Single-channel EEG automatic sleep staging based on transition optimized HMM

Sleep staging is a key process for evaluating sleep quality and diagnosing somnipathy-related diseases. Psychologists are required to do the traditional sleep stages identification. The manual work by these experts is often time-consuming and error-prone. In order to improve the performance of such...

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Veröffentlicht in:Multimedia tools and applications 2022-12, Vol.81 (30), p.43063-43081
Hauptverfasser: Huang, Jing, Ren, Lifeng, Ji, Zhiwei, Yan, Ke
Format: Artikel
Sprache:eng
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Zusammenfassung:Sleep staging is a key process for evaluating sleep quality and diagnosing somnipathy-related diseases. Psychologists are required to do the traditional sleep stages identification. The manual work by these experts is often time-consuming and error-prone. In order to improve the performance of such a process, automatic Electroencephalography (EEG) signal analysis using machine learning approaches is often used. In this paper, a transitions-optimized Hidden Markov Model (HMM) model is proposed to improve the accuracy of prediction. Our proposed framework includes 4 key modules: feature extraction, feature selection, classification, and transition optimization. By applying, transition optimization process after a general GMM-HMM classification, our experimental results show a convincing improvement in the accuracy of classification.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-12551-6