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"HeartFEV1: A Mobile ECG-Based System for Inferring FEV1 in COPD Patients"

Abstract

Chronic obstructive pulmonary disease (COPD), characterized by chronic airway inflammation and airflow obstruction, is the third leading cause of death globally. Patients with COPD experience exacerbated symptoms like breathlessness and cough, significantly impacting their quality of life and leading to costly hospitalizations. Early detection of COPD exacerbations is crucial for mitigating these negative effects. Monitoring lung function is the most critical element for predicting exacerbations. By tracking declines in lung function, healthcare providers can potentially predict exacerbations up to two weeks in advance, allowing for timely interventions and potentially reducing hospital admissions. However, at-home spirometry, the gold standard for lung function monitoring, requires a physically demanding forced breathing maneuver, posing limitations for some patients.

This thesis introduces HeartFEV1, a novel system that addresses this challenge. HeartFEV1 leverages readily available mobile ECG signals acquired during tidal breathing for non-invasive FEV1 estimation (FEV1 being a key metric of lung function). By combining machine learning and deep learning approaches, HeartFEV1 offers a convenient and non-invasive method for monitoring lung function and potentially detecting early signs of COPD exacerbations, ultimately improving COPD management.

Thesis Committee

• Prof. Kofi Odame (chair)
• Prof. Ryan Halter
• Prof. Vikrant Vaze
• Prof. Peter Charlton (University of Cambridge)

Contact

For more information, contact Thayer Registrar at thayer.registrar@dartmouth.edu.