Fractional Dynamics Foster Deep Learning of COPD Stage Prediction. Issue 12 (19th February 2023)

• Record Type: Journal Article
• Authors: Yin, Chenzhong; Udrescu, Mihai; Gupta, Gaurav; Cheng, Mingxi; Lihu, Andrei; Udrescu, Lucretia; Bogdan, Paul; Mannino, David M.; Mihaicuta, Stefan
• Is Part Of: Advanced science. Volume 10:Issue 12(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-19
• Subjects: chronic obstructive pulmonary disease (COPD) -- deep learning -- fractional analysis; Science -- Periodicals; 505
• DOI: 10.1002/advs.202203485
• Languages: English
• ISSNs: 2198-3844

Abstract

Abstract: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional‐order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional‐order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages—from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals. Abstract : Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide, usually associated with smoking and environmental occupational exposures. The COPD detection problem is addressed by constructing two novel COPD physiological signals datasets, demonstrating their complex coupled fractal dynamical characteristics, and performing a rigorous fractional‐order dynamics deep learning analysis to diagnose COPD with high accuracy.