Journal of Medical Systems

, 40:282 | Cite as

New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal

  • Hyoki Lee
  • Jonguk Park
  • Hojoong Kim
  • Kyoung-Joung LeeEmail author
Transactional Processing Systems
Part of the following topical collections:
  1. Transactional Processing Systems


We developed a rule-based algorithm for automatic real-time detection of sleep apnea and hypopnea events using a nasal pressure signal. Our basic premise was that the performance of our new algorithm using the nasal pressure signal would be comparable to that using other sensors as well as manual annotation labeled by a technician on polysomnography study. We investigated fifty patients with sleep apnea-hypopnea syndrome (age: 56.8 ± 10.5 years, apnea-hypopnea index (AHI): 36.2 ± 18.1/h) during full night PSG recordings at the sleep center. The algorithm was comprised of pre-processing with a median filter, amplitude computation and apnea-hypopnea detection parts. We evaluated the performance of the algorithm a confusion matric for each event and statistical analyses for AHI. Our evaluation achieved a good performance, with a sensitivity of 86.4 %, and a positive predictive value of 84.5 % for detection of apnea and hypopnea regardless of AHI severity. Our results indicated a high correlation with the manually labeled apnea-hypopnea events during PSG, with a correlation coefficient of r = 0.94 (p < 0.0001) and a mean difference of −2.9 ± 11.6 per hour. The proposed new algorithm could provide significant clinical and computational insights to design a PSG analysis system and a continuous positive airway pressure (CPAP) device for screening sleep quality related in patients with sleep apnea-hypopnea syndrome.


Real-time detection Apnea Hypopnea Nasal pressure signal 



This work was supported by the Human Resource Training Program for Regional Innovation and Creativity through the Ministry of Education and National Research Foundation of Korea (NRF-2014H1C1A1063845). This work was supported in part by the Yonsei University Research Fund of 2014.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Hyoki Lee
    • 1
  • Jonguk Park
    • 2
  • Hojoong Kim
    • 3
  • Kyoung-Joung Lee
    • 2
    Email author
  1. 1.Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of SurgeryBaylor College of MedicineHoustonUSA
  2. 2.Department of Biomedical EngineeringYonsei UniversityWonjuRepublic of Korea
  3. 3.Division of Pulmonary and Critical Care Medicine, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea

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