio.

A method of classifying anesthetized patients according to their conscious state as determined from an analysis of volunteer data using the OAA/S scale by performing the steps of:

• a. analyzing artifacts in time domain EEG data resulting in the rejection of data contaminated by predetermined types of artifacts;
• b. generating artifact labels and parameters;
• c. generating the Fast Fourier Transform (FFT) of segments of EEG data resulting in a frequency domain representation of the EEG data and computing therefrom the signal power in predetermined frequency bands;
• d. applying statistical analysis to various measures in the predetermined frequency bands to provide a probability of classification into aware and unaware groups, with reference to population norns obtained from statistical studies of large normative populations;
• e. combining the results of statistical analysis with other observations of the patient state to provide a single quantitative index of the patient's conscious state.

A method for monitoring the state of anesthesia of a medical patient using electroencephalograph (EEG) signals, comprising:

• a. acquiring a plurality of EEG signals from a preselected corresponding plurality of channels electrically connected to a plurality of international standard locations on a patient's head;
• b. digitizing and decimating the EEG signals in each of the plurality of channels;
• c. applying a plurality of time domain artifact detection modules to the signals in the plurality of channels;
• d. applying an eye blink observer to the signals in each of the plurality of channels and determining from them epochs with eye blinks
• e. applying a burst suppression observer to the signals in each of the plurality of channels and determining therefrom a suppression code;
• f. storing overlapped half-epoch decimated EEG signals in a buffer;
• g. performing a fast fourier transform with a resolution of 0.25 Hz or better on half epochs and epochs having no artifact label;
• h. using the fast Fourier transform results to compute a predetermined plurality of power measures for predetermined frequency bands;
• i. developing a classification probability by statistical analysis of the power measures;
• j. applying an EMG Beta-5 observer to the power measures
• k. determining a suppression ratio, an EMG index, and an artifact index;
• l. applying mediation logic to the results of all observers and indices, resulting in the patient state index,
• m. displaying the patient state index, the suppression ratio, the EMG index, and the artifact index on a display unit;
• n. administering anesthesia to the patient;
• o. repeating steps a. through l. at predetermined intervals and displaying the patient state index, the suppression ratio, the EMG index, and the artifact index on a display unit in a time ordered sequence;
• p. adjusting the anesthesia administered to the patient so as to attain a plane of anesthesia selected by the anesthesiologist;
• and
• q. adjusting the anesthesia administered to the patient in response to changes in the patient state index, the suppression ratio, the EMG index, and the artifact index.

A method for monitoring the state of anesthesia of a medical patient using electroencephalograph (EEG) signals, comprising:

• a. decimating incoming EEG signals;
• b. performing artifact analysis on the time domain EEG signals for validity and contamination;
• c. setting artifact codes and calculating artifact-related quantities;
• d. applying an eye blink observer and a suppression observer;
• e. determining the Fast Fourier Transform coefficients of the EEG data for each of the four channels;
• f. dividing the FFT spectral data into a plurality of frequency bands, and determining raw measures (either power or mean frequency) for the frequency bands;
• g. applying an EMG Beta 5 observer to these raw measures;
• h. applying statistical discriminant analysis to these raw measures, and determining a probability of correct classification into an aware population group;
• i. combining the probability with the results of other observers of the patient state to produce the Patient State Index (PSI).

A method for monitoring the state of anesthesia of a medical patient independent of the anesthetic agent comprising the steps of

• a. analyzing information in the frequency range of 0.5 Hz to 50 Hz;
• b. removing noisy or invalid data so as to provide artifact free frequency domain representations of EEG signals;
• c. extracting from the artifact free frequency domain representations specific frequency domain raw signal measures;
• d. applying a set of observers to both the time-domain data and frequency-domain measures;
• e. monitoring and evaluating these measures over time and providing statistical analysis of particular components of a montage of EEG signals;
• f. deriving a set of four processed parameters every epoch:
• the Patient State Index characterizing the relative state of consciousness of an anesthetized patient;
• the Suppression Ratio indicating the relative amount of time that the patient's EEG waveforms exhibit a characteristic Burst/Suppression pattern;
• the EMG index (EMG) indicating muscle activity as a weighted percentage of half-epochs (over the past one minute) in which muscle activity, as measured by the power in the BETA-2 band, exceeds a predetermined threshold level;
• and the Artifact Index indicating data quality or the amount of artifacts present in the data;
• g. scaling the Patient State Index so that it has a range of 0 to 100;
• and
• h. providing upper and lower thresholds of the patient state index within which the patient will be said to be in an appropriate level of unconsciousness.