Method Article

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

DOI:

10.3791/68222

⸱

September 19th, 2025

 ,  ,  ,  ,  ,  ,  ,  ,  , 
1Beijing University of Chinese Medicine, 2Institute of Information on Traditional Chinese Medicine, Chinese Academy of Traditional Chinese Medicine, 3Beijing University of Chinese Medicine Third Affiliated Hospital

In This Article

Summary

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This study effectively accomplished the automated classification of two distinct categories by acquiring cough sound data from patients diagnosed with chronic obstructive pulmonary disease (COPD) and respiratory tract infections (RTI), utilizing an integration of speech signal processing techniques and machine learning algorithms.

Abstract

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The objective of this study was to develop and evaluate a non-invasive method for distinguishing patients with chronic obstructive pulmonary disease (COPD) from those with respiratory tract infections (RTI) using voice signal analysis and machine learning. Fixed-pattern voice signals were collected from 25 COPD patients and 25 RTI patients (serving as the control/comparison group). Multi-dimensional voice feature analysis was performed to identify features significantly differentiating the two groups. Statistically significant features were selected and subjected to dimensionality reduction. Logistic Regression (LR) and Random Forest (RF) models were then trained and evaluated for classification performance in distinguishing COPD from RTI. Over 400 voice features were initially analyzed. Eighteen features showed highly significant differences between COPD and RTI patients (P <; 0.05). In the task of distinguishing COPD patients from RTI patients, the LR model achieved a test set area under the curve AUC of 0.95, significantly outperforming the RF model (AUC = 0.76). This study demonstrates the feasibility of using voice analysis and machine learning, particularly the LR model, as a promising non-invasive tool for differentiating COPD from RTI. It provides a foundation for the practical application and further optimization of this voice-based approach in clinical settings requiring differential diagnosis of respiratory conditions.

Introduction

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Chronic obstructive pulmonary disease (COPD) and respiratory tract infections represent significant contributors to mortality and morbidity on a global scale. COPD is defined as a chronic inflammatory condition affecting the airways and lung parenchyma, predominantly induced by smoking. It is characterized by symptoms such as persistent cough, dyspnea, and increased sputum production1. The World Health Organization projects that by 2030, COPD will rank as the third leading cause of death worldwide, imposing a substantial economic burden2,3. In contrast, respiratory tract infections (RTI....

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Protocol

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The Ethics Committee of Beijing University of Chinese Medicine and its Third Affiliated Hospital approved this research study. All participants provided their written informed consent to participate. Between July and August 2024, a cohort of 25 COPD patients was recruited from the Respiratory Medicine Department at the Third Affiliated Hospital of Beijing University of Chinese Medicine. Simultaneously, a control group consisting of 25 patients with typical upper RTI was also assembled.

1. Participant selection

  1. Inclusion criteria
    1. Select the audio samples with low background noise and clear articulation.
    2. Ens....

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Results

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Data analysis results

The research successfully isolated over 400 speech feature indexes using methods like time domain analysis, frequency domain analysis, extraction of Mel-frequency Cepstral Coefficient (MFCC), and altering feature indicators according to TCM diagnosis. Analyzing the time domain is a crucial element in speech signal processing, focusing on the direct manipulation of signal time series data to.......

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Discussion

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This study investigates non-invasive methods for detecting COPD through voice signal analysis and machine learning techniques. It involved collecting voice data from 25 COPD patients and 25 patients with RTI. Models were constructed using LR and RF algorithms. Both models showed similar accuracy in correctly classifying samples overall, yet the difference in AUC values indicates that the LR model might offer a superior balance between sensitivity and specificity. The subsequent sections provide a detailed examination of .......

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Disclosures

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The authors declare no conflicts of interest regarding the publication of this study. No financial or non-financial support was received from any commercial organization that could have influenced the results or interpretation of this research. All aspects of the study, including design, data collection, analysis, and manuscript preparation, were conducted independently of any external influence.

Acknowledgements

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This study was supported by the National Natural Science Foundation of China Youth Science Fund Project (Project Approval Number: 82104739) and the Scientific Research Program of the Hebei Provincial Administration of Traditional Chinese Medicine (Project Number: B2025032). The authors would like to thank all the teachers and students who provided assistance during the experiment.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Digital RecorderZOOMH6ZOOM Audio Store
GitHubGit2.47.1.2Official Website
MatlabMathWorksR2024bOfficial Website
PycharmJetBrains2024.1Official Website
PythonPython3.12Official Website

References

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  1. Roth, M. Pathogenesis of COPD. Part III. Inflammation in COPD. Int J Tuberc Lung Dis. 12 (4), 375-380 (2008).
  2. Alsayari, A., Muhsinah, A. B., Almaghaslah, D., Annadurai, S., Wahab, S. Pharmacological efficacy of ginseng against respiratory tract infections. Molecules.

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Tags

Cough Tone ClassificationMachine Learning DiagnosticsVoice Signal AnalysisChronic Obstructive PulmonaryRespiratory Tract InfectionsLogistic Regression ModelRandom Forest ModelPrincipal Component AnalysisVoice Feature ExtractionNon Invasive Diagnosis

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