Attractor Reconstruction of Breathing Dynamics: Characterising Respiratory Dysfunction in COPD.

Abstract

Chronic obstructive pulmonary disease (COPD) is characterised by persistent airflow limitation and fluctuating symptoms that often go undetected in between hospital visits. This paper investigates the use of attractor-based phase-space reconstruction, a non-linear method for transforming time-series data, to characterise respiratory dynamics from chest-worn RESpeck accelerometer. Respiratory signal data were collected over two to four weeks from 50 participants (18 COPD, 32 controls) in free-living conditions. Two-dimensional attractors were derived from 60-second stationary respiratory windows, and 27 features spanning geometric, spectral, recurrence, and complexity domains were extracted. Several features showed large effect sizes and enabled COPD classification with 84.4% accuracy. Temporal analysis revealed heightened diurnal variability in COPD, particularly during night-to-morning transitions. A case study of a COPD subject demonstrated that attractor-derived features captured gradual pre-exacerbation changes not evident from respiratory rate alone. These findings highlight the potential of attractor-derived features as objective, high-resolution digital biomarkers of respiratory dysfunction, validating their use in passive, continuous monitoring for personalised COPD management.