A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks
The electrocardiogram (ECG) is a non invasive tool used to diagnose various heart conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat classification plays a significant role in diagnosing life threatening cardiac arrhythmias. However, the ECG signal is very small, the anti interference potential is low, and the noise is easily influenced. Thus, clinicians face challenges in diagnosing arrhythmias. Thus, a method to automatically identify and distinguish arrhythmias from the ECG signal is invaluable. In this paper, a hybrid approach based on marine predators algorithm (MPA) and convolutional neural network (CNN) called MPA CNN is proposed to classify the non ectopic, ventricular ectopic, supraventricular ectopic, and fusion ECG types of arrhythmia. The proposed approach is a combination of heavy feature extraction and classification techniques; hence, outperforms other existing classification approaches. Optimal characteristics were derived directly from the raw signal to decrease the time required for and complexity of the computation. Precision levels of 99.31% 99.76% and 99.47% were achieved by the proposed approach on the MIT BIH,EDB, and INCART databases, respectively.
IEEE Access