Quantitative sleep EEG synchronization analysis for automatic arousals detection

• Published in: Biomedical signal processing and control Vol. 59; p. 101895
• Main Authors: Erdamar, Aykut, Aksahin, Mehmet Feyzi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2020
• Subjects: Coherence spectrum; Daytime sleepiness; Electroencephalographic synchronization; Mutual information; The microstructure of sleep; Coherence spectrum; Daytime sleepiness; Electroencephalographic synchronization; The microstructure of sleep; Mutual information
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2020.101895

[Display omitted] •A novel automatic arousal detection in sleep EEG.•Two EEG channels were used for synchronization.•The method is robust versus inter-scorer variability.•The method does not need any meta-rules or some empirical threshold values.•The method works fast at the same accuracy and reproducibility. Electroencephalographic arousals are considered to be the main reason for the interruption of sleep and are visually examined by sleep physicians. Visual scoring of all-night recordings has inter-scorer variability which may lead to subjective results. Hence, we aimed to develop a novel automated method to detect arousals from two electroencephalographic channels in terms of the synchronic events of the right and left hemispheres. In the context of the occurrence of arousal pattern, the relationship between two synchronic C3-A2 and C4-A1 channels were quantified using by coherence spectrum and mutual information. The power and the ratio values of the sub-bands of the coherence spectrum were selected as the five features. Furthermore, the mutual information value was determined as the sixth feature. The automatic detection performance was evaluated using six features and machine learning techniques, on five different patients' whole-night electroencephalography recordings. The presented method does not include any signal conditioning, pre-processing steps, any manual involvement, meta-rule-based approaches, and some empirical thresholds. The significant increases were found in sub-bands of the coherence spectrum in case of arousal. Moreover, the mutual information of these channels was distinctive during the arousal state. Consequently, the overall accuracy, sensitivity, specificity, and PPV values were achieved as 99.5 %, 99.8 %, 99.6 %, and 99.3 %, respectively with using ensemble bagged tree. The novelty of the present study is the practical determination of the relationship between electroencephalographic synchronization and the occurrence of the arousals between the central regions of the right and left hemispheres.