2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 2 , Issue 2 , PP: 42-49, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
S Hariharan 1 * , Monika Gupta 2
Doi: https://doi.org/10.54216/FPA.020201
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.
Internet of Things (IoT) , Generative Adversarial Networks (GAN) , Deep Learning , ECG Classification , Convolution Neural Networks
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