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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Accumulated bispectral image-based respiratory sound signal classification using deep learning.

Sandeep B Sangle1, Chandrakant J Gaikwad1

  • 1Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University, Nerul, Navi Mumbai, Maharashtra 400706 India.

Signal, Image and Video Processing
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Summary

This study introduces a novel method using accumulated bi-spectral features and AI algorithms to detect COVID-19 from respiratory sounds. The approach shows promising accuracy in distinguishing between healthy and COVID-19 infected individuals.

Keywords:
Accumulated bispectrumBispectral imageCNNCOVID-19

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Area of Science:

  • Medical Signal Processing
  • Artificial Intelligence in Healthcare
  • Respiratory Medicine

Background:

  • The COVID-19 pandemic necessitates rapid and accessible diagnostic tools.
  • Respiratory sound analysis offers a non-invasive method for disease detection.
  • Emerging SARS-CoV-2 variants highlight the need for continuous monitoring and diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel method for COVID-19 detection using respiratory sounds.
  • To analyze the efficacy of accumulated bi-spectral features in classifying respiratory sounds.
  • To compare the performance of Convolutional Neural Network (CNN) and ResNet-50 algorithms for COVID-19 classification.

Main Methods:

  • Respiratory sounds were analyzed using novel accumulated bi-spectral features derived from the principal domain bispectrum.
  • Bispectral images were generated from the magnitude bispectrum.
  • Convolutional Neural Network (CNN) and ResNet-50 models were designed and trained to classify sounds as COVID-19 positive or healthy.
  • Performance was benchmarked against existing state-of-the-art methods.

Main Results:

  • The proposed CNN-based method achieved a high accuracy of 87.68% for shallow breath sounds.
  • ResNet-50 demonstrated a high accuracy of 87.62% for deep breath sounds.
  • The implemented methods showed improved performance for various types of respiratory sounds compared to baseline approaches.

Conclusions:

  • Accumulated bi-spectral features combined with CNN and ResNet-50 offer a viable approach for non-invasive COVID-19 detection.
  • The study highlights the potential of AI-driven respiratory sound analysis in public health screening.
  • Further research can explore broader applications and refine algorithms for enhanced diagnostic accuracy.