TY - GEN
T1 - Sleep stage classification of sleep apnea patients using decision-tree-based support vector machines based on ECG parameters
AU - Wang, Jeen Shing
AU - Shih, Guan Rong
AU - Chiang, Wei Chun
PY - 2012/7/30
Y1 - 2012/7/30
N2 - This paper describes the design and validation of an effective sleep stage classification strategy for patients with sleep apnea. This strategy consists of a sequential forward selection (SFS) feature selection method and a decision-tree-based support vector machines (DTB-SVM) classifier for discriminating three types of sleep based on electrocardiogram (ECG) signals. Each 5-minute epoch of ECG signal data collected during sleep was used to generate 24 features using heart rate variability (HRV) analysis. An SFS feature selection method was then employed to determine which significant features should be selected to improve classification accuracy. A DTB-SVM was then trained using selected features in order to discriminate three sleep stages, including pre-sleep wakefulness, NREM sleep and REM sleep. The average classification accuracy of the proposed strategy was 73.51%. Our experimental results demonstrate that the proposed strategy provides moderate accuracy for detecting sleep stages in sleep apnea patients and can serve as a convenient tool for assessing sleep quality.
AB - This paper describes the design and validation of an effective sleep stage classification strategy for patients with sleep apnea. This strategy consists of a sequential forward selection (SFS) feature selection method and a decision-tree-based support vector machines (DTB-SVM) classifier for discriminating three types of sleep based on electrocardiogram (ECG) signals. Each 5-minute epoch of ECG signal data collected during sleep was used to generate 24 features using heart rate variability (HRV) analysis. An SFS feature selection method was then employed to determine which significant features should be selected to improve classification accuracy. A DTB-SVM was then trained using selected features in order to discriminate three sleep stages, including pre-sleep wakefulness, NREM sleep and REM sleep. The average classification accuracy of the proposed strategy was 73.51%. Our experimental results demonstrate that the proposed strategy provides moderate accuracy for detecting sleep stages in sleep apnea patients and can serve as a convenient tool for assessing sleep quality.
UR - http://www.scopus.com/inward/record.url?scp=84864256519&partnerID=8YFLogxK
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U2 - 10.1109/BHI.2012.6211567
DO - 10.1109/BHI.2012.6211567
M3 - Conference contribution
AN - SCOPUS:84864256519
SN - 9781457721779
T3 - Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012
SP - 285
EP - 288
BT - Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics
T2 - IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering
Y2 - 2 January 2012 through 7 January 2012
ER -