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Point out the mistakes : An HMM-based anomaly detection algorithm for sleep stage classification (2025)


Wang, Z., Liu, H., Cai, Y., Li, H., Yang, C., Zhang, X., & Cong, F. (2025). Point out the mistakes : An HMM-based anomaly detection algorithm for sleep stage classification. Biomedical Signal Processing and Control, 99, Article 106805. https://doi.org/10.1016/j.bspc.2024.106805


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatWang, Ziyi; Liu, Hang; Cai, Yukai; Li, Hongjin; Yang, Chuanshuai; Zhang, Xinlei; Cong, Fengyu

Lehti tai sarjaBiomedical Signal Processing and Control

ISSN1746-8094

eISSN1746-8108

Julkaisuvuosi2025

Ilmestymispäivä19.09.2024

Volyymi99

Artikkelinumero106805

KustantajaElsevier

JulkaisumaaBritannia

Julkaisun kielienglanti

DOIhttps://doi.org/10.1016/j.bspc.2024.106805

Julkaisun avoin saatavuusEi avoin

Julkaisukanavan avoin saatavuus


Tiivistelmä

Accurate sleep stage scoring is essential for diagnosing sleep disorders. Current automated sleep staging methods often exhibit staging errors, which can be interpreted as anomalies. Detecting these anomalies is crucial for improving staging accuracy. Most existing approaches modify staging based on predefined conditions but lack effective methods for localizing and identifying anomalies. In this study, we propose an anomaly detection method utilizing the Hidden Markov Model (HMM), a time-series modeling technique, to detect anomalies in sleep staging results. Evaluating our approach with four classical models as pre-classifiers, we achieve anomaly detection precisions of 0.760, 0.577, 0.631, and 0.613. Assuming that all detected anomalies are corrected, the pseudo-accuracies improve to 0.964, 0.929, 0.950, and 0.929, respectively. Our results indicate that the proposed method significantly enhances stage recognition accuracy, especially for stage N1, which is critical for diagnosing sleep-related disorders. Notably, approximately 28.6% of epochs require reinterpretation by sleep technicians to achieve these improvements.


YSO-asiasanatneuroverkotunitutkimusanomaliatEEGaivotutkimus

Vapaat asiasanatautomatic sleep stage scoring; hidden markov model (HMM); deep neural network; anomaly detection


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Viimeisin päivitys 2024-14-10 klo 14:57