The Ecg Monitoring And Decision Support System

Problem Statement

  • Cardiovascular disease is one of the leading causes of death in humans
  • Automatic detection of heart diseases based on ECG provides important assistances for doctors, and also helps common people to self-monitor 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.
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Technologies Used

Software:
  • CNNs, GRUs
  • Residual Neural Network (ResNet)
  • Tensorflow, Keras
  • Scikit-learn
 
Scikit-learn :
  • AD8232
  • ESP32
  • Raspberry Pi 4
  • PySide2