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

Monitoring System for Post-Operative Heart Disease Patients

Authors

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

Keywords:

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

Abstract

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.

References

Aieshwarya B. Chavan Patil, S. S. Sonawane, “To Predict Heart Disease Risk and Medications Using Data Mining Techniques with an IoT Based Monitoring System for Post-Operative Heart Disease Patients,” International Journal on Emerging Trends in Technology. 2017.

A. Alonso and F. L. Norby, “Predicting atrial fibrillation and its complications,” Apr. 2015.

Chang, F.C.; C.K. Chang, and C.C. Chiu, “Variations of HRV Analysis in Different Approaches.”, International Conference on Computers in Cardiology, 2007

da S.; Luz, W.R. Schwartz, G. Cmara-Chvez, D. Menotti, ECG-based heartbeat classification for arrhythmia detection: a survey, Comput. Methods Prog. Biomed. 127 (Suppl. C) (2016) 144–164

Melillo P. and R. zzo, “Automatic Prediction of Cardiovascular and Cerebrovascular Events Using Heart Rate Variability Analysis,” Mar. 2015.

Güler, İ. and Übeylı˙, E. (2005). ECG beat classifier designed by combined neural network model. Pattern Recognition, 38(2), pp.199-208.

Jun, T. J.; Park, H. J.; Minh, N. H.; Kim, D. and Y.-H. Kim, “Premature Ventricular Contraction Beat Detection with Deep Neural Networks,” 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016.

Simonyan, Karen, Zisserman, and Andrew, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv.org, 10-Apr-2015. [Online]. Available: https://arxiv.org/abs/1409.1556. [Accessed: 14-Mar-2019].

Jun, T.J.; Nguyen, H.M.; Kang, D.; Kim, D.; Kim, D. and Kim, Y.-H. “ECG Arrhythmia Classification using a 2-D Convolutional Neural Network,” 2018.

Downloads

Published

2020-10-05

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 https://worldascience.com/journals/index.php/wassn/article/view/11

Issue

Section

Computer Science & Mathematics