@inproceedings{6c20fd9b0003420fa843ca42712367dc,
title = "Detection of arousals in patients with respiratory sleep disorders using a single channel EEG",
abstract = "Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is inconvenient and time-consuming work. The purpose of this study was to develop an automatic algorithm to detect the arousal events. We proposed the automatic method to detect arousals based on time-frequency analysis and the support vector machine (SVM) classifier using a single channel sleep electroencephalogram (EEG). The performance of our method has been assessed using polysomnographic (PSG) recordings of nine patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). By the proposed method, we could obtain sensitivity of 87.92% and specificity of 95.56% for the training sets, and sensitivity of 75.26% and specificity of 93.08% for the testing sets, respectively. We have shown that proposed method was effective for detecting the arousal events.",
keywords = "Arousals, EEG, PSG, Sleep fragmentation, Support vector machine, Time-frequency analysis",
author = "Cho, {S. P.} and J. Lee and Park, {H. D.} and Lee, {K. J.}",
year = "2005",
doi = "10.1109/iembs.2005.1617036",
language = "English (US)",
isbn = "0780387406",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2733--2735",
booktitle = "Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005",
note = "2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 ; Conference date: 01-09-2005 Through 04-09-2005",
}