Detection of ECG points using Principal component analysis (A Review paper)

Abstract: Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. It provides valuable information about the functional aspects of the heart and cardiovascular system. The objective of paper is to automatic detection of cardiac arrhythmias in ECG signal. Recently developed digital signal processing and pattern reorganization technique is used in this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the ECG signal consists of following stages: detection of QRS complex in ECG signal; feature extraction from detected QRS complexes; classification of beat suing extracted feature set from QRS complexes. In turn automatic classification of heartbeats represents the automatic detection of cardiac arrhythmias in ECG signal. Principal Component Analysis is used where lots of data, all very confusing, too many variables to consider exists, some of them are probably insignificant. PCA was invented in1901 by Karl Pearson. It has some basic assumptions i.e. Linearity, Large variances, the principal components are orthogonal. In this paper discussing the different method of detection of ECG using PCA & various methods. The QRS complex feature is extracted based on PCA. QRS complexes feature can be presented by four largest principle components and the PCA results can be used to cluster analysis efficiently.[1] _________________________________________________________________________________________