Title :
ECG Modeling and QRS Detection using Principal Component Analysis
Author :
Chawla, M.P.S. ; Verma, H.K. ; Kumar, Vinod
Author_Institution :
Department of Electrical Engineering, Indian Institute of Technology, Roorkee 247667 (India). mpschawla@rediffmail.com
Abstract :
Principal Component Analysis (PCA) is used to arrange the redundantly distributed information on functional activity from several biomedical signals. A modeling procedure for ECG using PCA has been suggested in this paper. Eigenvectors ar e created which form a new orthogonal basis for finding various segments of an ECG waveform. The simulation results are obtained using FFT (Fast fourier transform) and PCA. The concept of "largest variance" in PCA is chosen so as to extract the QRS complex portion only and to exclude P-wave and T-wave. Normalization of the ECG data to zero mean and unit variance can considerably improve the results of visualization of various segments of an ECG waveform. Visualization plots or scree plots showed that there was good segments separation of the considered ECG waveform.
Keywords :
Covariance; Electrocardiogram; Principal component analysis; QRS complex; Shape-space;
Conference_Titel :
Advances in Medical, Signal and Information Processing, 2006. MEDSIP 2006. IET 3rd International Conference On
Conference_Location :
Glasgow, UK
Print_ISBN :
978-0-86341-658-3