Controlled Breathing Effect on Respiration Quality Assessment Using Machine Learning Approaches

Abstract

Thoracic bio-impedance (BioZ) measurements have been proposed as an alternative for respiratory monitoring. Given the ambulatory nature of this modality, it is more prone to noise sources. In this study, two pre-trained machine learning models were used to classify BioZ signals into clean and noisy classes. The models were trained on data from patients suffering from chronic obstructive pulmonary disease, and their performance was evaluated on data from patients undergoing bariatric surgery. Additionally, transfer learning (TL) was used to optimize the models for the new patient cohort. Lastly, the effect of different breathing patterns on the performance of the machine learning models was studied. Results showed that the models performed accurately when applying them to another patient population and their performance was improved by TL. However, different imposed respiratory frequencies were found to affect the performance of the models.