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Apnea detection based on time delay neural network.

J Tian1, J Liu

  • 1Department of Electrical Engineering, Harbin Institute of Technology, Harbin, 150001 P.R. China (phone: 0086-136-71161368;

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
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This study introduces an artificial neural network for detecting sleep apnea syndrome (SAS). The novel method shows high accuracy in identifying apnea and hypopnea events from polysomnographic data.

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Sleep Medicine

Background:

  • Sleep apnea syndrome (SAS) is a prevalent sleep disorder requiring accurate detection for effective treatment.
  • Current diagnostic methods for SAS rely on polysomnography (PSG), which can be time-consuming and complex.
  • Reliable and automated detection of respiratory events is crucial for timely diagnosis and management of SAS.

Purpose of the Study:

  • To propose a novel artificial neural network-based method for the reliable detection of sleep apnea and hypopnea events.
  • To utilize the temporal trends of apnea events by employing a time-delay neural network (TDNN).
  • To integrate features from airflow and SaO2 signals for enhanced diagnostic accuracy in SAS detection.

Main Methods:

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  • A time-delay neural network (TDNN) was developed to analyze temporal patterns in sleep apnea events.
  • Features extracted from airflow and SaO2 signals were simultaneously fed into the TDNN.
  • The algorithm was validated using 15 overnight polysomnographic (PSG) records.

    • Main Results:

    • The proposed algorithm achieved a sensitivity of 90.7% for apnea and 80.8% for hypopnea.
    • Specificity rates were 86.4% for apnea and 81.4% for hypopnea.
    • The system demonstrated robustness to potential airflow sensor failures and is suitable for offline SAS diagnosis.

    Conclusions:

    • The developed artificial neural network method provides accurate and reliable detection of sleep apnea and hypopnea.
    • Integration of SaO2 data, while enhancing detection, limits the method to offline analysis due to signal delay.
    • This algorithm serves as a valuable, time-saving tool for adjunct diagnosis of SAS in clinical settings.