Diagnosis of the Hypopnea syndrome in the early stage

Xiaodong Yang, Dou Fan, Aifeng Ren, Nan Zhao, Syed Shah, Akram Alomainy , Masood Ur-Rehman, Qammer H. Abbasi

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Hypopnea syndrome is a chronic respiratory disease that is characterized by repetitive episodes of breathing disruptions during sleep. Hypopnea syndrome is a systemic disease that manifests respiratory problems; however, more than 80% of Hypopnea syndrome patients remain undiagnosed due to complicated polysomnography. Objective assessment of breathing patterns of an individual can provide useful insight into the respiratory function unearthing severity of Hypopnea syndrome. This paper explores a novel approach to detect incognito Hypopnea syndrome as well as provide a contactless alternative to traditional medical tests. The proposed method is based on S-Band sensing technique (including a spectrum analyzer, vector network analyzer, antennas, software-defined radio, RF generator, etc.), peak detection algorithm and Sine function fitting for the observation of breathing patterns and characterization of normal or disruptive breathing patterns for Hypopnea syndrome detection. The proposed system observes the human subject and changes in the channel frequency response caused by Hypopnea syndrome utilizing a wireless link between two monopole antennas, placed 3 m apart. Commercial respiratory sensors were used to verify the experimental results. By comparing the results, it is found that for both cases, the pause time is more than 10 s with 14 peaks. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate Hypopnea syndrome monitoring in a patient-friendly and flexible environment.

Original languageEnglish
Pages (from-to) 855–866
Number of pages12
JournalNeural Computing and Applications
Volume32
Issue number3
Early online date6 Feb 2019
DOIs
Publication statusPublished - 1 Feb 2020
Externally publishedYes

Keywords

  • Hypopnea syndrome
  • Respiration sensor
  • Early warning
  • Biomedical engineering
  • Machine learning

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