Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG
Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG
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DOI：
10.1109/titb.2010.2087386
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发表时间：
2011-05-01
影响因子：
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通讯作者：
Tamil, Lakshman
中科院分区：
文献类型：
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作者：
Bsoul, Majdi;Minn, Hlaing;Tamil, Lakshman
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，具有高度的准确性，适用于家庭和临床护理应用。全自动系统使用患者的单通道夜间ECG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。“呼吸暂停医疗辅助”在基于Android操作系统（OS）的智能手机上实现，使用一般成人受试者无关SVC模型或受试者相关SVC模型，并实现了90%的分类F-测量值和96%的受试者无关SVC灵敏度。实时能力来自于使用1分钟段的ECG时期的特征提取和分类。“呼吸暂停医疗辅助”的复杂性降低来自ECG处理的有效优化，以及通过降低ECG和ECG衍生呼吸信号的特征集的维度以及通过减少支持向量的数量来降低SVC模型复杂性的技术的使用。
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.