Feasibility of Cough Detection and Classification Using Artificial Intelligence in an Ambulatory Setting with a Ceiling Mounted Microphone.

Bertschinger, Simon; Fenner, Lukas; Denecke, Kerstin (2023). Feasibility of Cough Detection and Classification Using Artificial Intelligence in an Ambulatory Setting with a Ceiling Mounted Microphone. In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 660-665). IEEE 10.1109/CBMS58004.2023.00296

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Cough is a sign of numerous respiratory infections
and is often quantified by cough frequency. Although the need
for accurate and objective cough detection in ambulatory
settings is widely acknowledged in the medical literature, little
research has been done on automating the classification using a
single microphone in an open, real-world setting. This study
examined the feasibility of applying artificial intelligence to
recognize and categorize coughs by patients wearing or not
wearing masks in a waiting room of a primary care institution
with a single microphone and varying degrees of background
noise. A sequential convolutional neural network (CNN)
consisting of two 2D convolutional layers with 3x3 kernels and
four filters were used with varying parameters. The best
performing classification model used three layers with 64, 32
and 16 filters. It achieved an overall accuracy of 98.5% with a
sensitivity of 98.2% and specificity of 98.8%. The findings imply
that detection using artificial intelligence and a single
microphone in a waiting room might be feasible to use in certain
scenarios.

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