IOT Based ECG Monitoring System for Post-Operative Heart Disease Patients

Monitoring System for Post-Operative Heart Disease Patients


  • Charu Gandhi
  • Pratibha Luthr
  • Nikhil Kandwal
  • Nimit Garg


Support Vector Machine, CNN model, ECG dataset, MIT-BIH


In this paper we are proposing a model based on IoT wearable devices which can be used to detect the risk of heart attack in patients suffered from heart stroke. We have applied Support Vector Machine (SVM) machine learning algorithm on the ECG dataset from MIT-BIH and evaluate the accuracy of the model since the accuracy is not so good for this kind of case so, we approach to CNN model, in CNN we go with the 2D CNN so that we get the maximum features from ECG signals because many features lost during the time of noise filtration process. On comparing the accuracy of CNN model with SVM model we found that the accuracy of CNN model is far much better than SVM model.


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How to Cite

Gandhi, C. ., Luthr, P. ., Kandwal, N. ., & Garg, N. . (2020). IOT Based ECG Monitoring System for Post-Operative Heart Disease Patients: Monitoring System for Post-Operative Heart Disease Patients. WAS Science Nature (WASSN) ISSN: 2766-7715, 3(1), 1–7. Retrieved from



Computer Science & Mathematics