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

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Abstract

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.

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Acknowledgments

This study is supported by Zhejiang Provincial Key Laboratory of New Network Standards and Technologies (NNST)(No.2013E10012). The authors would like to thank the editor and reviewers for improving the quality of the paper.

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Authors and Affiliations

  1. School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Zhejiang, 310018, Hangzhou, China

    Jing Huang & Lifeng Ren

  2. School of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China

    Zhiwei Ji

  3. Department of the Built Environment, College of Design and Engineering, National University of Singapore, Singapore, 117566, Singapore

    Ke Yan

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  1. Jing Huang
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Correspondence to Ke Yan.

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Huang, J., Ren, L., Ji, Z. et al. Single-channel EEG automatic sleep staging based on transition optimized HMM. Multimed Tools Appl 81, 43063–43081 (2022). https://doi.org/10.1007/s11042-022-12551-6

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