Method and apparatus for the estimation of anesthetic depth using wavelet analysis of the electroencephalogram

A method for extracting information from two observed electrophysiological signals from a subject, in order to evaluate the levels of depression of the central nervous system (CNS) and autonomic nervous system (ANS) of said subject with a processor, said method comprising:

• a) observing the CNS electrophysiological activity of a subject to produce a first observed electrophysiological signal;
• b) observing the ANS electrophysiological activity of the subject to produce a second observed electrophysiological signal;
• c) applying a pre-selected first transformation and first statistical function to the first observed electrophysiological signal to produce the CNS observed data set;
• d) applying a pre-selected second transformation and second statistical function to the second observed electrophysiological signal to produce the ANS observed data set;
• e) comparing the CNS and ANS observed data sets to at least one CNS and at least one ANS reference data set, respectively;
• and
• f) computing with a processor a numerical value or values representative of the levels of depression of the CNS and ANS of the subject, respectively, based at least in part on the comparison
• wherein the at least one CNS reference data set corresponds to a known CNS state, and
• wherein said at least one ANS reference data set corresponds to a known ANS state.

A method for calculating a filtered index of the level of depression of the central nervous system (CNS) of a subject from an electrophysiological signal with a processor, said method comprising:

• a) observing the electrophysiological activity of a subject using signal acquisition circuitry to produce an observed electrophysiological signal segment;
• b) detecting using a processor the presence of electro-cautery artifacts in the observed electrophysiological signal segment;
• c) setting an artifact flag variable to a certain value if one or more electro-cautery artifacts are detected, or to a different value if no artifact is detected;
• d) applying a pre-selected transformation and statistical function to said observed electrophysiological signal segment to produce an observed data set;
• e) comparing the observed data set to at least one reference data set corresponding to a known CNS state;
• f) computing with the processor an unfiltered index of the level of depression of the CNS of the subject based at least in part on the comparison;
• and
• g) calculating the filtered index of the level of depression by inputting to a filter both the unfiltered index of the level of depression and the artifact flag variable.

A method for calculating an index of the level of depression of the central nervous system (CNS) of a subject from an electrophysiological signal with a processor, said method comprising:

• a) observing the electrophysiological activity of a subject to produce an observed electrophysiological signal segment;
• b) detecting using a processor the presence of isoelectric activity in the observed electrophysiological signal segment;
• c) if isoelectric activity is detected, setting the index of the level of depression to a pre-defined value;
• and
• d) if isoelectric activity is not detected:
• i. applying a pre-selected transformation and statistical function to the observed electrophysiological signal to produce an observed data set;
• ii. comparing the observed data set to at least one reference data set corresponding to a known CNS state;
• and
• iii. computing with the processor the index of the level of depression of the CNS of the subject based at least in part on the comparison.