PCA and ICA Based Hybrid Dimension Reduction Model for Cardiac Arrhythmia Disease Diagnosis

Abstract

An arrhythmia is a fluctuation in the continuous beat of the heart (i.e., anomalous rhythm). Arrhythmia is considered a hazardous disease causing genuine medical problems in patients, when left untreated. For saving lives, early diagnosis of arrhythmias would be very conducive. The P-QRS-T wave of the Electrocardiogram (ECG) signal illustrates the cardiac function. However, it is a tough task to extract the discriminant information from a large number of data of ECG signal. In this perspective, this study exhibits a novel approach for diagnosing diseases related to cardiac arrhythmia. In this proposed model, a hybrid dimension reduction model including Independent and Principal Component Analysis (ICA, PCA) are introduced and machine learning features are extracted for disease diagnosis. The original ECG data are splitted into several windows and consider as input of dimension reduction process. After completing the ICA and PCA process, the different components of ICA and PCA are used for feature extraction. Finally, the Multi-Class Support Vector Machine (MCSVM) is used for training and identifying the disease. For evaluating the proposed method, MIT-BIH dataset is used. According to the experiment, the proposed model shows better classification accuracy using the first components of ICA and PCA algorithms, which is 98.67%.