Abstract
Embedding the original high dimensional data in a low dimensional space helps to overcome the curse of dimensionality and removes noise. The aim of this work is to evaluate the performance of three different linear dimensionality reduction techniques (DR) techniques namely principal component analysis (PCA), multi dimensional scaling (MDS) and linear discriminant analysis (LDA) on classification of cardiac arrhythmias using probabilistic neural network classifier (PNN). The design phase of 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 linear DR techniques specified, and arrhythmia classification using PNN. Linear dimensionality reduction techniques have simple geometric representations and simple computational properties. Entire MIT-BIH arrhythmia database is used for experimentation. The experimental results demonstrates that combination of PNN classifier (spread parameter, σ = 0.08) and PCA DR technique exhibits highest sensitivity and F score of 78.84% and 78.82% respectively with a minimum of 8 dimensions.
Highlights
Electrocardiogram (ECG) is the most commonly used tool for analysis in the field of cardiology
The objective of this work is to evaluate the performance of three different linear dimensionality reduction techniques in classification of ECG arrhythmias
Classifier performance is evaluated by using various linear dimensionality reduction techniques with different dimensions of data representations
Summary
Electrocardiogram (ECG) is the most commonly used tool for analysis in the field of cardiology. Recording ECG for a longer duration of time is simple whereas analyzing it every beat is a tedious process. ECG is a non-stationary signal which means whenever the heart rate is abnormal, the wave form shows irregularities. The irregularities are non-periodic in nature and manual observation takes a longer time. How to cite this paper: Rajagopal, R. and Ranganathan, V. (2016) Critical Evaluation of Linear Dimensionality Reduction Techniques for Cardiac Arrhythmia Classification.
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