Leveraging intrinsic non-sinusoidal patterns to infer search behavior to predict exposure to respiratory stressors

2025/05/27 by Anju Bimal, Szilard L. Beres, Victoria Ribeiro Rodrigues +4
Medicine · #Chronic Obstructive Pulmonary Disease (COPD) Research #Inhalation and Respiratory Drug Delivery #Respiratory Support and Mechanisms

paper · doi:10.1152/ajplung.00379.2024

openalex publication_date 2025/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Entropy-based metrics analyzing respiratory phase transitions (inspiration-to-expiration and expiration-to-inspiration) detect respiratory compromise under hypoxic conditions better than standard breathing rate measurements. Analysis of nonlinear dynamics during these transitions reveals key ventilatory adaptations during exposure to respiratory stressors. Measuring timing variations at phase transitions improves predictive model performance in detecting exposure to hypoxic environments by a 50.76% increase in area under the curve (AUC) vs. classical methods.

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