Authors: P. Sankar; Marykutty Cyriac
Addresses: Department of ECE, Jerusalem College of Engineering, Pallikaranai, Chennai 600100, India ' Department of Biomedical Engineering, Jerusalem College of Engineering, Pallikaranai, Chennai 600100, India
Abstract: As per the records of World Health Organisation, diabetes is currently one of the major diseases faced by the world community. This necessitates the introduction of screening tools for diabetes. In this paper, a non-invasive approach is proposed to diagnose the presence of Type 2 diabetes by analysing the relationship between the Heart Rate Variability (HRV) parameters and the arterial blood glucose changes. The HRV analysis is performed using non-linear methods such as Detrended Fluctuation Analysis (DFA) and Poincare plot. A few parameters derived from these non-linear methods are used to introduce two metrics named as Standard Deviation Ratio (SDR) and alpha-ratio. These two metrics are given as input to a machine learning classifier to categorise the subjects as diabetic or non-diabetic. The accuracy analysis of the classification results shows that 94.7% of the subjects are categorised correctly. Therefore, the proposed metrics can be considered as non-invasive screening tools in predicting the presence of Type 2 diabetes.
Keywords: alpha-ratio; detrended fluctuation analysis; heart rate variability; Poincare plot; standard deviation ratio.
International Journal of Biomedical Engineering and Technology, 2018 Vol.26 No.1, pp.71 - 83
Available online: 02 Jan 2018 *Full-text access for editors Access for subscribers Free access Comment on this article