A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG
Sleep stages are generally divided into three stages including Wake, REM and NRME. The standard sleep monitoring technology is Polysomnography (PSG). The inconvenience for PSG monitoring limits the usage of PSG in some areas. In this study, we developed a new method to classify sleep stage using ele...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2015-01-01
|
Series: | MATEC Web of Conferences |
Subjects: | |
Online Access: | http://dx.doi.org/10.1051/matecconf/20152205023 |
id |
doaj-3f6863b2040346acaa955fa7823c166a |
---|---|
record_format |
Article |
spelling |
doaj-3f6863b2040346acaa955fa7823c166a2021-04-02T10:03:07ZengEDP SciencesMATEC Web of Conferences2261-236X2015-01-01220502310.1051/matecconf/20152205023matecconf_iceta2015_05023A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMGLi Jie0Chen Hang1Ye Shuming2College of Biomedical Engineering & Instrument Science, Zhejiang UniversityKey lab of Biomedical Engineering of Ministry of Education, Zhejiang UniversityZhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effec-tiveness Appraisal HangzhouSleep stages are generally divided into three stages including Wake, REM and NRME. The standard sleep monitoring technology is Polysomnography (PSG). The inconvenience for PSG monitoring limits the usage of PSG in some areas. In this study, we developed a new method to classify sleep stage using electrooculogram (EOG) and electromyography (EMG) automatically. We extracted right and left EOG features and EMG feature in time domain, and classified them into strong, weak and none types through calculating self-adaptive threshold. Combination of the time features of EOG and EMG signals, we classified sleep stages into Wake, REM and NREM stages. The time domain features utilized in the method were Integrate Value, variance and energy. The experiment of 30 datasets showed a satisfactory result with the accuracy of 82.93% for Wake, NREM and REM stages classification, and the average accuracy of Wake stage classification was 83.29%, 82.11% for NREM stage and 76.73% for REM stage.http://dx.doi.org/10.1051/matecconf/20152205023sleep stage classificationself-adaptive thresholdEOGEMG |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Li Jie Chen Hang Ye Shuming |
spellingShingle |
Li Jie Chen Hang Ye Shuming A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG MATEC Web of Conferences sleep stage classification self-adaptive threshold EOG EMG |
author_facet |
Li Jie Chen Hang Ye Shuming |
author_sort |
Li Jie |
title |
A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG |
title_short |
A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG |
title_full |
A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG |
title_fullStr |
A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG |
title_full_unstemmed |
A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG |
title_sort |
self-adaptive threshold method for automatic sleep stage classification using eog and emg |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2015-01-01 |
description |
Sleep stages are generally divided into three stages including Wake, REM and NRME. The standard sleep monitoring technology is Polysomnography (PSG). The inconvenience for PSG monitoring limits the usage of PSG in some areas. In this study, we developed a new method to classify sleep stage using electrooculogram (EOG) and electromyography (EMG) automatically. We extracted right and left EOG features and EMG feature in time domain, and classified them into strong, weak and none types through calculating self-adaptive threshold. Combination of the time features of EOG and EMG signals, we classified sleep stages into Wake, REM and NREM stages. The time domain features utilized in the method were Integrate Value, variance and energy. The experiment of 30 datasets showed a satisfactory result with the accuracy of 82.93% for Wake, NREM and REM stages classification, and the average accuracy of Wake stage classification was 83.29%, 82.11% for NREM stage and 76.73% for REM stage. |
topic |
sleep stage classification self-adaptive threshold EOG EMG |
url |
http://dx.doi.org/10.1051/matecconf/20152205023 |
work_keys_str_mv |
AT lijie aselfadaptivethresholdmethodforautomaticsleepstageclassificationusingeogandemg AT chenhang aselfadaptivethresholdmethodforautomaticsleepstageclassificationusingeogandemg AT yeshuming aselfadaptivethresholdmethodforautomaticsleepstageclassificationusingeogandemg AT lijie selfadaptivethresholdmethodforautomaticsleepstageclassificationusingeogandemg AT chenhang selfadaptivethresholdmethodforautomaticsleepstageclassificationusingeogandemg AT yeshuming selfadaptivethresholdmethodforautomaticsleepstageclassificationusingeogandemg |
_version_ |
1724168059396030464 |