Type 2 Diabetes Screening Test by Means of a Pulse Oximeter

Abstract

Abstract-In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts; the first analyses the signal obtained from the pulse oximeter, and the second consists of a machine-learning module.The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i.e. whether the subject had diabetes or not. The machine-learning algorithms were random forests, gradient boosting, and linear discriminant analysis as benchmark. The system was tested on a database of 1, 157 subjects (two samples per subject) collected from five community health centres.The mean receiver operating characteristic (ROC) area found was 69.4% (median value 71.9% and range [75.4%−61.1%]), with a specificity=64% for a threshold that gave a sensitivity=65%.We present a screening method for detecting diabetes that has a performance comparable to the glycated haemoglobin (haemoglobin A1c HbA1c) test, does not require blood extraction, and yields results in less than five minutes.

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

“…An interesting article about diabetes detection was proposed by Monte-Moreno et al [73]. In this study, the cepstral analysis was computed over PPG signals from 830 diabetic and 340 healthy subjects.…”

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

“…A painless, easy to operate, and low-cost will improve patient compliance with routine blood glucose monitoring and early detection of diabetes [8,10]. Non-invasive methods to monitor or predict BGL have been developed using different techniques, as shown in Figure 1 [1,8,[10][11][12][13][14]. The preferred approach is based on sensors and optical techniques [11,[15][16][17].…”

“…The PPG signal is collected from a finger sensor that uses a photodiode as an optical sensor to capture light from an infrared LED, as shown in Figure 2. The Prototype of PPG signals is an easy and inexpensive optical technique measurement method that could monitor BGL in diabetes [14,17,19]. Many academics have developed prototypes with optical sensor-based data-acquisition systems.…”

“…Many academics have developed prototypes with optical sensor-based data-acquisition systems. The prototypes are The Prototype of PPG signals is an easy and inexpensive optical technique measurement method that could monitor BGL in diabetes [14,17,19]. Many academics have developed prototypes with optical sensor-based data-acquisition systems.…”

“…The combination of PPG data collected by near-infrared sensors with artificial intelligence (AI) makes it possible to predict BGL. Machine learning (ML) or deep learning (DL) approaches are equally good at predicting BGL using PPG [14,[38][39][40][41][42][43][44]. A common challenge in implementing ML or DL is limited test samples.…”