1144 669
Full Length Article
Fusion: Practice and Applications
Volume 2 , Issue 2, PP: 42-49 , 2020 | Cite this article as | XML | Html |PDF

Title

Improving Cloud-based ECG Monitoring, Detection and Classification using GAN

Authors Names :   S Hariharan   1 *     Monika Gupta   2  

1  Affiliation :  Maharaja Agrasen Institute of Technology, Delhi, INDIA

    Email :  hari0298@gmail.com


2  Affiliation :  Maharaja Agrasen Institute of Technology, Delhi, INDIA

    Email :  monikagupta@mait.ac.in



Doi   :   https://doi.org/10.54216/FPA.020201

Received: April 13, 2020 Revised: April 28, 2020 Accepted: June 10, 2020

Abstract :

Internet of Things (IoT) based healthcare applications have grown exponentially over the past decade. With the increasing number of fatalities due to cardiovascular diseases (CVD), it is the need of the hour to detect any signs of cardiac abnormalities as early as possible. This calls for automation in the detection and classification of said cardiac abnormalities by physicians. The problem here is that there is not enough data to train Deep Learning models to classify ECG signals accurately because of the sensitive nature of data and the rarity of certain cases involved in CVDs. In this paper, we propose a framework that involves Generative Adversarial Networks (GAN) to create synthetic training data for the classes with fewer data points to improve the performance of Deep Learning models trained with the dataset. With data being input from sensors via the cloud and this model to classify the ECG signals, we expect the framework to be functional, accurate, and efficient.

Keywords :

Internet of Things (IoT); Generative Adversarial Networks (GAN); Deep Learning; ECG Classification; Convolution Neural Networks

References :

1.     Acharya, D., Huang, Z., Paudel, D., & Van Gool, L. (2018). Covariance Pooling For Facial Expression Recognition. ArXiv:1805.04855 [Cs]. http://arxiv.org/abs/1805.04855

2. Azariadi, D., Tsoutsouras, V., Xydis, S., & Soudris, D. (2016). ECG signal analysis and arrhythmia detection on IoT wearable medical devices. 2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST), 1–4. https://doi.org/10.1109/MOCAST.2016.7495143

3. Coast, D. A., Stern, R. M., Cano, G. G., & Briller, S. A. (1990). An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Transactions on Bio-Medical Engineering, 37(9), 826–836. https://doi.org/10.1109/10.58593

4. Delaney, A. M., Brophy, E., & Ward, T. E. (2019). Synthesis of Realistic ECG using Generative Adversarial Networks. ArXiv:1909.09150 [Cs, Eess, Stat]. http://arxiv.org/abs/1909.09150

5. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Networks. ArXiv:1406.2661 [Cs, Stat]. http://arxiv.org/abs/1406.2661

6. Kachuee, M., Fazeli, S., & Sarrafzadeh, M. (2018). ECG Heartbeat Classification: A Deep Transferable Representation. 2018 IEEE International Conference on Healthcare Informatics (ICHI), 443–444. https://doi.org/10.1109/ICHI.2018.00092

7. Kiranyaz, S., Ince, T., & Gabbouj, M. (2016). Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 63(3), 664–675. https://doi.org/10.1109/TBME.2015.2468589

8. Kshirsagar, P. R. (n.d.). Classification of ECG-signals using Artificial Neural Networks. 5.

9. Mustaqeem, A., Anwar, S. M., & Majid, M. (2018, March 5). Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants [Research Article]. Computational and Mathematical Methods in Medicine; Hindawi. https://doi.org/10.1155/2018/7310496

10. Shaker, A. M., Tantawi, M., Shedeed, H. A., & Tolba, M. F. (2020). Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks. IEEE Access, 8, 35592–35605. https://doi.org/10.1109/ACCESS.2020.2974712

11. Willems, J. L., & Lesaffre, E. (1987). Comparison of multigroup logistic and linear discriminant ECG and VCG classification. Journal of Electrocardiology, 20(2), 83–92. https://doi.org/10.1016/S0022-0736(87)80096-1

 

 


Cite this Article as :
S Hariharan , Monika Gupta, Improving Cloud-based ECG Monitoring, Detection and Classification using GAN, Fusion: Practice and Applications, Vol. 2 , No. 2 , (2020) : 42-49 (Doi   :  https://doi.org/10.54216/FPA.020201)