A Novel Deep Network Approach for Ecg Classification System Aided on Gsm

The prevalence of heart-related diseases and heart attacks has significantly risen, particularly in recent times, largely attributed to the Covid-19 pandemic, thereby increasing the burden on healthcare professionals. Electrocardiogram (ECG)-based approaches are widely employed for the detection of heart diseases due to their reliability and non-invasive nature. With the advancements in technology, numerous novel devices and methods, including machine learning and artificial intelligence (ML-AI) techniques, have been developed to address heart diseases, playing a crucial role in enhancing human well-being. This study introduces a GSM-based approach supported by deep learning, enabling remote monitoring of heart signals, aiming to alleviate the workload of doctors.The purpose of this investigation is to develop a wireless surveillance framework for ECG signals, with the goal of improving patient safety and reducing the workload of healthcare providers. Our research centers on implementing a portable, real-time, and cost-efficient ECG monitoring system that utilizes the GSM network, specifically the GSM Shield Sim900l.