An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model

• Published in: Journal of neuroscience methods Vol. 324; p. 108320
• Main Authors: Ghimatgar, Hojat, Kazemi, Kamran, Helfroush, Mohammad Sadegh, Aarabi, Ardalan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.08.2019; Elsevier
• Subjects: EEG-based sleep stage scoring; Hidden Markov Model; Life Sciences; MGCACO feature selection; Random Forest; EEG-based sleep stage scoring; MGCACO feature selection; Hidden Markov Model; Random Forest
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2019.108320

•A single channel EEG sleep staging method is proposed based on HMM.•Feature selection using redundancy and relevance analyses improved the performance.•HMM reduced false positives by introducing the temporal structure of sleep stages.•HMM-based sleep stage classification method achieved promising results. Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis. In this approach, a set of optimal features was first selected from a pool of features extracted from sleep EEG epochs by using a feature selection method based on the relevance and redundancy analysis. EEG segments were then classified using a random forest classifier. Finally, a Hidden Markov Model (HMM) was used to reduce false positives by incorporating knowledge of the temporal structure of transitions between sleep stages. We evaluated the proposed method using single-channel EEG signals from four public sleep EEG datasets scored according to R&K and AASM guidelines. We compared the performance of our method with existing methods using different cross validation strategies. Using a leave-one-out validation strategy, our method achieved overall accuracies in the range of (79.4–87.4%) and (77.6–80.4%) with Kappa values in the range of 0.7–0.85 for six-stage (R&K) and five-stage (AASM) classification, respectively. Our method showed a reduction in overall accuracy up to 8% using the cross-dataset validation strategy in comparison with the subject cross-validation method. Our method outperformed the existing methods for all multi-stage classification. The proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.