Heartbeat Classification using discrete wavelet transform and kernel principal component analysis

• Published in: 2013 IEEE TENCON Spring Conference pp. 34 - 38
• Main Authors: Shengkai Yang, Haibin Shen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2013
• Subjects: Accuracy; Discrete Wavelet Transform(DWT); Discrete wavelet transforms; Electrocardiogram(ECG); Electrocardiography; Feature extraction; Heart beat; Heartbeat classification; Kernel; Kernel Principal Component Analysis(KPCA); Multilayer Perceptron Neural Network(MLPNN); Principal component analysis
• ISBN: 1467363472; 9781467363471
• DOI: 10.1109/TENCONSpring.2013.6584412

In this paper, an automatic heartbeat Classification method based on discrete wavelet transform (DWT) and kernel principal component analysis (KPCA) is proposed. DWT is employed to extract time-frequency characteristics of heartbeats, and KPCA is utilized to extract a more complete nonlinear representation of the principal components. In addition, RR interval features are also adopted. A three-layer multilayer perceptron neural network (MLPNN) is used as a classifier. The MIT-BIH Arrhythmia Database was used as a test bench. In the "class-oriented" evaluation, the classification accuracy is 98.48%, which is comparable to previous works. In the "subject-oriented" evaluation, the classification accuracy is 92.34%. The Se (sensitivity) of class "S" and "V" is 62.0% and 84.4% respectively, and the P+ (positive predictive rate) of class "S" and "V" is 70.6% and 77.7% respectively. The results show an improvement on previous works. The proposed method suggested a better performance than the state-of-art method in real situation.