An Approach of Sleep Stage Classification Based on Time-frequency Analysis and Random Forest on a Single Channel

DOI

10.2991/acaai-18.2018.49How to use a DOI?

Keywords

sleep stage classification; EEG; time-frequency analysis; Random Forest

Abstract

An approach of sleep stage classification based on Time-Frequency analysis and Random Forest (TFRF) on a single channel is presented in the paper. Before classifying sleep stages, representative features are extracted by feature extraction method, such as FFT in the frequency domain of EEG signal and Hilbert transform in time domain, to reduce dimensionality and channel numbers. Other parameters are also used, e.g. Hjorth parameters. Then a Random Forest is trained by classifying the sleep stages. The TFRF is evaluated by means of Dreams database provided by University of MONS - TCTS Laboratory. The new standard of the American Academy of Sleep Medicine about the sleep stage classification is obeyed. 16 objects are considered as the training samples, while the other 4 objects are regarded as the test samples. A best result of 90.4% sensitivity is got with the signals from only one channel. Its application prospect is very extensive.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Siyuan Bi
AU  - Qingmin Liao
AU  - Zongqing Lu
PY  - 2018/03
DA  - 2018/03
TI  - An Approach of Sleep Stage Classification Based on Time-frequency Analysis and Random Forest on a Single Channel
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 209
EP  - 213
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.49
DO  - 10.2991/acaai-18.2018.49
ID  - Bi2018/03
ER  -