Respiratory Attractor Dynamics and Their Association with Symptom Burden in COPD

Passara Chanchotisatien, DK Arvind

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Keywords: Chronic obstructive pulmonary disease, Respiratory signal processing, Wearable sensors, Nonlinear signal analysis, Feature extraction, Patient monitoring

Abstract: Chronic obstructive pulmonary disease (COPD) is characterised by persistent respiratory symptoms and activity limitations. While tools like the COPD Assessment Test (CAT) enable self-reported monitoring, they lack physiological objectivity and temporal resolution. This study investigates the use of \textit{phase-space attractor reconstruction}, a nonlinear time-series method, for symptom tracking using respiratory signals from a chest-worn accelerometer. Data from 12 COPD patients over four to six weeks were segmented using a CNN-BiGRU-based activity classifier to isolate stationary periods. \textit{Attractor reconstructions} were computed at 60-second intervals, and 112 features spanning geometric, spectral, and topological domains were extracted. Several features showed noteworthy correlations with total and item-level CAT scores, supporting their potential as objective markers of symptom burden. These results highlight the feasibility of attractor-based analysis for non-invasive, continuous COPD monitoring and personalised disease management.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Passara Chanchotisatien ([e-mail])

Submission Number: 73