  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. We 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.
