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Automatic Lung Health Screening Using Respiratory Sounds.

Himadri Mukherjee1, Priyanka Sreerama2, Ankita Dhar1

  • 1Department of Computer Science, West Bengal State University, Kolkata, India.

Journal of Medical Systems
|January 11, 2021
PubMed
Summary
This summary is machine-generated.

This study developed a tool using Linear Predictive Cepstral Coefficient (LPCC) features and a Multilayer Perceptron (MLP) classifier to detect respiratory infections from audio data, achieving 99.22% accuracy.

Keywords:
HealthcareLung healthRespiratory infectionRespiratory sound

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Area of Science:

  • Medical Technology
  • Bioacoustics
  • Artificial Intelligence in Healthcare

Background:

  • Respiratory sound analysis is a growing field in healthcare.
  • Developing automated tools for respiratory infection detection is crucial for early diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel tool for detecting respiratory sounds indicative of infection.
  • To assess the efficacy of Linear Predictive Cepstral Coefficient (LPCC) features and Multilayer Perceptron (MLP) classification for this task.

Main Methods:

  • Audio clips of respiratory sounds were analyzed.
  • Linear Predictive Cepstral Coefficient (LPCC) features were extracted to characterize the audio data.
  • A Multilayer Perceptron (MLP) classifier was employed for detection.

Main Results:

  • The developed tool achieved a high accuracy of 99.22% on the ICBHI17 dataset.
  • The results demonstrated superior performance compared to other machine learning classifiers and existing literature.
  • The system successfully identified respiratory sounds from patients with respiratory infections.

Conclusions:

  • The proposed LPCC and MLP-based approach is highly effective for automated respiratory infection detection from audio.
  • This technology holds significant potential for non-invasive health screening and diagnosis.