Non-contact Lung Disease Classification Via OFDM-based Passive 6G ISAC Sensing

This paper is the first to present a novel, non-contact method that utilizesorthogonal frequency division multiplexing (OFDM) signals (of frequency 5.23GHz, emitted by a software defined radio) to radio-expose the pulmonarypatients in order to differentiate between five prevalent respiratory diseases,i.e., Asthma, Chronic obstructive pulmonary disease (COPD), Interstitial lungdisease (ILD), Pneumonia (PN), and Tuberculosis (TB). The fact that eachpulmonary disease leads to a distinct breathing pattern, and thus modulates theOFDM signal in a different way, motivates us to acquire OFDM-Breathe dataset,first of its kind. It consists of 13,920 seconds of raw RF data (at 64 distinctOFDM frequencies) that we have acquired from a total of 116 subjects in ahospital setting (25 healthy control subjects, and 91 pulmonary patients).Among the 91 patients, 25 have Asthma, 25 have COPD, 25 have TB, 5 have ILD,and 11 have PN. We implement a number of machine and deep learning models inorder to do lung disease classification using OFDM-Breathe dataset. The vanillaconvolutional neural network outperforms all the models with an accuracy of97study reveals that it is sufficient to radio-observe the human chest on sevendifferent microwave frequencies only, in order to make a reliable diagnosis(with 96sensing overhead that is merely 10.93to the feasibility of 6G integrated sensing and communication (ISAC) systems offuture where 89.07exchange amidst on-demand health sensing. Through 6G ISAC, this work provides atool for mass screening for respiratory diseases (e.g., COVID-19) at publicplaces.