Leutheuser Heike, Gradl Stefan, Kugler Patrick, Anneken Lars, Arnold Martin, Achenbach Stephan, Eskofier Bjoern M
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2690-3. doi: 10.1109/EMBC.2014.6944177.
The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.
心电图（ECG）是心脏病诊断的关键工具，可用于检测缺血、心律失常及其他病症。对心电图信号进行自动、低成本监测，可在出现症状时提供即时分析，并可能促使患者前往急诊科就诊。目前，由于移动设备（智能手机、平板电脑）已成为日常生活中不可或缺的一部分，它们可为心电图信号的自动、低成本监测解决方案提供理想基础。在这项工作中，我们旨在构建一个能够在基于安卓的移动设备上运行的心律失常实时分类检测系统。我们的分析基于70%的麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库和70%的麻省理工学院 - 贝斯以色列女执事医疗中心室上性心律失常数据库。其余30%留作最终评估使用。我们使用QRS检测算法检测R波峰，并基于检测到的R波峰计算了16个特征（统计特征、心跳特征和基于模板的特征）。利用这些特征和四个不同特征子集，我们使用嵌入式分类软件工具箱（ECST）训练了8个分类器，并比较了每个分类决策的计算成本和每个分类器的内存需求。我们得出结论，对于我们的二分类问题（区分正常和异常心跳），C4.5分类器表现最佳，准确率为91.6%。该分类器仍需进行详细的特征选择评估。我们接下来的步骤是在基于安卓的移动设备上实现C4.5分类器，并使用两个已使用数据库中剩余的30%对最终系统进行评估。
