The proposed signal processing algorithms can detect non-physiological artifacts in polysomnographic EEG, potentially improving the accuracy of sleep disorder diagnosis.
Polysomnograhy EEG is mainly used to diagnose sleep disorders and related diseases. Hence, for effective analysis or diagnosis using the polysomnographic EEG, the acquired signal must be artifact free. This paper presents conventional signal processing techniques to detect the non-physiological artifacts in EEG signal like flat lines (FL), baseline wandering (BW), movement artifacts (MA), 50Hz/60Hz power line interference (PLI) and abrupt slopes (AS) due to electrode pop or cardiac pacemakers in the subject body. The proposed algorithms use the turning point calculation algorithm considering the amplitude and frequency variation of the sleep stages of the polysomnographic EEG. The algorithms are validated using the polysomnography EEG dataset available in the Physionet.
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Dora et al. (2017) studied this question.
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