The Heartbeat Classification Based on PCA

Abstract:

Classify the electrocardiogram (ECG) into different pathophysiological categories is a complex pattern recognition task which has been tried in lots of methods. This paper will discuss a method of principal component analysis (PCA) in exacting the heartbeat features, and a new method of classification that is to calculate the error between the testing heartbeat and reconstructed heartbeat. Training and testing heartbeat is taken from the MIT-BIH Arrhythmia Database, in which 8 types of arrhythmia signals are selected in this paper. The true positive rate (TPR) is 83%.

• Periodical: Applied Mechanics and Materials (Volumes 195-196)
• Pages: 402-406
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.195-196.402
• Online since: August 2012
• Authors: Xue Qin Chen, Rui Ping Wang
• Keywords: Arrhythmia, Classification, ECG, Feature Vector, Principal Component Analysis (PCA)

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