Computer rejection of EEG artifact. II. Contamination by drowsiness.

As part of an effort to automatically measure a background EEG baseline against which changes due to therapy or experimental manipulations may be measured, algorithms to detect EEG patterns associated with drowsiness have been developed and objectively evaluated. The decision of drowsiness is tentatively based upon changes in simple signal features, including increased ratios of both delta-band to alpha-band and theta-band to alpha-band spectral intensity as compared to thresholds automatically determined from a waking calibration period. Several heuristic criteria are then required to reach a final decision. Thirty-one normal and abnormal, 3-minute, 8-channel clinical EEG recordings containing drowsiness were scored by 5 expert scorers. Out of a total of 106 events labeled drowsy by at least one judge, 85 were found by a consensus of 3 or more of the 5 experts. On the 20 recordings not used for training the decision thresholds (testing data set), the system found 84% for the 85 episodes found by the consensus, and 89% of the 62 episodes found by all 5 scorers. Only one event was found by the system which was not found by any scorer, or which did not border on a consensus-defined episode of drowsiness. This performance is adequate to justify inclusion of these algorithms into a previously described real time EEG analysis system, ADI-EEG, allowing integration of the decisions of the separate subsystems for detection of artifact, sharp transients and drowsiness.