Inferring COPD Severity from Tidal Breathing

TL;DR

This study develops a wearable device-based algorithm that accurately classifies COPD severity from tidal breathing data, offering a non-invasive method for assessing airflow limitation.

Contribution

The paper introduces a novel classification model that infers COPD severity from tidal breathing signals collected by a wearable device, achieving high accuracy.

Findings

Classification accuracy of 96.4% for COPD severity levels

Tidal breathing parameters can distinguish airflow limitation levels

Wearable device data correlates well with spirometry results

Abstract

Objective: To develop an algorithm that can infer the severity level of a COPD patient's airflow limitation from tidal breathing data that is collected by a wearable device. Methods: Data was collected from 25 single visit adult volunteers with a confirmed or suspected diagnosis of chronic obstructive pulmonary disease (COPD). The ground truth airflow limitation severity of each subject was determined by applying the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging criteria to the subject's spirometry results. Spirometry was performed in a pulmonary function test laboratory under the supervision of trained clinical staff. Separately, the subjects' respiratory signal was measured during quiet breathing, and a classification model was built to infer the subjects' level of airflow limitation from the measured respiratory signal. The classification model was evaluated against the ground truth using leave-one-out testing. Results: Severity of airway obstruction was classified as either mild/moderate or severe/very severe with an accuracy of 96.4%. Conclusion: Tidal breathing parameters that are measured with a wearable device can be used to distinguish between different levels of airflow limitation in COPD patients.