Bsoul Majdi, Minn Hlaing, Tamil Lakshman
Alcatel-Lucent, Plano, TX 75075, USA.
IEEE Trans Inf Technol Biomed. 2011 May;15(3):416-27. doi: 10.1109/TITB.2010.2087386. Epub 2010 Oct 14.
We have developed a low-cost, real-time sleep apnea monitoring system ''Apnea MedAssist" for recognizing obstructive sleep apnea episodes with a high degree of accuracy for both home and clinical care applications. The fully automated system uses patient's single channel nocturnal ECG to extract feature sets, and uses the support vector classifier (SVC) to detect apnea episodes. "Apnea MedAssist" is implemented on Android operating system (OS) based smartphones, uses either the general adult subject-independent SVC model or subject-dependent SVC model, and achieves a classification F-measure of 90% and a sensitivity of 96% for the subject-independent SVC. The real-time capability comes from the use of 1-min segments of ECG epochs for feature extraction and classification. The reduced complexity of "Apnea MedAssist" comes from efficient optimization of the ECG processing, and use of techniques to reduce SVC model complexity by reducing the dimension of feature set from ECG and ECG-derived respiration signals and by reducing the number of support vectors.
我们开发了一种低成本的实时睡眠呼吸暂停监测系统“Apnea MedAssist”，用于识别阻塞性睡眠呼吸暂停发作，在家庭和临床护理应用中均具有高度准确性。该全自动系统利用患者的单通道夜间心电图来提取特征集，并使用支持向量分类器（SVC）来检测呼吸暂停发作。“Apnea MedAssist”在基于安卓操作系统（OS）的智能手机上实现，使用通用的成人独立于个体的SVC模型或个体依赖的SVC模型，对于独立于个体的SVC，实现了90%的分类F值和96%的灵敏度。实时能力来自于使用1分钟的心电图时段进行特征提取和分类。“Apnea MedAssist”降低的复杂度来自于心电图处理的高效优化，以及通过减少心电图和心电图衍生呼吸信号的特征集维度以及减少支持向量数量来降低SVC模型复杂度的技术。
