CardioNet: Deep learning framework for prediction of CVD risk factors

Panwar, M. and Gautam, A. and Dutt, R. and Acharyya, Amit (2020) CardioNet: Deep learning framework for prediction of CVD risk factors. In: 52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020, 10 October 2020through 21 October 2020, Virtual, Online.

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The recent progressions in semiconductor and computing technology have empowered the PPG utilization in medical diagnosis. This paper presents a reconfigurable deep learning framework 'CardioNet' for early diagnosis of cardiovascular risk factors or most common diseases (such as diabetes, hypertension, cerebrovascular, cerebra-infraction) using the PPG data. The proposed model has a light-weight architecture, designed by exploiting the deep learning framework of convolutional neural network, exhibiting inherent capability of feature extraction, thereby, eliminating the cost effective steps of feature selection and extraction. The performance demonstration of the proposed model is done on a healthy dataset comprising 657 data segments of 219 subjects holding records of common CVD risk factors (diabetes, hypertension, cerebrovascular, cerebra-infraction). The obtained results of an overall accuracy of 97% for diagnosis of CVD risk factors, show the efficiency of the proposed model for real-time usability. The clinical significance of this work to provide an accurate and non-invasive method for early diagnosis and monitoring of cardio-risk factors. © 2020 IEEE

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IITH Creators:
IITH CreatorsORCiD
Acharyya, Amit
Item Type: Conference or Workshop Item (Paper)
Additional Information: ISSN: 0271-4310
Uncontrolled Keywords: Cardio risk factors, Classification, Convolutional neural network, Deep learning
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 27 Oct 2022 08:59
Last Modified: 27 Oct 2022 08:59
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