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Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer

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This study introduces a non-invasive Software Defined Radio (SDR) system using Radio-Frequency sensing to monitor respiratory patterns. The technology accurately detects various breathing types, crucial for predicting COVID-19 symptoms.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Non-invasive healthcare technologies are vital for remote patient monitoring, especially during pandemics like COVID-19.
  • Real-time monitoring of respiratory rate is crucial for identifying COVID-19 symptoms.
  • Traditional methods for respiratory monitoring can burden healthcare personnel.

Purpose of the Study:

  • To develop and evaluate a contactless system for detecting and classifying diverse respiratory patterns.
  • To leverage Software Defined Radio (SDR) and Radio-Frequency (RF) sensing for non-invasive respiratory monitoring.
  • To assess the efficacy of a supervised machine learning algorithm in identifying respiratory abnormalities.

Main Methods:

  • Utilized Software Defined Radio (SDR) for Radio-Frequency (RF) sensing to capture variations in Channel State Information (CSI) caused by respiration.
  • Employed fine-grained Orthogonal Frequency-Division Multiplexing (OFDM) signals to analyze respiratory-induced CSI variations.
  • Implemented a Deep Multilayer Perceptron (MLP) classifier for supervised machine learning to detect and classify respiratory patterns.

Main Results:

  • The proposed SDR-based platform achieved up to 99% accuracy in detecting and classifying various respiratory patterns, including eupnea, biot, bradypnea, sighing, tachypnea, and Kussmaul.
  • Demonstrated high performance in diagnosis accuracy, precision, recall, and F1-score.
  • Outperformed the Random Forest classifier in classifying distinct respiratory patterns.

Conclusions:

  • The developed SDR and Deep MLP system offers an effective, non-invasive solution for real-time respiratory monitoring.
  • This technology has significant potential for early detection and management of respiratory conditions, including those related to COVID-19.
  • The system provides a reliable and accurate method for classifying multiple respiratory patterns with high precision.