Diagnosis of the Hypopnea syndrome in the early stage

  • Xiaodong YangEmail author
  • Dou Fan
  • Aifeng Ren
  • Nan Zhao
  • Syed Aziz Shah
  • Akram Alomainy
  • Masood Ur-Rehman
  • Qammer H. Abbasi
Intelligent Biomedical Data Analysis and Processing


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.


Hypopnea syndrome Respiration sensor Early warning Biomedical engineering Machine learning 



Funding was provided by International Scientific and Technological Cooperation and Exchange Projects in Shaanxi Province (Grant No. 2017KW-005); Fundamental Research Funds for the Central Universities (JB180205); China Postdoctoral Science Foundation funded project (Grant No. 2018T111023); National Natural Science Foundation of China (Grant No. 61301175); National Natural Science Foundation of China (Grant No. 61671349).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest in this work.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic EngineeringXidian UniversityXi’anChina
  2. 2.School of Electrical Engineering and Computer ScienceQueen Mary University of LondonLondonUK
  3. 3.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK
  4. 4.School of EngineeringUniversity of GlasgowGlasgowUK

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