A deep learning classification system using CNN, LSTM, and SVM diagnosed diabetes from ECG-derived heart rate variability signals with an accuracy of 95.7%.
Do deep learning algorithms applied to ECG-derived heart rate variability signals accurately detect diabetes?
Deep learning algorithms applied to ECG-derived heart rate variability signals can non-invasively detect diabetes with high accuracy (95.7%).
Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. RR-interval signals known as heart rate variability (HRV) signals (derived from electrocardiogram (ECG) signals) can be effectively used for the non-invasive detection of diabetes. This research paper presents a methodology for classification of diabetic and normal HRV signals using deep learning architectures. We employ long short-term memory (LSTM), convolutional neural network (CNN) and its combinations for extracting complex temporal dynamic features of the input HRV data. These features are passed into support vector machine (SVM) for classification. We have obtained the performance improvement of 0.03% and 0.06% in CNN and CNN-LSTM architecture respectively compared to our earlier work without using SVM. The classification system proposed can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%.
Swapna et al. (Thu,) conducted a other in Diabetes. Deep learning architectures (LSTM, CNN) combined with SVM vs. Earlier work without using SVM was evaluated on Classification accuracy of diabetic and normal HRV signals. A deep learning classification system using CNN, LSTM, and SVM diagnosed diabetes from ECG-derived heart rate variability signals with an accuracy of 95.7%.