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.
Citation:
Hettiarachchi, C.Y. (2020). Use of machine learning for the prediction of diabetes from photoplethysmography (PPG) measurements & physiological characteristics [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22273