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.
Chanchotisatien et al. (Mon,) studied this question.