Evaluation of smartphone-based cough data in amyotrophic lateral sclerosis as a potential predictor of functional disability.

Autor: Rocha PS; Institute of Physiology, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal.; Mamede de Carvalho Lab, Institute of Molecular Medicine - João Lobo Antunes, University of Lisbon, Lisbon, Portugal., Bento N; Fraunhofer Portugal AICOS, Lisbon, Portugal., Folgado D; Fraunhofer Portugal AICOS, Lisbon, Portugal.; LiBPhys (Laboratory for Instrumentation Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology, Lisbon, Portugal., Carreiro AV; Fraunhofer Portugal AICOS, Lisbon, Portugal., Santos MO; Institute of Physiology, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal.; Department of Neurosciences and Mental Health, Hospital de Santa Maria -CHULN, Lisbon, Portugal., de Carvalho M; Institute of Physiology, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal.; Mamede de Carvalho Lab, Institute of Molecular Medicine - João Lobo Antunes, University of Lisbon, Lisbon, Portugal.; Department of Neurosciences and Mental Health, Hospital de Santa Maria -CHULN, Lisbon, Portugal., Miranda B; Institute of Physiology, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal.; Mamede de Carvalho Lab, Institute of Molecular Medicine - João Lobo Antunes, University of Lisbon, Lisbon, Portugal.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Dec 16; Vol. 19 (12), pp. e0301734. Date of Electronic Publication: 2024 Dec 16 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0301734
Abstrakt: Objectives: Cough dysfunction is a feature of patients with amyotrophic lateral sclerosis (ALS). The cough sounds carry information about the respiratory system and bulbar involvement. Our goal was to explore the association between cough sound characteristics and the respiratory and bulbar functions in ALS.
Methods: This was a single-center, cross-sectional, and case-control study. On-demand coughs from ALS patients and healthy controls were collected with a smartphone. A total of 31 sound features were extracted for each cough recording using time-frequency signal processing analysis. Logistic regression was applied to test the differences between patients and controls, and in patients with bulbar and respiratory impairment. Support vector machines (SVM) were employed to estimate the accuracy of classifying between patients and controls and between patients with bulbar and respiratory impairment. Multiple linear regressions were applied to examine correlations between cough sound features and clinical variables.
Results: Sixty ALS patients (28 with bulbar dysfunction, and 25 with respiratory dysfunction) and forty age- and gender-matched controls were recruited. Our results revealed clear differences between patients and controls, particularly within the frequency-related group of features (AUC 0.85, CI 0.79-0.91). Similar results were observed when comparing patients with and without bulbar dysfunction. Sound features related to intensity displayed the strongest correlation with disease severity, and were the most significant in distinguishing patients with and without respiratory dysfunction.
Discussion: We found a good relationship between specific cough sound features and clinical variables related to ALS functional disability. The findings relate well with some expected impact from ALS on both respiratory and bulbar contributions to the physiology of cough. Finally, our approach could be relevant for clinical practice, and it also facilitates home-based data collection.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Rocha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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