Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier.

This paper seeks to solve the classification problem of cardiac arrhythmias by using a small number of electrocardiogram (ECG) recordings. To offer a reasonable solution to this problem, a technique that combines a common matrix approach (CMA)-based classifier model with the Fourier decomposition method (FDM) is proposed. The FDM is responsible for generating time-frequency (T-F) representations of ECG recordings. The classification process is performed with feature images applied as input to the classifier model. The feature images are obtained after two-dimensional principal component analysis (2DPCA) of data matrices related to ECG recordings. Each data matrix is created by concatenating the ECG record itself, the Fourier transform, and the T-F representation on a single matrix. To verify the efficacy of the proposed method, various experiments are conducted with the MIT-BIH, Chapman, and PTB-XL databases. In the assessments using the MIT-BIH database under the inter-patient paradigm, we achieved a mean overall accuracy rate of 99.81%. The proposed method outperforms the majority of recent efforts, yielding rates exceeding 99% on nearly five performance metrics for the recognition of V- and S-class arrhythmias. It is found that, in the classification of four types of arrhythmias using ECG recordings from the Chapman database, our model surpasses recent works by reaching mean overall accuracy rates of 99.76% and 99.45% for the raw and de-noised ECG recordings, respectively. Similarly, five different forms of arrhythmias from the PTB-XL database were recognized with a mean overall accuracy of 98.71%.