Relationship between PPG Signals and Glucose levels through Chaotic Descriptors and Support Vector Machines

© 2021 IEEE.When shining a light through a finger, some of it will be absorbed by oxygenated and unoxygenated hemoglobin. Measuring the absorbed light over time provides the photo-plethysmographic (PPG) waveform, which can represent the blood flow of a subject. One way of obtaining the PPG waveform is to use the camera and flash of a smartphone, placing them on the finger of a subject, and analyzing the variation of red color. The PPG can also be obtained using oximeter-like devices, which are non-invasive and safe. In contrast, to measure the blood glucose of a subject, a glucometer is used, which is a device that is typically invasive and expensive. Therefore, we propose the use of the following descriptors from Chaos theory to analyze the PPG signal: correlation dimension, maximum Lyapunov exponent and Hurst exponent. Then, these values are converted into a 3-dimensional vector that can be represented in a 3-dimensional space. Each vector has an associated glucose level that is used to train an algorithm which classifies all the vectors in three different ranges of blood glucose levels.

Relationship between PPG Signals and Glucose levels through Chaotic Descriptors and Support Vector Machines Academic Article in Scopus