Abstract
Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.
Highlights
Diabetes is an incurable chronic disease that occurs either when the pancreas is no longer able to produce insulin, or when the body is unable to utilize insulin properly [1,2]
The preprocessing steps were held constant throughout the trials—which involved denoising through Gaussian filter and baseline corrections using Asymmetric Least Square (ALS)
A computational model comprised of signal processing techniques used for cleaning the data and extracting the features and regression models trained with the features for the quantitative estimation of blood glucose has been presented
Summary
Diabetes is an incurable chronic disease that occurs either when the pancreas is no longer able to produce insulin, or when the body is unable to utilize insulin properly [1,2] This results in poor regulation of blood glucose level, which can lead to severe health complications such as chronic heart and kidney disease if blood glucose levels are not monitored carefully. The conventional approach to glucose level monitoring requires several apparatuses such as a glucometer, a one-time test strip, and a single-use lancet or lancing device to draw blood [4]. It requires alcohol pads, gloves, and a band-aid to reduce the risk of infection for the patient. It is an uncomfortable process and one that people with diabetes need to use to monitor their blood glucose level regularly
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