A CNN-LSTM deep learning network analyzing heart rate variability signals achieved a maximum accuracy of 95.1% using 5-fold cross-validation for the automated detection of diabetes.
Do CNN and CNN-LSTM deep learning networks accurately detect diabetes using heart rate variability signals?
Deep learning techniques, specifically CNN-LSTM networks, can accurately detect diabetes using ECG-derived heart rate variability signals without requiring manual feature extraction.
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.
Swapna et al. (Mon,) conducted a other in Diabetes mellitus. CNN and CNN-LSTM network analysis of HRV signals vs. Normal HRV was evaluated on Accuracy of automated detection. A CNN-LSTM deep learning network analyzing heart rate variability signals achieved a maximum accuracy of 95.1% using 5-fold cross-validation for the automated detection of diabetes.