Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network.

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Cardiac arrhythmias pose a significant global health threat, contributing to increased mortality.
• Existing arrhythmia classification methods struggle with accuracy and automated monitoring.
• There is a critical need for advanced, accurate, and automated systems for arrhythmia detection.

Purpose of the Study:

• To propose an automated arrhythmia classification strategy leveraging an optimization-based deep Convolutional Neural Network (deep CNN).
• To develop and integrate a novel optimization algorithm, the Bat-Rider Optimization Algorithm (BaROA), for enhanced classification performance.
• To improve the accuracy and efficiency of identifying cardiac arrhythmias from electrocardiogram (ECG) signals.

Main Methods:

• Feature extraction from ECG signals using wave and Gabor filters to capture individual ECG characteristics.
• Development of the Bat-Rider Optimization Algorithm (BaROA) by combining the multi-objective bat algorithm (MOBA) and Rider Optimization Algorithm (ROA).
• Implementation of a deep CNN classifier optimized by BaROA for classifying ECG signals into arrhythmia and no-arrhythmia categories.

Main Results:

• The BaROA-based deep CNN achieved high classification accuracy of 93.19%.
• The system demonstrated strong performance with a specificity of 95% and sensitivity of 93.98%.
• Analysis conducted on the MIT-BIH Arrhythmia Database validated the effectiveness of the proposed method.

Conclusions:

• The proposed automated arrhythmia classification strategy using BaROA-based deep CNN is effective and accurate.
• This method offers a promising solution for automatic monitoring and classification of cardiac arrhythmias.
• The high accuracy, specificity, and sensitivity indicate the clinical potential of this approach for early detection and management of arrhythmias.