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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.

José Antonio Rivas-Navarrete1, Humberto Pérez-Espinosa2, A L Padilla-Ortiz3,4

  • 1CICESE-UAT, CICESE, Andador 10 # 109, Tepic, 63173, Nayarit, México. jrivas@cicese.edu.mx.

Journal of Medical Systems
|March 4, 2025
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Summary

This study developed an AI-powered edge computing system for detecting chronic respiratory diseases (CRDs) using cough and breath sounds. The system achieved high accuracy, offering a potential low-cost screening tool for remote areas.

Keywords:
CDRCOPDEdge computingMachine learning

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Respiratory Medicine

Background:

  • Chronic respiratory diseases (CRDs) pose a global health challenge, with diagnostic limitations in remote areas.
  • Respiratory sounds contain acoustic features indicative of CRDs.
  • Artificial intelligence (AI) and edge computing offer novel diagnostic avenues.

Purpose of the Study:

  • To develop and evaluate an audio-based edge computing system for autonomous detection of CRDs.
  • To utilize machine learning (ML) for analyzing respiratory sounds (cough and breath).
  • To assess the system's performance on edge devices like smartphones and Raspberry Pi.

Main Methods:

  • Development of an edge computing system employing ML algorithms.
  • Analysis of respiratory sound features, including Mel frequency cepstral coefficients (MFCC) and chromagrams.
  • Training and testing on a dataset of 86 individuals (53 with CRDs, 33 healthy), with final evaluation on 35 individuals.

Main Results:

  • The system demonstrated high sensitivity (90.0%) and specificity (93.55%) for CRD detection.
  • Achieved a balanced accuracy of 91.75% in classifying healthy and diseased individuals.
  • Successful classification of respiratory sounds on edge devices.

Conclusions:

  • Edge computing and ML systems show significant potential for respiratory disease detection.
  • The developed system can serve as an efficient and cost-effective screening tool.
  • This technology can improve accessibility to diagnostics for CRDs, especially in underserved regions.