Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules

摘要

The classification of sleep-wake stages suffers from poor standardization in scoring criteria and heterogeneous conditioning of polysomnographic signals. To improve applicability of fully automated sleep staging, we have designed a formal classification framework to rigorously (1) select robust candidate features, (2) emulate artificial neural network classifiers, and (3) assign sleep-wake stages using flexible decision rules. An extensive database of 48 PSG records scored in 20 s epochs by two independent clinicians was used. A small subset of 2 s elementary epochs representative of each stages with unequivocal expert scores was selected to form a limited set of learning exemplars. From 16 statistical, spectral and non-linear candidate features extracted in 2 s epochs from EEG and EMG signals, a sequential forward search selected an optimal set of five features with a 22% error rate. Multiple layer perceptions were trained from this optimal feature set while classification accuracy was assessed using the unequivocal instance subset. A simple majority vote among 10 consecutive classifier outputs ensured a final scoring resolution comparable to that of the experts. Poor classification performance was obtained for movement time, wakefulness, and intermediate sleep stages with a 36±15% error rate (Cohen's kappa 0.48±0.18). In contrast, deep and paradoxical sleep was classified with an 82% accuracy not far from inter-expert expert agreement (83 ±3%). Significant improvements should be expected using a larger learning set compensating for a high inter-individual variability, and decision rules incorporating more domain-knowledge.