Analysis of the use of activated wavelet functions in wavelet neural networks for categorizing ECG cardiac reductions

Accurate and automated classification of heartbeats in an electrocardiogram (ECG) is fundamental to the diagnosis and treatment of cardiovascular diseases (CVD), but it remains a challenging task due to the complexity and variability of ECG signals [1]. This study examines the effectiveness of using various wavelet functions, including Morlet, Mexican Hat, Gabor, Shannon, and Gaussian, as activation functions in a wavelet neural network (WNN) architecture for ECG heart rate classification [3]. Using a publicly available dataset obtained from the MIT-BIH Arrhythmia and PTB Diagnostic ECG databases focusing on the 5-grade arrhythmia categorization based on the Association for the Advancement of Medical Instrumentation (AAMI) standard [5]. The key findings point to differences in the performance of different wavelet activation functions, emphasizing the potential benefits of using the inherent properties of time-frequency localization of wavelets to capture discriminative morphological features in ECG rhythms [7]. This work emphasizes the prospects of using wavelet-based activation functions to improve the accuracy and reliability of deep learning models in biomedical signal processing programs, including ECG analysis.