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Full Length Article
Volume 2 , Issue 2, PP: 42-49 , 2020


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   :  10.5281/zenodo.3944661

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 on 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 sensitive nature of data and the rarity of certain cases involved in CVDs. In this paper, we propose a framework which involves Generative Adversarial Networks (GAN) to create synthetic training data for the classes with less data points to improve the performance of Deep Learning models trained with the dataset. With data being input from sensors via 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

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