MUSCLE ARTIFACTS IN THE SLEEP EEG - AUTOMATED DETECTION AND EFFECT ONALL-NIGHT EEG POWER SPECTRA

Owing to the use of scalp electrodes in human sleep recordings, cortic al EEG signals are inevitably intermingled with the electrical activit y of the muscle tissue on the skull. Muscle artifacts are characterize d by surges in high frequency activity and are readily identified beca use of their outlying high values relative to the local background act ivity. To detect bursts of myogenic activity a simple algorithm is int roduced 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 activi ty by a certain factor. Sensitivity and specificity of the artifact de tection algorithm were empirically adjusted by applying different fact ors as artifact thresholds. In an analysis of sleep EEG signals record ed from 25 healthy young adults 2.3% (SEM: 0.16) of all 4-s epochs dur ing sleep were identified as artifacts when a threshold factor of four was applied. Contamination of the EEG by muscle activity was more fre quent towards the end of non-REM sleep episodes when EEG slow wave act ivity declined. Within and across REM sleep episodes muscle artifacts were evenly distributed. When the EEG signal was cleared of muscle art ifacts, the all-night EEG power spectrum showed significant reductions in power density for all frequencies from 0.25-32.0 Hz. Between 15 an d 32 Hz, muscle artifacts made up a substantial part (20-70%) of all-n ight EEG power density. It is concluded that elimination of short-last ing muscle artifacts reduces the confound between cortical and myogeni c activity and is important in interpreting quantitative EEG data. Qua ntitative approaches in defining and detecting transient events in the EEG signal may help to determine which EEG phenomena constitute clini cally significant arousals.