Heart Rate Variability Classification and Feature Extraction Using Support Vector Machine and PCA: An Overview

In today’s era Heart Rate Variability becomes an important characteristic to determine the condition of heart. That’s why the calculation of HRV and classification to generate rules is necessary. Human Heart Generates the electrical signal. ECG is used to detect the heart beat. ECG signal contains lots of noise. To classify the signals first to decompose the signals using wavelet transform. Many Mother wavelet are used to denoise the signals. Support Vector Machine is used to classify the denoise signal and recognize pattern for better classification of ECG signal. Various methods have been done using different classification tools like Neural Network, Support Vector Machine, and Wavelet transform. Among them Support Vector Machine is very successful in many research areas such as pattern recognition, bioinformatics, etc. This paper gives Brief Survey on Support Vector Machine and Combination of Wavelet Transform & PCA for better Feature Extraction of ECG signals Keyword Classification, ECG, HRV, Kernel SVM, PCA, SVM, Wavelet Transform