Classification of Respiratory Diseases

Abstract Contactless measurement methods offer a novel approach to assessing respiratory parameters. This study investigates the feasibility of classifying chronic obstructive pulmonary disease, asthma, and healthy individuals using depth-based plethysmography (DPG). The approach involves calculating Pearson's correlation coefficient for all pixel-wise signals against each other, with the cumulative result visualized in patient-specific masks. A convolutional neural network is used for the classification process. For evaluation, on a recorded data set (N=53), a classification accuracy of 57.7% and Cohen’s Kappa of 0.28 were reached. These findings provide indications that DPG might effectively classify respiratory conditions by analyzing respiratory motion dynamics.