Lin Yin-Yan, Wu Hau-Tieng, Hsu Chi-An, Huang Po-Chiun, Huang Yuan-Hao, Lo Yu-Lun
IEEE J Biomed Health Inform. 2016 Nov;21(6):1533-1545. doi: 10.1109/JBHI.2016.2636778. Epub 2016 Dec 7.
Physiologically, the thoracic (THO) and abdominal (ABD) movement signals, captured using wearable piezo-electric bands, provide information about various types of apnea, including central sleep apnea (CSA) and obstructive sleep apnea (OSA). However, the use of piezo-electric wearables in detecting sleep apnea events has been seldom explored in the literature. This study explored the possibility of identifying sleep apnea events, including OSA and CSA, by solely analyzing one or both the THO and ABD signals. An adaptive non-harmonic model was introduced to model the THO and ABD signals, which allows us to design features for sleep apnea events. To confirm the suitability of the extracted features, a support vector machine was applied to classify three categories - normal and hypopnea, OSA, and CSA. According to a database of 34 subjects, the overall classification accuracies were on average 75.9%±11.7% and 73.8%±4.4%, respectively, based on the cross validation. When the features determined from the THO and ABD signals were combined, the overall classification accuracy became 81.8%±9.4%. These features were applied for designing a state machine for online apnea event detection. Two event-byevent accuracy indices, S and I, were proposed for evaluating the performance of the state machine. For the same database, the S index was 84.01%±9.06%, and the I index was 77.21%±19.01%. The results indicate the considerable potential of applying the proposed algorithm to clinical examinations for both screening and homecare purposes.
从生理角度来看，使用可穿戴式压电带采集的胸部（THO）和腹部（ABD）运动信号可提供有关各种类型呼吸暂停的信息，包括中枢性睡眠呼吸暂停（CSA）和阻塞性睡眠呼吸暂停（OSA）。然而，文献中很少探讨使用压电可穿戴设备检测睡眠呼吸暂停事件。本研究探索了仅通过分析THO和ABD信号中的一个或两个来识别睡眠呼吸暂停事件（包括OSA和CSA）的可能性。引入了一种自适应非谐波模型来对THO和ABD信号进行建模，这使我们能够设计用于睡眠呼吸暂停事件的特征。为了确认提取特征的适用性，应用支持向量机对三类进行分类——正常和呼吸不足、OSA和CSA。根据一个包含34名受试者的数据库，基于交叉验证，总体分类准确率平均分别为75.9%±11.7%和73.8%±4.4%。当将从THO和ABD信号确定的特征组合时，总体分类准确率变为81.8%±9.4%。这些特征被用于设计一个用于在线呼吸暂停事件检测的状态机。提出了两个逐事件准确率指标S和I来评估状态机的性能。对于同一个数据库，S指标为84.01%±9.06%，I指标为77.21%±19.01%。结果表明，将所提出的算法应用于临床检查以进行筛查和家庭护理具有相当大的潜力。
