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Abstract: An automatic method was developed for detecting slow wave sleep (SWS). The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. Synchronous electroencephalographic (EEG) activity was detected by calculating cross-correlation between the two EOG channels by using 0.5–6Hz band. An amplitude criterion was used for detecting slow waves and beta power 18–30Hz was used to exclude artefacts. The automatic scoring was compared to a standard visual sleep scoring based on EOG, central EEG and submental EMG. Sleep EEG and EOG were recorded from 265 subjects. The optimal cross-correlation, amplitude and beta thresholds were derived using data from 133 training subjects and then applied to the data from different 132 validation subjects. Results were most sensitive to the changes in the amplitude criteria. Cohen''s Kappa between the visual and the new developed automatic scoring in separating non-SWS and SWS was substantial (0.70) with epoch-by-epoch agreement of 93%. SWS epoch detection sensitivity was 75% and specificity was 96%. Also the total amount of slow waves, slow wave time (SWT), was estimated. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive electrodes. [Copyright &y& Elsevier]

Abstract: In this work, topographic differences in computational sleep depth between healthy controls and obstructive sleep apnoea syndrome (OSAS) patients have been examined. Sleep depth estimation was based on continuous monitoring of the mean frequency of the EEG. During the experiments, all-night sleep EEG recordings of carefully age and gender matched sets of 16 healthy controls and 16 OSAS patients were compared on six electrode locations (Fp1-M2, Fp2-M1, C3-M2, C4-M1, O1-M2, and O2-M1). To optimise the diagnostic ability of the method, we examined the influence of 45 sets of adjustable analysis parameters on the ability of the method to show differences in computational sleep depth between the diagnostic groups. The results show clearly that although the visual scores for a set of epochs are the same for both clinical groups, computational sleep depth measure still shows deeper local sleep for healthy controls, both during NREM and REM sleep. Although the best achievable performance in different sleep stages is reached in different EEG derivations and with different parameter values, computation of sleep depth with 1-s output resolution in non-overlapping segments of 2s (400 samples) with maximum analysis band frequency of 20.5Hz and 51-point moving median smoothing on Fp2-M1 or O1-M2 leads to near-optimal performance in deep sleep or wakefulness/light sleep, respectively. [Copyright &y& Elsevier]