دورية أكاديمية
Physically Meaningful Surrogate Data for COPD
| العنوان: | Physically Meaningful Surrogate Data for COPD |
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| المؤلفون: | Harry J. Davies, Ghena Hammour, Hongjian Xiao, Patrik Bachtiger, Alexander Larionov, Philip L. Molyneaux, Nicholas S. Peters, Danilo P. Mandic |
| المصدر: | IEEE Open Journal of Engineering in Medicine and Biology, Vol 5, Pp 148-156 (2024) |
| بيانات النشر: | IEEE, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Medical technology |
| مصطلحات موضوعية: | COPD, deep learning, photoplethysmography, surrogate data, wearable health, Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2644-1276 |
| Relation: | https://ieeexplore.ieee.org/document/10417113/; https://doaj.org/toc/2644-1276 |
| DOI: | 10.1109/OJEMB.2024.3360688 |
| URL الوصول: | https://doaj.org/article/9614f87448ae4814921b8b144b44fc52 |
| رقم الأكسشن: | edsdoj.9614f87448ae4814921b8b144b44fc52 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26441276 |
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| DOI: | 10.1109/OJEMB.2024.3360688 |