Delta sleep deficits in schizophrenia: evidence from automated analyses of sleep data.

BACKGROUND: Several, though not all, polysomnographic studies that use conventional visual scoring techniques show delta sleep deficits in schizophrenia. Delta sleep (in particular, > or = 1- to 2-Hz frequency range), mediated by thalamocortical circuits, is postulated to be abnormal in schizophrenia. We investigated whether deficits in delta sleep occur in schizophrenia and whether these are primarily related to the illness or are epiphenomena of previous medication use or illness chronicity. METHODS: We compared 30 unmedicated schizophrenic patients and 30 age- and sex-matched controls for sleep data evaluated by visual scoring as well as automated period amplitude analyses and power spectral analyses. RESULTS: Schizophrenic patients had reduced visually scored delta sleep. Period amplitude analyses showed significant reductions in delta wave counts but not rapid eye movement counts; power spectral analyses showed reductions in delta as well as theta power. Delta spectral power was also reduced in the subset of 19 neuroleptic-naive, first-episode schizophrenic patients compared with matched controls. Delta deficits were more pronounced in the greater than 1- to 2-Hz frequency range. CONCLUSIONS: Delta sleep deficits that occur in schizophrenia may be related to the primary pathophysiological characteristics of the illness and may not be secondary to previous neuroleptic use. Automated sleep quantification by means of period amplitude and power spectral analyses can complement the use of conventional visual scoring for understanding electrophysiological abnormalities in psychiatric disorders.

Power spectral analysis of EEG in a multiple-bedroom, multiple-polygraph sleep laboratory.

OBJECTIVES: We describe the methods for power spectral analysis (PSA) of sleep electroencephalogram (EEG) data at a large clinical and research sleep laboratory. The multiple-bedroom, multiple-polygraph design of the sleep laboratory poses unique challenges for the quantitative analysis of the data. This paper focuses on the steps taken to ensure that our PSA results are not biased by the particular bedroom or polygraph from which the data were acquired. METHODS: After describing the data acquisition system hardware, we present our signal amplitude calibration procedure and our methods for performing PSA. We validate the amplitude calibration procedure in several experiments using PSA to establish tolerances for data acquisition from multiple bedrooms and polygraphs. RESULTS: Since it is not possible to acquire identical digitized versions of an EEG signal using different sets of equipment, the best that can be achieved is data acquisition that is polygraph-independent within a known tolerance. We are able to demonstrate a tolerance in signal amplitude of +/- 0.25% when digitizing data from different bedrooms. When different data acquisition hardware is used, the power tolerance is approximately +/- 3% for frequencies from 1 to 35 Hz. The power tolerance is between +/- 3 and +/- 7% for frequencies below 1 Hz and frequencies between 35 and 50 Hz. Additional data demonstrate that variability due to the hardware system is small relative to the inherent variability of the sleep EEG. CONCLUSION: The PSA results obtained in one location can be replicated elsewhere (subject to known tolerances) only if the data acquisition system and PSA method are adequately specified.

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.