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Related Experiment Video

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An alternative respiratory sounds classification system utilizing artificial neural networks.

Rami J Oweis1, Enas W Abdulhay, Amer Khayal

  • 1Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid, Jordan.

Biomedical Journal
|September 3, 2014
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Summary

This study introduces an efficient method for computerized lung sound analysis using autocorrelation for feature extraction. Artificial neural networks (ANNs) achieved high accuracy (98.6%), outperforming other systems for respiratory sound classification.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computational Intelligence

Background:

  • Computerized lung sound analysis aids in diagnosing respiratory conditions by analyzing signal characteristics like non-linearity and nonstationarity.
  • Automated analysis is crucial to reduce reliance on subjective expert interpretation.
  • Air turbulence in airways is a primary cause of complex lung sound generation.

Purpose of the Study:

  • To develop an automated and efficient method for respiratory sound classification.
  • To explore the utility of autocorrelation in feature extraction for lung sound analysis.
  • To compare the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for this task.

Main Methods:

  • Lung sounds were recorded and analyzed using MATLAB.
  • Autocorrelation was employed for feature extraction from respiratory sounds.
  • Classification was performed comparatively using ANNs and ANFIS.

Main Results:

  • The Artificial Neural Network (ANN) demonstrated superior performance over the ANFIS system.
  • ANN achieved high performance metrics: 98.6% accuracy, 100% specificity, and 97.8% sensitivity.
  • The proposed method's parameters surpassed those of many recent approaches in lung sound analysis.

Conclusions:

  • The autocorrelation-based feature extraction method is an efficient and fast tool for respiratory sound classification.
  • The ANN model provides excellent performance, highlighting its suitability for automated lung sound analysis.
  • Utilizing autocorrelation enhances performance and simplifies computation compared to alternative techniques.