Nano Marina-Marinela, Werth Jan, Aarts Ronald M, Heusdens Richard
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7679-82. doi: 10.1109/EMBC.2015.7320171.
Sleep apnea is a sleep disorder distinguished by repetitive absence of breathing. Compared with the traditional expensive and cumbersome methods, sleep apnea diagnosis or screening with physiological information that can be easily acquired is needed. This paper describes algorithms using heart rate variability (HRV) to automatically detect sleep apneas as long as it can be easily acquired with unobtrusive sensors. Because the changes in cardiac activity are usually hysteretic than the presence of apneas with a few minutes, we propose to use the delayed HRV features to identify the episodes with sleep apneic events. This is expected to help improve the apnea detection performance. Experiments were conducted with a data set of 23 sleep apnea patients using support vector machine (SVM) classifiers and cross validations. Results show that using eleven HRV features with a time delay of 1.5 minutes rather than the features without time delay for SA detection, the overall accuracy increased from 74.9% to 76.2% and the Cohen's Kappa coefficient increased from 0.49 to 0.52. Further, an accuracy of 94.5% and a Kappa of 0.89 were achieved when applying subject-specific classifiers.
睡眠呼吸暂停是一种以反复呼吸停止为特征的睡眠障碍。与传统的昂贵且繁琐的方法相比，需要利用易于获取的生理信息来进行睡眠呼吸暂停的诊断或筛查。本文描述了使用心率变异性（HRV）的算法，只要能通过非侵入式传感器轻松获取相关信息，就能自动检测睡眠呼吸暂停。由于心脏活动的变化通常比呼吸暂停的出现滞后几分钟，我们建议使用延迟的HRV特征来识别睡眠呼吸暂停事件的发作。这有望有助于提高呼吸暂停检测性能。使用支持向量机（SVM）分类器和交叉验证对23名睡眠呼吸暂停患者的数据集进行了实验。结果表明，对于睡眠呼吸暂停检测，使用延迟1.5分钟的11个HRV特征而非无时间延迟的特征，总体准确率从74.9%提高到76.2%，科恩卡帕系数从0.49提高到0.52。此外，应用个体特异性分类器时，准确率达到了94.5%，卡帕系数为0.89。
