ECG modeling and QRS detection using principal component analysis

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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 are 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 screen plots showed that there was good segments separation of the considered ECG waveform. (4 pages)

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