In this work, we give a short introduction on 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. As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.  We apply few data preprocessing steps  and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance. The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1{\div}91.2\%, 81.8{\div}83.8\% and 95.0{\div}95.9\% for the test set, respectively. The ANN solution was selected as the final classifier. Classification methodology developed in this study is robust for detecting cough events during spirometry measurements. We also show that it is universal and transferable between different systems as the performance on the NHANES and  AioCare test sets is similar. The solution presented in this work was implemented in the AioCare mobile spirometry system .
In this work, we give a short introduction on 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. As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics. Using such a massive dataset is a novelty in the field. We apply few data preprocessing steps , derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance. The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1{\div}91.2\%, 81.8{\div}83.8\% and 95.0{\div}95.9\% for the test set, respectively. The ANN solution was selected as the final classifier. Classification methodology developed in this study is robust for detecting cough events during spirometry measurements. We also show that it is universal and transferable between different systems as the performance on the NHANES and the AioCare test sets is similar. As far as we know, the solution presented in this work is the first description of the automatic cough detection algorithm based totally on the air flow signals and the first cough detection implemented in the commercial spirometry system that is to be published .
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