Abstract
BACKGROUND: Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events in clinical settings using a flexible chest patch with tri-axial acceleration sensors. METHODS: Twenty-five young healthy persons (hereinafter referred to as healthy adults) and twenty-five clinically diagnosed pneumoconiosis patients (hereinafter referred to as patients) participated in the experiment by wearing a flexible chest patch with an embedded ACC sensor. The top 56% of the highest scoring features were then combined using several feature selection algorithms to perform the cough classification task. The multicriteria decision making (MCDM) method was used to select the classifier with the highest scores. RESULTS: The optimized classifier proposed in this paper achieved an accuracy of 87.1%, precision of 95%, recall of 79.1%, F1 score of 86.4%, and AUC of 95.4% for recognizing coughs in healthy adults; an accuracy of 96.1%, precision of 95%, recall of 97.4%, F1 score of 96.2%, and AUC of 98.7% for recognizing coughs in patients; and an overall accuracy of 92% for distinguishing coughs in the combined group of healthy adults and patients. CONCLUSIONS: Our study demonstrated the effectiveness of an automated cough recognition system in both pneumoconiosis patients and healthy adults. This approach facilitates daily remote monitoring of cough occurrence in individuals with pneumoconiosis, potentially enhancing the ability of physicians to evaluate clinical status.