Detection of cardiac arrhythmias from ECG signals using FBSE and Jaya optimized ensemble random subspace K-nearest neighbor algorithm

Abstract

Cardiac Arrhythmias is defined as the sudden and unexpected occurrence of a rhythm in the heart. Arrhythmia detection is prevalent in developing countries that require systems-based screening solutions for diagnosis. A new rhythm-based approach is proposed to screen the patients with cardiac arrhythmia at the primary level. This method eliminates multiple tests, enabling faster and more accurate diagnosis. This method first segments the electrocardiogram signals to create a complete picture of the single heartbeat. Then the Fourier–Bessel series expansion (FBSE) is used to transform the sequences of each heartbeat into more meaningful ones that can characterize the structural integrity of arrhythmia. The FBSE sequence series are trained using the Jaya optimized ensemble random subspace KNN (JO-ERSKNN) model with 10-fold cross-validation for classifying five types of cardiac arrhythmia beats. We have achieved an accuracy of 99.49%, a sensitivity of 95.43%, and specificity of 99.48%. The results demonstrate that the proposed algorithm can detect differences in the five types of cardiac arrhythmia signals. It can also be utilized as a screening tool for detecting arrhythmia and can be made compatible with various wearable devices.