title: Transition detection in body movement activities for wearable ECG.
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.
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology-Bombay, Mumbai 400076, India. tanmaypawar@iitb.ac.in
authors.forename: Tanmay
authors.initials: T
authors.lastname: Pawar
authors.forename: N S
authors.initials: NS
authors.lastname: Anantakrishnan
authors.forename: Subhasis
authors.initials: S
authors.lastname: Chaudhuri
authors.forename: Siddhartha P
authors.initials: SP
authors.lastname: Duttagupta
mesh_terms: 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