Use of machine learning for the prediction of diabetes from photoplethysmography (PPG) measurements & physiological characteristics

Abstract

Type 2 Diabetes (T2D) is a chronic disease affecting millions of people worldwide. It is a result of impaired glucose regulation, leading to abnormally high levels of glucose causing microvascular and macrovascular problems. The failure to timely identify and treat, results in complications such as limb amputations, blindness and heart disease. Busy unhealthy lifestyles are a root cause and not much effort undertaken to obtain regular health checkups for early T2D detection. Photoplethysmography (PPG) is a non-invasive, optic technique mostly used towards disease estimation in clinical environments. Recent technological advancements have integrated PPG sensors within smartphones and wearables. However, these signals suffer from various noise components, which is intensified in signals acquired in routine everyday environments. The research analysed the feasibility of short (~2.1s) PPG segments in order to address these limitations and identify biomarkers related to T2D. The identified biomarkers mainly relate to the vascular system of the body. Several classification algorithms were evaluated using cross validation to estimate T2D, focussing on a public PPG dataset. Linear Discriminant Analysis (LDA) achieved the highest area under the ROC curve of 79% for the estimation of T2D in a setting where healthy individuals, T2D only, T2D subjects with hypertension and prehypertension were present. It is important to identify relationships between standard medical measures such as Fasting Blood Glucose (FBG) and PPG features, for better understanding T2D estimation. FBG measurements were collected, and several regression algorithms evaluated using leaveone-out cross validation to assess the suitability of predicting FBG using PPG features. The

results

were examined using the Clarke’s Error Grid, where 75% & 22.5% of predictions were distributed in regions A & B respectively for both ElasticNet and Lasso Regression. The results were comparable with long PPG signal based approaches. The suitability of the method in practical environments was evaluated using simulated PPG signals with noise and motion artifacts. The ElasticNet Regression achieved 70% and 27.5% in regions A & B respectively. The analysis of short PPG segments shows promise towards the development of an early T2D estimation system in a routine everyday environment.