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Diagnosis of the Hypopnea syndrome in the early stage

  • Intelligent Biomedical Data Analysis and Processing
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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.

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Funding

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).

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, Shaanxi, China

    Xiaodong Yang, Dou Fan, Aifeng Ren, Nan Zhao & Syed Aziz Shah

  2. School of Electrical Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK

    Akram Alomainy

  3. School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

    Masood Ur-Rehman

  4. School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK

    Qammer H. Abbasi

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  1. Xiaodong Yang
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  2. Dou Fan
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  3. Aifeng Ren
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  4. Nan Zhao
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  5. Syed Aziz Shah
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  6. Akram Alomainy
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  7. Masood Ur-Rehman
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  8. Qammer H. Abbasi
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Correspondence to Xiaodong Yang.

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Yang, X., Fan, D., Ren, A. et al. Diagnosis of the Hypopnea syndrome in the early stage. Neural Comput & Applic 32, 855–866 (2020). https://doi.org/10.1007/s00521-019-04037-8

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  • DOI: https://doi.org/10.1007/s00521-019-04037-8

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