ECG Signal Analysis using Whale Optimization Algorithm combined with Chaos Theory

This paper presents a novel approach for analyzing ECG signals using the Whale Optimization Algorithm (WOA) combined with Chaos Theory. The analysis of electrocardiogram (ECG) signals plays a crucial role in diagnosing various cardiovascular diseases, necessitating the development of efficient and accurate methods for signal processing. WOA which is inspired by the hunting behavior of humpback whales, is employed to optimize feature extraction and classification tasks within ECG analysis. Chaos theory (CT), which deals with nonlinear dynamic systems, is integrated to enhance the robustness and accuracy of the algorithm by introducing non-periodic, unpredictable behavior that mimics the complexity of real-world ECG signals. The proposed method is evaluated on standard ECG datasets, and the results demonstrate significant improvements in terms of signal accuracy, classification performance, and noise reduction when compared to traditional techniques. The combination of WOA and Chaos theory proves to be a powerful tool in the enhancement of ECG signal analysis, offering potential applications in the early detection of heart diseases.