Automated detection of diabetes using higher order spectral features extracted from heart rate signals

  • Authors:

  • G. Swapna;U. Rajendra Acharya;S. VinithaSree;Jasjit S. Suri

  • Affiliations:

  • Department of Applied Electronics {and} Instrumentation, Government Engineering College, Kozhikode, Kerala, India;Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore and Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia;School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore;Biomedical Technologies Inc., Denver, CO, USA and Idaho State University Aff., ID, USA

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2013

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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.