Automated classification of diabetes using heart rate variability signals and various feature extraction techniques was reviewed for its efficiency in detecting cardiovascular complications.
This review discusses the techniques and efficiency of using heart rate variability signals for the automated classification of diabetes.
Diabetes mellitus (DM) is a critical and long-term disorder due to the insufficient production of insulin by the pancreas or ineffective use of insulin by the body. Importantly, cardiovascular disease (CVD) has long been thought to be linked with diabetes. Despite more diabetic individuals surviving from better medications and treatments, there has been significant rise in the morbidity and mortality from CVD. Indeed, the classification of DM based on the electrocardiogram signals of the heart will be an advantageous system. Further, computer-aided classification of DM with integrated algorithms may enhance the execution of the system. In this paper, we have reviewed various studies using heart rate variability signals for automated classification of diabetes. Furthermore, the different techniques used to extract the features and the efficiency of the classification systems are discussed.
Adam et al. (Fri,) conducted a review in Diabetes mellitus and cardiovascular disease. Automated classification of diabetes using heart rate variability signals was evaluated. Automated classification of diabetes using heart rate variability signals and various feature extraction techniques was reviewed for its efficiency in detecting cardiovascular complications.