Point out the mistakes: An HMM-based anomaly detection algorithm for sleep stage classification

Ziyi Wang, Hang Liu, Yukai Cai, Hongjin Li, Chuanshuai Yang, Xinlei Zhang, Fengyu Cong

Abstract: Highlights•A new HMM-based anomaly detection method is proposed for sleep stage classification.•Our proposed method fills the gap in post-processing of sleep staging tasks.•About 28.6% of epochs need reinterpreting by sleep technicians for improvements.