Abstract
This study focuses on the development and analysis of an algorithm utilizing neural network techniques for the processing of electrocardiograms. The proposed approach integrates advanced neural network models to enhance the processing and analysis of intricate electrocardiogram (ECG) data. The algorithm aims to accurately detect and interpret various cardiac patterns and abnormalities within ECG signals through neural network analysis, contributing to the efficient diagnosis and monitoring of cardiovascular conditions. The research involves the application of machine learning methodologies, particularly neural networks, to optimize signal processing, enabling robust feature extraction and pattern recognition from ECG data. The study's methodology involves training and validating the neural network algorithm with a diverse dataset of electro cardio signals, ensuring its effectiveness across varying cardiac conditions and patterns. The outcomes of this research aim to offer a sophisticated tool for clinicians and researchers, enhancing the accuracy and speed of ECG analysis, and ultimately contributing to improved clinical decision-making and patient care in cardiology.practitioners for the evolving educational landscape.