Poor sleep quality is common in individuals with chronic obstructive pulmonary disease (COPD) and is linked to higher risk of exacerbation and hospitalisation. While clinical studies typically assess sleep quality using the subjective Pittsburgh Sleep Quality Index (PSQI), this study proposes an objective method based on continuous respiratory signals from the chest-worn RESpeck device. Features were extracted from respiratory rate, breath regularity, PSQI-aligned signal metrics, and attractor reconstruction of respiratory dynamics, alongside a consolidated feature set combining all domains. Five machine learning models, including CatBoost, were trained on overnight data (21:00-10:00) to predict the self-reported score (0-5) to Question 7 of the COPD Assessment Test (Q7CAT), which evaluates sleep quality. The CatBoost model with the consolidated feature set achieved the best performance, with a mean absolute error of$0.75 \pm 0.20$and a quadratic weighted kappa of$0.60 \pm 0.15$, using 160 nights of data from 12 COPD patients (SMILE dataset). These results demonstrate that respiratory signal–derived features can objectively predict sleep quality, offering a potential alternative to subjective questionnaires and supporting early intervention in COPD management.

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 phase-space attractor reconstruction, a nonlinear timeseries 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. 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.

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.

This paper describes an unobtrusive cough monitor based on the wireless Respeck sensor worn as a patch on the chest, in tandem with a deep learning-based cough classification method for the automatic detection of instances of coughs in the Respeck sensor data. The cough monitor was evaluated on an unseen, 2.5h-long, Respeck dataset which mimicked real-life settings and achieved an accuracy of greater than 82% using a one-dimensional convolutional neural network. Results are presented on testing the Respeck cough monitor in the wild on asthma and COPD patients which provided insights validated by independent publications.