Cho S, Lee J, Park H, Lee K
Department of Biomedical Engineering, College of Health Science, Yonsei University, South Korea. E-mail:
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2733-5. doi: 10.1109/IEMBS.2005.1617036.
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.
睡眠期间频繁觉醒会降低睡眠质量并导致睡眠片段化。通过目视检查生理信号来检测觉醒事件既不方便又耗时。本研究的目的是开发一种自动算法来检测觉醒事件。我们提出了一种基于时频分析和支持向量机（SVM）分类器的自动方法，使用单通道睡眠脑电图（EEG）来检测觉醒。我们使用9名患有睡眠呼吸暂停、打鼾和日间过度嗜睡（EDS）患者的多导睡眠图（PSG）记录评估了我们方法的性能。通过所提出的方法，我们分别获得了训练集的灵敏度为87.92%、特异性为95.56%，测试集的灵敏度为75.26%、特异性为93.08%。我们已经表明所提出的方法对于检测觉醒事件是有效的。
