Muscle artifacts in the sleep EEG: automated detection and effect on all-night EEG power spectra.

Owing to the use of scalp electrodes in human sleep recordings, cortical EEG signals are inevitably intermingled with the electrical activity of the muscle tissue on the skull. Muscle artifacts are characterized by surges in high frequency activity and are readily identified because of their outlying high values relative to the local background activity. To detect bursts of myogenic activity a simple algorithm is introduced that compares high frequency activity (26.25-32.0 Hz) in each 4-s epoch with the activity level in a local 3-min window. A 4-s value was considered artifactual if it exceeded the local background activity by a certain factor. Sensitivity and specificity of the artifact detection algorithm were empirically adjusted by applying different factors as artifact thresholds. In an analysis of sleep EEG signals recorded from 25 healthy young adults 2.3% (SEM: 0.16) of all 4-s epochs during sleep were identified as artifacts when a threshold factor of four was applied. Contamination of the EEG by muscle activity was more frequent towards the end of non-REM sleep episodes when EEG slow wave activity declined. Within and across REM sleep episodes muscle artifacts were evenly distributed. When the EEG signal was cleared of muscle artifacts, the all-night EEG power spectrum showed significant reductions in power density for all frequencies from 0.25-32.0 Hz. Between 15 and 32 Hz, muscle artifacts made up a substantial part (20-70%) of all-night EEG power density. It is concluded that elimination of short-lasting muscle artifacts reduces the confound between cortical and myogenic activity and is important in interpreting quantitative EEG data. Quantitative approaches in defining and detecting transient events in the EEG signal may help to determine which EEG phenomena constitute clinically significant arousals.

Automating the sleep laboratory: implementation and validation of digital recording and analysis.

OBJECTIVES: We report on the implementation of digital processing in a large clinical and research sleep laboratory. The system includes the digital collection, display, analysis, and repository of physiological signals collected during sleep. METHODS: After describing the original analog system, the computer equipment and software necessary for the digital implementation are presented and we explain our algorithms for rapid eye movement (REM) and delta-wave detection. Finally, we describe an experiment validating the digital system of display and analyses. CONCLUSIONS: The digital processing of sleep signals saves computer operator, polysomnographic technologist, and computer time. It also saves resources such as polysomnographic paper and FM tape. The digital signals lend themselves to a large array of analysis techniques and result in improved signal quality. Automated REM and delta-wave detection via digital processing correlate highly with visual counts of rapid eye movements and delta waves.