Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation
Wolfgang Mayr,1,* Andreas Triantafyllopoulos,2,3,* Anton Batliner,2,3 Björn W Schuller,2– 5 Thomas M Berghaus1,6 1Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany; 2Chair of Health Informatics (CHI), Department of Clin...
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Dove Medical Press
2025-01-01
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| author | Mayr W Triantafyllopoulos A Batliner A Schuller BW Berghaus TM |
| author_facet | Mayr W Triantafyllopoulos A Batliner A Schuller BW Berghaus TM |
| author_sort | Mayr W |
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| description | Wolfgang Mayr,1,* Andreas Triantafyllopoulos,2,3,* Anton Batliner,2,3 Björn W Schuller,2– 5 Thomas M Berghaus1,6 1Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany; 2Chair of Health Informatics (CHI), Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; 3Munich Center for Machine Learning (MCML), Munich, Germany; 4Group on Language Audio, & Music (GLAM), Imperial College, London, UK; 5Munich Data Science Institute (MDSI), Munich, Germany; 6Medical Faculty, Ludwig Maximilians University of Munich, Munich, Germany*These authors contributed equally to this workCorrespondence: Wolfgang Mayr, Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Stenglinstrasse 2, Augsburg, D-86156, Germany, Email wolfgang.mayr@uk-augsburg.de Andreas Triantafyllopoulos, Chair of Health Informatics, Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, Munich, 81675, Germany, Email andreas.triantafyllopoulos@tum.deBackground: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation. We extracted a set of spectral, prosodic, and temporal variability features, which were used as input to a support vector machine (SVM). Our baseline for predicting patient state was an SVM model using self-reported BORG and COPD Assessment Test (CAT) scores.Results: In 50 COPD patients (52% males, 22% GOLD II, 44% GOLD III, 32% GOLD IV, all patients group E), speech analysis was superior in distinguishing during and after exacerbation status compared to BORG and CAT scores alone by achieving 84% accuracy in prediction. CAT scores correlated with reading rhythm, and BORG scales with stability in articulation. Pulmonary function testing (PFT) correlated with speech pause rate and speech rhythm variability.Conclusion: Speech analysis may be a viable technology for classifying COPD status, opening up new opportunities for remote disease monitoring.Keywords: COPD, pathological speech, feature interpretation, personalization, digital health |
| format | Article |
| id | doaj-art-1b67d0935c0145efa738b97fd5a2ea90 |
| institution | Kabale University |
| issn | 1178-2005 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | International Journal of COPD |
| spelling | doaj-art-1b67d0935c0145efa738b97fd5a2ea902025-01-21T16:58:06ZengDove Medical PressInternational Journal of COPD1178-20052025-01-01Volume 2013714799371Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After ExacerbationMayr WTriantafyllopoulos ABatliner ASchuller BWBerghaus TMWolfgang Mayr,1,* Andreas Triantafyllopoulos,2,3,* Anton Batliner,2,3 Björn W Schuller,2– 5 Thomas M Berghaus1,6 1Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany; 2Chair of Health Informatics (CHI), Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; 3Munich Center for Machine Learning (MCML), Munich, Germany; 4Group on Language Audio, & Music (GLAM), Imperial College, London, UK; 5Munich Data Science Institute (MDSI), Munich, Germany; 6Medical Faculty, Ludwig Maximilians University of Munich, Munich, Germany*These authors contributed equally to this workCorrespondence: Wolfgang Mayr, Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Stenglinstrasse 2, Augsburg, D-86156, Germany, Email wolfgang.mayr@uk-augsburg.de Andreas Triantafyllopoulos, Chair of Health Informatics, Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, Munich, 81675, Germany, Email andreas.triantafyllopoulos@tum.deBackground: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation. We extracted a set of spectral, prosodic, and temporal variability features, which were used as input to a support vector machine (SVM). Our baseline for predicting patient state was an SVM model using self-reported BORG and COPD Assessment Test (CAT) scores.Results: In 50 COPD patients (52% males, 22% GOLD II, 44% GOLD III, 32% GOLD IV, all patients group E), speech analysis was superior in distinguishing during and after exacerbation status compared to BORG and CAT scores alone by achieving 84% accuracy in prediction. CAT scores correlated with reading rhythm, and BORG scales with stability in articulation. Pulmonary function testing (PFT) correlated with speech pause rate and speech rhythm variability.Conclusion: Speech analysis may be a viable technology for classifying COPD status, opening up new opportunities for remote disease monitoring.Keywords: COPD, pathological speech, feature interpretation, personalization, digital healthhttps://www.dovepress.com/assessing-the-clinical-and-functional-status-of-copd-patients-using-sp-peer-reviewed-fulltext-article-COPDcopdpathological speechfeature interpretationpersonalizationdigital health |
| spellingShingle | Mayr W Triantafyllopoulos A Batliner A Schuller BW Berghaus TM Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation International Journal of COPD copd pathological speech feature interpretation personalization digital health |
| title | Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation |
| title_full | Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation |
| title_fullStr | Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation |
| title_full_unstemmed | Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation |
| title_short | Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation |
| title_sort | assessing the clinical and functional status of copd patients using speech analysis during and after exacerbation |
| topic | copd pathological speech feature interpretation personalization digital health |
| url | https://www.dovepress.com/assessing-the-clinical-and-functional-status-of-copd-patients-using-sp-peer-reviewed-fulltext-article-COPD |
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