Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity

Abstract

We consider the task of automatically predicting spirometry readings from cough and wheeze audio signals for asthma severity monitoring. Spirometry is a pulmonary function test used to measure forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) when a subject exhales in the spirometry sensor after taking a deep breath. FEV1%, FVC% and their ratio are typically used to determine the asthma severity. Accurate prediction of these spirometry readings from cough and wheeze could help patients to non-invasively monitor their asthma severity in the absence of spirometry. We use statistical spectrum description (SSD) as the cue from cough and wheeze signal to predict the spirometry readings using support vector regression (SVR). We perform experiments with cough and wheeze recordings from 16 healthy persons and 12 patients. We find that the coughs are better predictor of spirometry readings compared to the wheeze signal. FEV1%, FVC% and their ratio are predicted with root mean squared error of 11.06%, 10.3% and 0.08 respectively. We also perform a three class asthma severity level classification with predicted FEV1% and obtain an accuracy of 77.77%.