Transition detection in body movement activities for wearable ECG.
other · Level V
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Abstract
It has been shown by Pawar et al. (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.
Medical subject headings
- Algorithms
- Artifacts
- Diagnosis, Computer-Assisted
- Electrocardiography, Ambulatory
- Motor Activity
- Movement
- Pattern Recognition, Automated