Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification

Automation in cardiac arrhythmia classification helps medical professionals to make accurate decisions upon the patient's health. The aim of this work is to evaluate the performance of five different linear and nonlinear unsupervised dimensionality reduction (DR) techniques namely principal component analysis (PCA), fast independent component analysis (fastICA) with tangential, kurtosis and Gaussian contrast functions, kernel PCA (KPCA) with polynomial kernel, hierarchical nonlinear PCA (hNLPCA) and principal polynomial analysis (PPA) on classification of cardiac arrhythmias using probabilistic neural network classifier (PNN). The design phase of the classification model comprises of the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, dimensionality reduction through unsupervised DR techniques, and arrhythmia classification using PNN. PCA is a widely used DR technique for mapping high dimensional data to its low dimensional representation. But real world data like electrocardiogram (ECG) signals are complex and nonlinear in nature. This work concentrates on performance analysis of four nonlinear DR techniques and conventional linear PCA technique on classification of cardiac arrhythmias. Entire MIT-BIH arrhythmia database is used for experimentation. The experimental results demonstrate that the combination of PNN classifier (at spread parameter, σ = 0.4) and fastICA DR technique with tangential contrast function exhibit highest F score of 99.83% with a minimum of 10 dimensions. hNLPCA and KPCA requires more computation time for low dimensional mapping. PPA performs about 10% better than PCA and serves intermediate between linear and nonlinear techniques.