Application of automatic detection based on overnight airflow and blood oxygen in patients with sleep disordered breathing

European Archives of Oto-Rhino-Laryngology volume 278, pages873–881(2021)Cite this article

Abstract

Purpose

To explore the feasibility of automatic detection based on air flow and blood oxygen in patients with sleep disordered breathing.

Methods

This study proposes a new automated detection method for sleep disordered breathing based on overnight airflow and blood oxygen saturation (SaO2). In this regard, local range (LR) of the airflow was adopted to detect apnea events and the SaO2 sudden drops were used to help determine hypopnea events. Pearson correlation index was used to evaluate the relationship between the two automated methods (this study vs. Remlogic software) and the manual reports. Error and mean absolute error (MAE) were used to assess the two automated methods.

Results

For all patients, the apnea–hypopnea index (AHI), apnea index (AI) and hypopnea index (HI) for our automated scoring and manual reports were highly correlated (the Pearson correlation index were 0.996, 0.995 and 0.928, respectively, P < 0.001). However, HI for Remlogic automated scoring and clinical manual reports was poorly correlated (r = 0.316, P < 0.001). Compared with the manual reports, mean absolute error of AHI, AI and HI between the two automated methods (this study vs. Remlogic software) were statistically significant (P < 0.0001). Furthermore, among the three subgroups (group 1, AHI < 15/h, group 2, 15/h ≤ AHI < 30/h and group 3, AHI ≥ 30/h), the mean error and MAE of AHI between the two automated methods were also statistically significant (P < 0.01).

Conclusions

Generally, good agreements were shown between our automated detection and clinical reports. This procedure is robust and effective, which would significantly shorten the analysis time.

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Funding

This work was funded by the Science and Technology Commission of Shanghai Municipality of China (Grant no 17411962000) and also supported by the Health and Family Planning Commission of Shanghai Municipality of China (Grant no 2019SY059).

Author information

Author notes

  1. Jingjing Huang and Liujie Ren contribute equally to this paper.

Affiliations

  1. ENT Institute, Eye and ENT Hospital of Fudan University, Fenyang Road No. 83, Xuhui District, Shanghai, China

    Jingjing Huang, Liujie Ren, Tianyu Zhang & Haitao Wu

  2. FPRS Department, Eye and ENT Hospital of Fudan University, Shanghai, China

    Liujie Ren & Tianyu Zhang

  3. Department of Aeronautics and Astronautics, Fudan University, Shanghai, China

    Lifen Chen & Zirui Jia

Authors
  1. Jingjing Huang
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  2. Liujie Ren
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  3. Lifen Chen
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  4. Zirui Jia
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  5. Tianyu Zhang
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  6. Haitao Wu
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Contributions

All authors contributed to the study conception and design. JH and LR contribute equally to this paper. Material preparation, data collection and analysis were performed by JH, LR, LC, ZJ, TZ and HW. The first draft of the manuscript was written by JH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: JH, LR and LC; Methodology: LR and LC; Formal analysis and investigation: JH and ZJ; Writing-original draft preparation: JH and LR; Writing-review and editing: HW; Funding acquisition: HW; Resources: HW; Supervision: TZ.

Corresponding author

Correspondence to Haitao Wu.

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Conflict of interest

None of the above-mentioned authors has a conflict of interest regarding this publication.

Ethics approval

Our study was performed in accordance with Declaration of Helsinki and its amendments. The Ethics Committee of Shanghai Fudan University Affiliated Eye and ENT Hospital has confirmed that no ethical approval is required. All the procedures being performed were part of the routine care.

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This article is part of the Topical Collection on sleep apnea syndrome. Guest Editors: Manuele Casale, Rinaldi Vittorio.

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Huang, J., Ren, L., Chen, L. et al. Application of automatic detection based on overnight airflow and blood oxygen in patients with sleep disordered breathing. Eur Arch Otorhinolaryngol 278, 873–881 (2021). https://doi.org/10.1007/s00405-020-06008-5

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Keywords

  • Sleep apnea–hypopnea syndrome
  • Airflow record
  • Automated detection
  • Blood oxygen