Web-Based Application for Physical to Digital ECG Signal Analysis for Cardiac Dysfunctions

Abstract

Electrocardiogram (ECG) acts as a symptomatic tool that routinely analyzes the functions of the heart. Till recently, most ECG records were kept on thermal paper. The evaluation of ECG charts needs considerable training and can be time-consuming and daunting process. The evaluation of ECG charts needs considerable training and can be time-consuming and daunting process. We can perform diagnosis and analysis with automation by digitizing the paper ECG. We can perform diagnosis and analysis with automation by digitizing the paper ECG. The main goal of this chapter is physical to-digital fusion of ECG signal and implement machine learning algorithm. This can be achieved by extracting the P, QRS, and T waves in ECG signals to demonstrate the heart's electrical activity using various techniques. The web-based application can make use of a machine-learning algorithm that analyzes and diagnoses cardiac disorders and normal conditions by uploading the ECG image. Thereby it reduces the time-consuming and daunting process for the analysis of ECG reports.