Affiliations: [a] Department of Applied Electronics {and} Instrumentation, Government Engineering College, Kozhikode, Kerala, India | [b] Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore | [c] School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore | [d] Biomedical Technologies Inc., Denver, CO, USA and Idaho State University (Aff.), ID, USA | [e] Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
Correspondence:
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Corresponding author: G. Swapna, Department of Applied Electronics {and} Instrumentation, Government Engineering College, Kozhikode, Kerala 673005, India. E-mail: [email protected].
Abstract: Diabetes Mellitus, often referred to as diabetes, is a chronic disease that affects a vast majority of world population. The percentage of people affected is increasing every year. Diabetes is very difficult to cure. It can only be kept under control. In this scenario, diagnosis of diabetes is of great importance. In this work, we used Heart Rate Variability (HRV) signals obtained from ECG signals for the purpose of diagnosis of diabetes. We employed signal processing methods to extract features from the HRV signal. Since HRV signals are of nonlinear nature, we made use of Higher Order Spectrum (HOS) based features for analysis. In this paper, we have extracted the HOS features from HRV signals corresponding to normal and diabetic subjects. These selected features were fed independently to seven classifiers namely Gaussian Mixture Model (GMM), Support Vector Machine (SVM), NaïveBayes classifier (NB), K-Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Fuzzy classifier and Decision Tree (DT) classifier. The performance of these classifiers was evaluated using accuracy, sensitivity, specificity, positive predictive value, and the area under the receiver operating characteristics curve measures. We observed that the GMM classifier presented the highest accuracy of 90.5%, while the other classifiers presented accuracies in the range of 86.5% to 71.4%. Thus, the proposed Computer Aided Diagnostic (CAD) technique has the ability to detect diabetes efficiently by analyzing the subtle changes in ECG signals that are indicative of the presence of diabetes in a patient. Also, we have proposed unique bispectrum and bicoherence plots for normal and diabetes heart rate signals.
