Authors : Nilotpal Bhandary; Soumen Bhowmik

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/vrtjcyey

Scribd : https://tinyurl.com/4pcy4ccs

DOI : https://doi.org/10.38124/ijisrt/26mar1285

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Abstract : This research paper propose a real-time Electrocardiography -based cardiac risk monitoring system using the AD8232 sensor integrated with machine learning algorithms for early detection of abnormal cardiac patterns. In this research paper also reviews the recent developments in Electrocardiography -based cardiac monitoring systems, focusing on wearable ECG sensors, signal preprocessing techniques, and machine learning methods used for detecting abnormal cardiac patterns. Nowadays cardiovascular disease is one of the leading causes of death worldwide, making early detection and continuous monitoring of heart activity is very much essential for all age groups. Electrocardiography (ECG) is widely used to detect cardiac abnormalities such as arrhythmia and other heart-related disorders. Recent advances in wearable sensors such as AD8232, the Internet of Things (IoT), and machine learning have enabled the development of intelligent ECG monitoring systems capable of providing real-time health information outside traditional clinical environments. Here we focused the role of AIoT-based systems in enabling remote patient monitoring and also discussed about the current challenges and future research directions for developing efficient and low-cost ECG monitoring systems are highlighted.

Keywords : ECG, AD8232, Machine Learning, Cardiac Risk Monitoring, Arrhythmia Detection, IoT Healthcare, MIT-BIH Arrhythmia.

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