Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring

Abstract

This study introduces an automated, non-intrusive method for detecting cough events in clinical settings, using a flexible chest patch with tri-axial acceleration sensors. It aims to achieve remote and automatic identification of coughing in patients with respiratory system diseases, providing clinicians with a tool for the remote and objective recording and recognition of changes in patients' cough patterns. Twenty-five young and healthy persons (hereinafter referred to as healthy adults) and twenty-five clinical 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 higher scoring features were then combined using several feature selection algorithm to perform the cough classification task. The Multi-Criteria Decision Making (MCDM) method was used to select the classifier with the highest scores. The optimized classifier proposed in this paper achieved 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, and 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. Our study demonstrates the effectiveness of an automated cough recognition system in both pneumoconiosis patients and healthy adults. This approach facilitates daily remote monitoring of cough occurrences in individuals with pneumoconiosis, potentially enhancing the ability of physicians to evaluate the clinical status.