A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL

Abstract

Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.