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

doi:10.1007/978-3-319-04573-3_109

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

Institute of Medical Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013
Editors	Cheng-Yi Chen, Cheng-Fu Yang, Jengnan Juang
Publisher	Springer Verlag
Pages	891-898
Number of pages	8
ISBN (Electronic)	9783319045726
DOIs	https://doi.org/10.1007/978-3-319-04573-3_109
Publication status	Published - 2014
Event	2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013 - Kaohsiung, Taiwan Duration: 2013 Dec 12 → 2013 Dec 14

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	293
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Other

Other	2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013
Country/Territory	Taiwan
City	Kaohsiung
Period	13-12-12 → 13-12-14

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-3-319-04573-3_109

Cite this

Liang, S. F., Chen, C. F., Zeng, J. H., & Pan, S. T. (2014). Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging. In C-Y. Chen, C-F. Yang, & J. Juang (Eds.), Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013 (pp. 891-898). (Lecture Notes in Electrical Engineering; Vol. 293). Springer Verlag. https://doi.org/10.1007/978-3-319-04573-3_109

Liang, Sheng Fu ; Chen, Ching Fa ; Zeng, Jian Hong et al. / Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging. Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013. editor / Cheng-Yi Chen ; Cheng-Fu Yang ; Jengnan Juang. Springer Verlag, 2014. pp. 891-898 (Lecture Notes in Electrical Engineering).

@inproceedings{96907a16a88d4173800862356c6c9212,

title = "Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging",

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.",

author = "Liang, {Sheng Fu} and Chen, {Ching Fa} and Zeng, {Jian Hong} and Pan, {Shing Tai}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013 ; Conference date: 12-12-2013 Through 14-12-2013",

year = "2014",

doi = "10.1007/978-3-319-04573-3_109",

language = "English",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "891--898",

editor = "Cheng-Yi Chen and Cheng-Fu Yang and Jengnan Juang",

booktitle = "Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013",

address = "Germany",

}

Liang, SF, Chen, CF, Zeng, JH & Pan, ST 2014, Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging. in C-Y Chen, C-F Yang & J Juang (eds), Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013. Lecture Notes in Electrical Engineering, vol. 293, Springer Verlag, pp. 891-898, 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013, Kaohsiung, Taiwan, 13-12-12. https://doi.org/10.1007/978-3-319-04573-3_109

Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging. / Liang, Sheng Fu; Chen, Ching Fa; Zeng, Jian Hong et al.
Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013. ed. / Cheng-Yi Chen; Cheng-Fu Yang; Jengnan Juang. Springer Verlag, 2014. p. 891-898 (Lecture Notes in Electrical Engineering; Vol. 293).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging

AU - Liang, Sheng Fu

AU - Chen, Ching Fa

AU - Zeng, Jian Hong

AU - Pan, Shing Tai

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84928233989&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84928233989&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-04573-3_109

DO - 10.1007/978-3-319-04573-3_109

M3 - Conference contribution

AN - SCOPUS:84928233989

T3 - Lecture Notes in Electrical Engineering

SP - 891

EP - 898

BT - Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013

A2 - Chen, Cheng-Yi

A2 - Yang, Cheng-Fu

A2 - Juang, Jengnan

PB - Springer Verlag

T2 - 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013

Y2 - 12 December 2013 through 14 December 2013

ER -

Liang SF, Chen CF, Zeng JH, Pan ST. Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging. In Chen C-Y, Yang C-F, Juang J, editors, Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013. Springer Verlag. 2014. p. 891-898. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-3-319-04573-3_109

Combination of genetic algorithm and hidden markov model for EEG-based automatic sleep staging

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this