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Publication Type : Conference Paper
Publisher : Procedia Computer Science
Source : Procedia Computer Science, Volume 132, p.1253 - 1262 (2018)
Keywords : Cardiovascular Autonomic Neuropathy, CNN, Deep learning, diabetes, ECG, HRV, LSTM
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2018
Abstract : Diabetes mellitus, commonly known as diabetes, is a disease that affects a vast majority of people globally. Diabetes cannot be cured; it can only be kept under control. In this paper, diabetes is diagnosed by the analysis of Heart Rate Variability (HRV) signals obtained from ECG signals. We employed deep learning networks of Convolutional neural network (CNN) and CNN-LSTM (LSTM = Long short term memory) combination to automatically detect the abnormality. Unlike the conventional analysis methods so far followed, deep learning techniques do not require any feature extraction. We initially performed classification splitting the database into separate training and testing data. The maximum accuracy obtained for test data is 90.9% using CNN-LSTM. Using 5 fold cross-validation, CNN gave an accuracy of 93.6% while CNN-LSTM combination gave the maximum accuracy of 95.1%. As per our best knowledge, this is the first paper in which deep learning techniques are employed in distinguishing diabetes and normal HRV. The accuracy obtained using cross-validation is the maximum value achieved so far for the the automated detection of diabetes using HRV. © 2018 The Authors. Published by Elsevier Ltd.
Cite this Research Publication : S. G, Dr. Soman K. P., R, V., and Dr. Soman K. P., “Automated Detection of Diabetes using CNN and CNN-LSTM Network and Heart Rate Signals”, in Procedia Computer Science, 2018, vol. 132, pp. 1253 - 1262.