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Diagnostics
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Physiological Sound Processing for Medical Diagnostics: Innovations, Challenges, and Applications
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Physiological Sound Processing for Medical Diagnostics: Innovations, Challenges, and Applications

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 382

Special Issue Editors

Dr. Oliver Faust

E-Mail Website
Guest Editor
School of Computing and Information Science, Anglia Ruskin University, East Road, Cambridge CB1 1PT, UK
Interests: artificial intelligence; formal methods; biomedical signal processing; service based healthcare; intelligent internet of medical things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Prabal Datta Barua

E-Mail Website
Guest Editor
School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Interests: deep learning; neural networks; artificial intelligence; data science; information and communication

Special Issue Information

Dear Colleagues,

Physiological sound processing is an emerging field that could revolutionize non-invasive diagnostics. The analysis of physiological sounds, such as heartbeats, lung sounds, coughs, and snoring, provides valuable insights into a wide range of medical conditions. With advancements in artificial intelligence (AI) and signal processing, these sounds can now be analyzed with unprecedented accuracy, enabling early disease detection, patient monitoring, and treatment planning.

This Special Issue aims to provide an overview of recent developments in physiological sound processing for medical diagnostics, focusing on innovative methodologies, real-world applications, and clinical integration. By compiling interdisciplinary contributions from engineers, clinicians, and researchers, we aim to bridge the gap between technological innovation and practical implementation in healthcare.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Heart Sounds: AI-driven auscultation techniques for detecting arrhythmias, murmurs, and valvular diseases.
  • Lung Sounds: Advanced signal processing methods for diagnosing asthma, COPD, pneumonia, and other respiratory conditions.
  • Cough Analysis: Automated tools for differentiating between viral, bacterial, and chronic respiratory diseases, including tuberculosis and COVID-19.
  • Sleep Apnea and Snoring: Acoustic analysis for detecting and characterizing sleep-related breathing disorders.
  • Wearable Devices: The development and validation of mobile and wearable technologies for capturing and processing physiological sounds.
  • Explainability and Ethical AI: Designing explainable AI models for sound diagnostics and addressing ethical challenges in clinical deployment.
  • Standardization and Clinical Validation: Efforts to standardize datasets, protocols, and clinical trials for sound-based diagnostics.

Dr. Oliver Faust
Prof. Dr. Prabal Datta Barua
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • heart sounds
  • lung sounds
  • cough analysis
  • sleep apnea and snoring
  • wearable devices
  • explainability and ethical AI
  • standardization and clinical validation

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Published Papers (1 paper)

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Research

29 pages, 1415 KiB  
Article
Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals
by Shuting Xu, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar and Rajendra Acharya
Diagnostics 2025, 15(9), 1155; https://doi.org/10.3390/diagnostics15091155 - 1 May 2025
Viewed by 214
Abstract
Background and objective: Chronic respiratory diseases, such as asthma and COPD, pose significant challenges to human health and global healthcare systems. This pioneering study utilises AI analysis and modelling of cough and respiratory sound signals to classify and differentiate between asthma, COPD, and [...] Read more.
Background and objective: Chronic respiratory diseases, such as asthma and COPD, pose significant challenges to human health and global healthcare systems. This pioneering study utilises AI analysis and modelling of cough and respiratory sound signals to classify and differentiate between asthma, COPD, and healthy subjects. The aim is to develop an AI-based diagnostic system capable of accurately distinguishing these conditions, thereby enhancing early detection and clinical management. Our study, therefore, presents the first AI system that leverages dual acoustic signals to enhance the diagnostic ACC of asthma using automated, lightweight deep learning models. Methods: To build an automated, lightweight model for asthma detection, tested separately with respiratory and cough sounds to assess their suitability for detecting asthma and COPD, the proposed AI models integrate the following ML algorithms: RF, SVM, DT, NN, and KNN, with an overall aim to demonstrate the efficacy of the proposed method for future clinical use. Model training and validation were performed using 5-fold cross-validation, wherein the dataset was randomly divided into five folds and the models were trained and tested iteratively to ensure robust performance. We evaluated the model outcomes with several performance metrics: ACC, precision, recall, F1 score, and area under the AUC. Additionally, a majority voting ensemble technique was employed to aggregate the predictions of the various classifiers for improved diagnostic reliability. We applied Gabor time–frequency transformation for feature extraction and NCA) for feature selection to optimise predictive accuracy. Independent comparative experiments were conducted, where cough-sound subsets were used to evaluate asthma detection capabilities, and respiratory-sound subsets were used to evaluate COPD detection capabilities, allowing for targeted model assessment. Results: The proposed ensemble approach, facilitated by a majority voting approach for model efficacy evaluation, achieved acceptable ACC values of 94.05% and 83.31% for differentiating between asthma and normal cases utilising separate respiratory sounds and cough sounds, respectively. The results highlight a substantial benefit in integrating multiple classifier models and sound modalities while demonstrating an unprecedented level of ACC and robustness for future diagnostic predictions of the disease. Conclusions: The present study sets a new benchmark in AI-based detection of respiratory diseases by integrating cough and respiratory sound signals for future diagnostics. The successful implementation of a dual-sound analysis approach promises advancements in the early detection and management of asthma and COPD.We conclude that the proposed model holds strong potential to transform asthma diagnostic practices and support clinicians in their respiratory healthcare practices. Full article
(This article belongs to the Special Issue Physiological Sound Processing for Medical Diagnostics: Innovations, Challenges, and Applications)
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