Improving Twin Support Vector Machine Based on Hybrid Swarm Optimizer for Heartbeat Classification

Abstract

The computer-aided diagnosis system is used to reduce the high mortality rate among heart patients through detecting cardiac diseases at an early stage. Since the process of detecting the cardiac heartbeat is a hard task because of the human eye cannot be distinguished between the variations in electrocardiogram (ECG) signals due to they are very small. There are several machine learning approaches are applied to improve the performance of detecting the heartbeats, however, these methods suffer from some limitations such as high time computational and slow convergence. To avoid these limitations, this paper proposed an ECG heartbeat classification approach, called Swarm-TWSVM, that combined twin support vector machines (TWSVMs) with the hybrid between the particle swarm optimization with gravitational search algorithm (PSOGSA). Also, the empirical mode decomposition (EMD) has been applied for the ECG noise removing, and feature extraction, then PSOGSA was used to find the optimal parameters of TWSVM to improve the classification process. The experiments were performed using the MIT-BIH arrhythmia database and results show that the Swarm- TWSVM gives better accuracy than TWSVM 99.44 and 85.87%, respectively.