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. 2016 May;20(3):963-969.
doi: 10.1109/JBHI.2015.2412880. Epub 2015 Mar 13.

Classification of voluntary cough airflow patterns for prediction of abnormal spirometry

Jeffrey Reynolds, William Goldsmith, Jeremy Day, Ayman Abaza, Ahmed Mahmoud, Ali Afshari, Jacob Barkley, Edward Petsonk, Michael Kashon, David Frazer

Classification of voluntary cough airflow patterns for prediction of abnormal spirometry

Jeffrey Reynolds et al. IEEE J Biomed Health Inform. 2016 May.

Abstract

Measurement of partial expiratory flow-volume curves has become an important technique in diagnosing lung disease, particularly in children and in the elderly. The objective of this study was to investigate the feasibility of predicting abnormal spirometry using the partial flow-volume curve generated during a voluntary cough. Here, abnormal spirometry is defined as less than the lower limit of normal (LLN) predicted by standard reference equations [1]. Cough airflow signals of 107 subjects (56 male, 51 female) were previously collected [2] from patients performing spirometry in a pulmonary function clinic. A variety of features were extracted from the airflow signal. A support vector machine (SVM) classifier was developed to predict abnormal spirometry. Airflow signal features and SVM parameters were selected using a genetic algorithm. The ability of the classifier to distinguish between normal and abnormal spirometry based on cough flow was evaluated by comparing the classifiers decisions with the LLN for the given subject's spirometry, including forced expiratory volume in one second (FEV1), forced vital capacity (FV C), and their ratio (FEV1=FV C%). Findings indicated that it was possible to classify patients whose spirometry results were less than the LLN with an overall accuracy of 76% for FEV1, 65% for FV C, and 76% for the ratio FEV1=FV C%. Accuracies were determined by repeated double cross-validation [3]. This study demonstrates the potential of using airflow measured during voluntary coughing to identify test subjects with abnormal spirometry.

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Figures

Fig. 1
Fig. 1
Airflow during a cough as a function of time for a subject with normal spirometry. Three example features are illustrated: peak flow, average flow, and peak acceleration.
Fig. 2
Fig. 2
Classifier diagram.
Fig. 3
Fig. 3
An example airflow waveform (upper panel), and a partial expiratory flow-volume curve (lower panel) recorded during a voluntary cough for a subject with normal spirometry.
Fig. 4
Fig. 4
An example airflow waveform (upper panel), and a partial expiratory flow-volume curve (lower panel) recorded during a voluntary cough for a subject whose spirometry was less than the lower limit of normal for FEV1 and FEV1/FV C%.
Fig. 5
Fig. 5
Histogram showing the number of times a particular feature was selected for the FEV1 classifier.
Fig. 6
Fig. 6
ROC curve (solid black line) for the FEV1 classifier. The grey lines represent the ROC curves calculated on each of the individual repetitions. The dashed black lines represent the envelope of the bounding boxes created from the pointwise confidence bounds (95% confidence).
Fig. 7
Fig. 7
ROC curve (solid black line) for the FV C classifier. The grey lines represent the ROC curves calculated on each of the individual repetitions. The dashed black lines represent the envelope of the bounding boxes created from the pointwise confidence bounds (95% confidence).
Fig. 8
Fig. 8
ROC curve (solid black line) for the FEV1/FV C% classifier. The grey lines represent the ROC curves calculated on each of the individual repetitions. The dashed black lines represent the envelope of the bounding boxes created from the pointwise confidence bounds (95% confidence).
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