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Human respiratory syncytial virus (RSV) is a widespread pathogen that primarily targets infants and young children but also poses a serious health risk to elderly and immunocompromised individuals. Belonging to the Pneumoviridae family, RSV is a negative-sense, single-stranded RNA virus within the Pneumovirus genus. Its global health burden is significant, with millions of cases annually resulting in hospitalizations and mortality, particularly in resource-limited settings. Although most...
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Related Experiment Video

Updated: Jul 16, 2026

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

Published on: September 19, 2025

Respiratory Disease Classification Using NMF-Enhanced Log-Mel Spectrograms and Convolutional Recurrent Neural

Bowen Han1, Wei Quan1, Bogdan Matuszewski1

  • 1School of Engineering and Computing, University of Lancashire, Preston PR1 2HE, UK.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces a new method for classifying respiratory diseases using lung sounds. The NMF-enhanced deep learning model accurately identifies conditions like Asthma, COPD, and Pneumonia from audio recordings.

Keywords:
computer-assisted auscultationconvolutional recurrent neural networklog-mel spectrogramnon-negative matrix factorizationrespiratory disease classification

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

  • Medical acoustics
  • Artificial intelligence in healthcare
  • Signal processing for biomedical applications

Background:

  • Respiratory disease classification from lung sounds is difficult due to signal noise and overlapping acoustic patterns.
  • Existing methods struggle with the heterogeneity of audio data and diverse disease presentations.

Purpose of the Study:

  • To develop and evaluate a novel framework for multi-class respiratory disease classification using enhanced lung sound analysis.
  • To improve the accuracy and robustness of automated detection for conditions including Asthma, COPD, Pneumonia, and others.

Main Methods:

  • Lung sound recordings from public datasets were harmonized into a unified seven-class label space.
  • A Non-negative Matrix Factorization (NMF) enhancement was applied to log-mel spectrograms for improved feature salience.
  • A Convolutional Recurrent Neural Network (CRNN) with attention was developed and compared against other deep learning architectures.

Main Results:

  • The proposed CRNN model achieved high performance, with 96.14% accuracy and 94.05% Macro-F1 score on the seven-class dataset.
  • The CRNN demonstrated superior class separation and more balanced recognition across different respiratory diseases compared to RDLINet, ResNet, and YOLO-style models.
  • Class-wise analysis and confusion matrix evaluation confirmed the CRNN's effectiveness.

Conclusions:

  • NMF-enhanced spectro-temporal modeling combined with CRNNs offers a powerful approach for automated multi-class respiratory disease classification.
  • This framework shows significant potential for clinical applications in diagnosing respiratory conditions from lung sound recordings.
  • The study highlights the benefits of advanced signal processing and deep learning for overcoming challenges in respiratory sound analysis.