Objective Study of Sensor Relevance for Automatic Cough Detection

TL;DR

This study objectively evaluates various sensors for automatic cough detection, demonstrating that a combined approach achieves high sensitivity and specificity, outperforming existing commercial systems.

Contribution

The paper provides a comprehensive analysis of multiple sensors for cough detection, establishing their relative effectiveness and proposing an improved detection method.

Findings

Achieved about 94.5% sensitivity and specificity in cough detection.

Outperformed commercial Karmelsonix system in accuracy.

Identified the most relevant sensors for reliable cough detection.

Abstract

The development of a system for the automatic, objective and reliable detection of cough events is a need underlined by the medical literature for years. The benefit of such a tool is clear as it would allow the assessment of pathology severity in chronic cough diseases. Even though some approaches have recently reported solutions achieving this task with a relative success, there is still no standardization about the method to adopt or the sensors to use. The goal of this paper is to study objectively the performance of several sensors for cough detection: ECG, thermistor, chest belt, accelerometer, contact and audio microphones. Experiments are carried out on a database of 32 healthy subjects producing, in a confined room and in three situations, voluntary cough at various volumes as well as other event categories which can possibly lead to some detection errors: background noise, forced expiration, throat clearing, speech and laugh. The relevance of each sensor is evaluated at three stages: mutual information conveyed by the features, ability to discriminate at the frame level cough from these latter other sources of ambiguity, and ability to detect cough events. In this latter experiment, with both an averaged sensitivity and specificity of about 94.5%, the proposed approach is shown to clearly outperform the commercial Karmelsonix system which achieved a specificity of 95.3% and a sensitivity of 64.9%.