Audio and Contact Microphones for Cough Detection
This work addresses the lack of reliable tools for objective cough detection in chronic cough diseases, but it is incremental as it builds on existing methods with minor improvements.
The paper tackled the problem of automatic cough detection by developing a system using audio and contact microphones, finding that the contact microphone added little value, and achieved better performance with an audio-only approach, yielding average sensitivity of 94.7% and specificity of 95% compared to a commercial system.
In the framework of assessing the pathology severity in chronic cough diseases, medical literature underlines the lack of tools for allowing the automatic, objective and reliable detection of cough events. This paper describes a system based on two microphones which we developed for this purpose. The proposed approach relies on a large variety of audio descriptors, an efficient algorithm of feature selection based on their mutual information and the use of artificial neural networks. First, the possible use of a contact microphone (placed on the patient's thorax or trachea) in complement to the audio signal is investigated. This study underlines that this contact microphone suffers from reliability issues, and conveys little new relevant information compared to the audio modality. Secondly, the proposed audio-only approach is compared to a commercially available system using four sensors on a database with different sound categories often misdetected as coughs, and produced in various conditions. With average sensitivity and specificity of 94.7% and 95% respectively, the proposed method achieves better cough detection performance than the commercial system.