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Inferring forced expiratory volume in 1 second (FEV1) from mobile ECG signals collected during quiet breathing
Maria T Nyamukuru1, Alix Ashare2, Kofi M Odame1
1Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.
Physiological Measurement
|February 26, 2025
Summary
A machine learning model can estimate lung function (FEV1) from a simple finger electrocardiogram (ECG) signal. This offers a low-effort method for asthma and COPD patients to monitor their condition at home.
Area of Science:
- Biomedical Engineering
- Cardiology
- Pulmonology
Background:
- Forced expiratory volume in one second (FEV1) is crucial for managing asthma and COPD.
- Current home FEV1 monitoring methods are often effort-dependent or use impractical devices.
Purpose of the Study:
- To investigate the feasibility of inferring FEV1 from single-lead ECG signals using a machine learning model.
- To explore a low-effort, mobile-based approach for FEV1 measurement.
Main Methods:
- A machine learning model was trained to predict FEV1 from 270-second ECG recordings.
- The model's predictions were validated against hospital-grade spirometry in 25 patients with obstructive respiratory disease.
Main Results:
- The model-inferred FEV1 showed a correlation coefficient (r) of 0.73 with spirometry measurements.
- A mean absolute percentage error of 23% and a bias of -0.08 were observed.
Conclusions:
- ECG signals contain valuable information for estimating FEV1.
- Further research with larger datasets may improve prediction accuracy.
- An ECG-based mobile solution could enhance patient adherence and self-management for respiratory diseases.
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