Physically meaningful surrogate data for COPD
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV1/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV1/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.
Date Issued
2024-02-23
Date Acceptance
2024-01-26
Citation
IEEE Open J Eng Med Biol, 2024, 5, pp.148-156
ISSN
2644-1276
Publisher
IEEE
Start Page
148
End Page
156
Journal / Book Title
IEEE Open J Eng Med Biol
Volume
5
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/38487098
Subjects
COPD
deep learning
photoplethysmography
surrogate data
wearable health
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2024-01-31