Problem Statement

Cardiovascular disease is one of the leading causes of death in humans.

Automatic detection of heart diseases based on ECG provides important assistance for doctors, and also helps common people in self-monitoring their heart conditions.

We propose a portable and inexpensive ECG Monitoring and Decision Support System which is based on IoT and Machine Learning. 


The main objective of this system is to monitor electrical activity of the patient’s heart and filter patient’s electrocardiogram (ECGs) after that apply machine-learning classifiers to identify cardiac health risk and estimate severity by learning pattern within the database which serves as the basic predictions about patient’s heart condition then upload that report and real time value of patient’s ECG to the cloud and display on the device for future analyses by a cardiologist worldwide with the help of communication platforms such as web and mobile application.

Technologies Used