Rational selection of RGB channels for disease classification based on IPPG technology

Abstract

The green channel is usually selected as the optimal channel for vital signs monitoring in image photoplethysmography (IPPG) technology. However, some controversies arising from the different penetrability of skin tissue in visible light remain unresolved, i.e., making the optical and physiological information carried by the IPPG signals of the RGB channels inconsistent. This study clarifies that the optimal channels for different diseases are different when IPPG technology is used for disease classification. We further verified this conclusion in the classification model of heart disease and diabetes mellitus based on the random forest classification algorithm. The experimental results indicate that the green channel has a considerably excellent performance in classifying heart disease patients and the healthy with an average Accuracy value of 88.43% and an average F1score value of 93.72%. The optimal channel for classifying diabetes mellitus patients and the healthy is the red channel with an average Accuracy value of 82.12% and the average F1score value of 89.31%. Due to the limited penetration depth of the blue channel into the skin tissue, the blue channel is not as effective as the green and red channels as a disease classification channel. This investigation is of great significance to the development of IPPG technology and its application in disease classification.