Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis

Abstract

Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of invasive treatments might be overcome by a painless, inexpensive, non-invasive approach to blood glucose level (BGL) monitoring. Photoplethysmography (PPG) signals obtained from sensor leads placed on specific organ tissues are collected using photodiodes and nearby infrared LEDs. Cardiovascular disease can be detected via photoplethysmography. These characteristics can be used to directly affect BGL monitoring in diabetic patients if PPG signals are used. The Guilin People’s Hospital’s open database was used to produce the data collection. The dataset was gathered from 219 adult respondents spanning an age range from 21 to 86 of which 48 percent were male. There were 2100 sampling points total for each PPG data segment. The methodology of feature extraction from data may assist in increasing the effectiveness of classifier training and testing. PPG data information is modified in the frequency domain by the instantaneous frequency (IF) and spectral entropy (SE) moments using the time–frequency (TF) analysis. Three different forms of raw data were used as inputs, and we investigated the original PPG signal, the PPG signal with instantaneous frequency, and the PPG signal with spectral entropy. According to the results of the model testing, the PPG signal with spectral entropy generated the best outcomes. Compared to decision trees, subspace k-nearest neighbor, and k-nearest neighbor, our suggested approach with the super vector machine obtains a greater level of accuracy. The super vector machine, with 91.3% accuracy and a training duration of 9 s, was the best classifier.