Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99475
Title: Automated detection of diabetes using higher order spectral features extracted from heart rate signals
Authors: Swapna, Goutham
Acharya, U. Rajendra
VinithaSree, S.
Suri, Jasjit S.
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 2013
Source: Swapna, G., Acharya, U. R., VinithaSree, S., & Suri, J. S. (2013). Automated detection of diabetes using higher order spectral features extracted from heart rate signals. Intelligent data analysis, 17(2), 309-326.
Series/Report no.: Intelligent data analysis
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.
URI: https://hdl.handle.net/10356/99475
http://hdl.handle.net/10220/17419
DOI: http://dx.doi.org/10.3233/IDA-130580
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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