Transition detection in body movement activities for wearable ECG.
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.
Pawar
Tanmay
T
Department of Electrical Engineering, Indian Institute of Technology-Bombay, Mumbai 400076, India. tanmaypawar@iitb.ac.in
Anantakrishnan
N S
NS
Chaudhuri
Subhasis
S
Duttagupta
Siddhartha P
SP
D000328:Adult; D000465:Algorithms; D016477:Artifacts; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D009043:Motor Activity; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity