Body movement activity recognition for ambulatory cardiac monitoring
Body movement activity recognition for ambulatory cardiac monitoring
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DOI：
10.1109/tbme.2006.889186
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发表时间：
2007-05-01
影响因子：
4.6
通讯作者：
Duttagupta, Siddhartha P.
中科院分区：
文献类型：
--
作者：
Pawar, Tanmay;Chaudhuri, Subhasis;Duttagupta, Siddhartha P.
可穿戴式心电图（W-ECG）记录仪越来越多地被患有心脏异常的人使用，他们也选择了积极的生活方式。目前的挑战是心电信号受到佩戴者身体运动活动（BMA）引起的运动伪影的影响。通常的做法是开发有效的过滤算法来消除伪影。相反，我们的目标是检测运动伪影，并从ECG信号本身对BMA的类型进行分类。我们用单导联系统记录了指定bma期间的心电图信号，如静坐、行走、手臂运动和爬楼梯等。在BMA期间收集的心电信号被认为是由于心脏活动，运动伪影和传感器噪声引起的信号的加性混合。通过对相应的心电数据进行特征分解来表征一类特定的BMA。基于该技术，根据bma的唯一性，对不同类别组合的分类准确率在70% ~ 98%之间。上述分类也适用于分析有BMA存在的P波和T波。
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.