Automatic cough detection based on airflow signals for portable spirometry system

TL;DR

This paper presents a novel, airflow signal-based automatic cough detection algorithm integrated into a portable spirometry system, demonstrating high accuracy and robustness across diverse environments and patient data.

Contribution

It introduces the first fully reproducible airflow signal-based cough detection algorithm implemented in a commercial spirometry device, validated on large datasets.

Findings

Achieved 86% sensitivity and 91% specificity in cough detection

Demonstrated robustness across diverse patient and environment data

First implementation of airflow-based cough detection in a commercial system

Abstract

We give a short introduction to cough detection efforts that were undertaken during the last decade and we describe the solution for automatic cough detection developed for the AioCare portable spirometry system. In contrast to more popular analysis of sound and audio recordings, we fully based our approach on airflow signals only. As the system is intended to be used in a large variety of environments and different patients, we trained and validated the algorithm using AioCare-collected data and the large database of spirometry curves from the NHANES database by the American National Center for Health Statistics. We trained different classifiers, such as logistic regression, feed-forward artificial neural network, support vector machine, and random forest to choose the one with the best performance. The ANN solution was selected as the final classifier. The classification results on the test set (AioCare data) are: 0.86 (sensitivity), 0.91 (specificity), 0.91 (accuracy) and 0.88 (F1 score). The classification methodology developed in this study is robust for detecting cough events during spirometry measurements. As far as we know, the solution presented in this work is the first fully reproducible description of the automatic cough detection algorithm based totally on airflow signals and the first cough detection implemented in a commercial spirometry system that is to be published.