Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome

Muhammad Bilal Khan, Najah AbuAli, Mohammad Hayajneh, Farman Ullah, Mobeen Ur Rehman, Kil To Chong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

Original languageEnglish
Pages (from-to)14-24
Number of pages11
JournalMethods
Volume218
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Artificial intelligence
  • SDRF sensing
  • Sleep apnea syndrome
  • WCSI

ASJC Scopus subject areas

  • Molecular Biology
  • General Biochemistry,Genetics and Molecular Biology

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