Improved Cough Classification With Symmetric Projection Attractor Reconstruction

Respiratory disturbances, such as coughing and hyperventilation, are key indicators of deterioration in a patient's condition. This study explores using Symmetric Projection Attractor Reconstruction (SPAR) to improve the accuracy of classification of respiratory disturbances using data from the RESpeck device worn as a plaster on the chest. SPAR reconstructs phase space attractors from time-series data, uncovering complex dynamics overlooked by traditional methods. Deep Learning models were trained and evaluated on labelled Respeck accelerometer data gathered by 152 healthy volunteers and tested on 904-days worth of unseen longitudinal Respeck data from 17 COPD patients. Deep learning models, including CNNs, RNNs, and hybrids, were tested on raw accelerometer data and then on the same dataset with augmented with SPAR features. A SPAR-enhanced CNN-BiLSTM achieved 83.09% accuracy. The analysis of coughing episodes in Respeck datasets gathered by COPD patients in their normal daily lives revealed diurnal coughing patterns consistent with published respiratory health studies. The results demonstrate that cough detection using the Respeck is a viable approach in practice for remote monitoring of respiratory disturbances in patients.Clinical relevance- This study demonstrates the potential of combining deep learning methods with SPAR, applied to accelerometer data from the Respeck patch worn by COPD patients, to enhance the accuracy of respiratory disturbance detection. Accurate cough detection as part of remote respiratory monitoring can reduce reliance on clinical visits and subjective self-reporting. Furthermore, this approach could enable the early identification of exacerbation events and support personalised intervention strategies, ultimately improving patient outcomes and reducing healthcare costs.