用于动态心脏监护的体动活动识别

摘要(中文译文)

可穿戴心电图记录仪正越来越多地被心脏异常患者所使用，而这些患者同时也选择积极的生活方式。目前的挑战在于，心电信号会受到佩戴者身体运动活动所诱发的运动伪迹的影响。通常的做法是开发有效的滤波算法以消除伪迹。相反，我们的目标是从心电信号本身检测运动伪迹并对身体运动活动的类型进行分类。我们使用单导联系统记录了在特定身体运动活动期间的心电信号，例如静坐、行走、手臂运动和爬楼梯等。在身体运动活动期间采集的心电信号被假定为心脏活动信号、运动伪迹和传感器噪声的加性混合。通过对相应心电数据进行特征分解，可以表征某一特定类别的身体运动活动。基于该技术，根据各类身体运动活动的独特性，不同类别组合的分类准确率在70%至98%之间。上述分类对于在存在身体运动活动的情况下分析P波和T波也具有实用价值。

Abstract

Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a single-lead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA.

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