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Combination of Genetic Algorithm and Hidden Markov Model for EEG-Based Automatic Sleep Staging

  • Sheng-Fu Liang
  • Ching-Fa Chen
  • Jian-Hong Zeng
  • Shing-Tai Pan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 293)

Abstract

In this paper, we propose a strategy that combines Genetic Algorithm (GA) and HMM to improve the recognition rate of sleep staging. The GA is used to train a better codebook for HMM. With this method, the accuracy and efficiency of sleep medical diagnosis can be expected to be improved. Moreover, some features used in other research are selected as supporting features. These features are used to train the HMM model and then fed into the trained HMM for recognition. Unlike the existing research on sleep staging by HMM, in which the modeling of HMM is independent of the special properties of the sleep stage transition, the HMM in this study is adjusted to meet these properties. The experimental results show that the proposed method greatly enhances the recognition rate compared with other existing research.

Keywords

Sleep staging Genetic algorithm (GA) Discrete hidden Markov model (DHMM) Electroencephalogram (EEG) 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sheng-Fu Liang
    • 1
  • Ching-Fa Chen
    • 2
  • Jian-Hong Zeng
    • 3
  • Shing-Tai Pan
    • 3
  1. 1.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainan CityTaiwan, Republic of China
  2. 2.Department of Electronic EngineeringKao Yuan UniversityKaohsiungTaiwan, Republic of China
  3. 3.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan, Republic of China

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