Unveiling the Power of Convolutional Networks: Applied Computational Intelligence for Arrhythmia Detection from ECG Signals

Abstract

Arrhythmias are a significant cause of morbidity and mortality worldwide, necessitating accurate and timely detection for effective clinical intervention. Electrocardiogram (ECG) signals serve as invaluable sources of information for diagnosing arrhythmias, but their analysis is complex and demanding. Recent advancements in computational intelligence, particularly Convolutional Networks (CNNs), have demonstrated remarkable capabilities in various signal-processing tasks. In this paper, we unveil the power of CNNs by applying computational intelligence techniques to detect arrhythmias from ECG signals. The proposed methodology involves preprocessing the ECG signals to enhance their quality and remove noise interference. Subsequently, CNN architectures are developed and trained using a large dataset of annotated ECG recordings. The network's structure is optimized to effectively capture the discriminative features present in the ECG signals that characterize diverse types of arrhythmias. Through an extensive evaluation process, the performance of the CNN models is assessed using confusion matrices. Experimental results demonstrate the effectiveness of the applied computational intelligence approach in arrhythmia detection. The CNN model achieves outstanding performance, exhibiting robustness against noise and variations in ECG recording conditions, highlighting its potential for real-world applications.