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

Abstract

Objective.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.Methods.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.Results.The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient ofr = 0.73, a mean absolute percentage error of 23% and a bias of -0.08.Conclusions.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.Significance.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.