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|Title:||Transition detection in body movement activities for wearable ECG|
Principal Component Analysis
|Citation:||IEEE Transactions on Biomedical Engineering 54(6 Part 2), 1149-52|
|Abstract:||It has been shown by Pawar (2007) that the motion artifacts induced by body movement activity (BMA) in a single-lead wearable electrocardiogram (ECG) signal recorder, while monitoring an ambulatory patient, can be detected and removed by using a principal component analysis (PCA)-based classification technique. However, this requires the ECG signal to be temporally segmented so that each segment comprises of artifacts due to a single type of body movement activity. In this paper, we propose a simple, recursively updated PCA-based technique to detect transitions wherever the type of body movement is changed.|
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