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%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 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.
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 %DIFDELCMD < }%%% %DIFDELCMD < }%%% %DIFDELCMD < }%%% 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.
R
In this work, we
We
R
on
to
A
In contrast to more popular analysis of sound and audio recordings, we fully based our approach on airflow signals only.
R
train
trained and validated
A
AioCare-collected data and
R
which is
from
R
Using such a massive dataset is a novelty in the field. We apply few data preprocessing steps, derive specific features and train different classifiers
We trained different classifiers,
R
(LR), feed forward
, feed-forward
R
(ANN), artificial neural network combined with principal component analysis (PCA-ANN),
,
R
(SVM)
,
D
(RF) on this data
R
of
with
D
accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1
D
\div
D
91.2\%, 81.8
D
\div
D
83.8\% and 95.0
D
\div
D
95.9\% for the test set, respectively. The
R
Classification
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
D
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.
A
fully reproducible
R
the air flow
airflow
R
the
a