Inferring forced expiratory volume in 1 second (FEV1) from mobile ECG signals collected during quiet breathing

<i>Objective.</i>Forced expiratory volume in one second (FEV1) is an important metric for patients to track at home for their self-management of asthma and chronic obstructive pulmonary disease (COPD). Unfortunately, the state-of-the-art for measuring FEV1 at home either depends on the patient's physical effort and motivation, or relies on bulky wearable devices that are impractical for long-term monitoring. This paper explores the feasibility of using a machine learning model to infer FEV1 from 270 seconds of a single-lead electrocardiogram (ECG) signal measured on the fingers with a mobile device.<i>Methods.</i>We evaluated the model's inferred FEV1 values against the ground truth of hospital-grade spirometry tests, which were performed by twenty-five patients with obstructive respiratory disease.<i>Results.</i>The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient of<i>r</i> = 0.73, a mean absolute percentage error of 23% and a bias of -0.08.<i>Conclusions.</i>These results suggest that the ECG signal contains useful information about FEV1, although a larger, richer dataset might be necessary to train a machine learning model that can extract this information with better accuracy.<i>Significance.</i>The benefit of a mobile ECG-based solution for measuring FEV1 is that it would require minimal effort, thus encouraging patient adherence and promoting successful self-management of asthma and COPD.