Improved Cough Classification With Symmetric Projection Attractor Reconstruction.

Area of Science:

• Biomedical Engineering
• Data Science
• Pulmonology

Background:

• Respiratory disturbances like coughing are critical indicators of patient health decline.
• Accurate, continuous monitoring of respiratory conditions is essential for effective patient management.
• Existing methods may not capture the complex dynamics of respiratory signals.

Purpose of the Study:

• To enhance the classification accuracy of respiratory disturbances using Symmetric Projection Attractor Reconstruction (SPAR).
• To evaluate the efficacy of SPAR-augmented deep learning models for analyzing data from the RESpeck device.
• To assess the viability of remote cough detection in Chronic Obstructive Pulmonary Disease (COPD) patients.

Main Methods:

• Utilized Symmetric Projection Attractor Reconstruction (SPAR) to process time-series accelerometer data from the RESpeck device.
• Trained and evaluated various deep learning models (CNNs, RNNs, hybrids) on labeled RESpeck data from healthy volunteers.
• Tested SPAR-enhanced deep learning models, specifically a CNN-BiLSTM, on longitudinal data from COPD patients.

Main Results:

• A SPAR-enhanced CNN-BiLSTM model achieved 83.09% accuracy in classifying respiratory disturbances.
• Analysis revealed diurnal coughing patterns in COPD patients, consistent with existing respiratory health studies.
• Demonstrated the feasibility of using the RESpeck device for practical, remote cough detection.

Conclusions:

• Combining deep learning with SPAR significantly improves the accuracy of respiratory disturbance detection from RESpeck data.
• Remote monitoring of coughs using this technology offers a viable alternative to clinical visits and self-reporting.
• This approach facilitates early exacerbation identification, personalized interventions, and improved COPD patient outcomes.