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A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool.

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Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine
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A novel artificial neural network tool effectively screens for sleep-disordered breathing (SDB) using pulse oximetry and clinical data. This accurate screening method shows high sensitivity for detecting SDB across various apnea-hypopnea index thresholds.

Keywords:
artificial neural networkgeneral populationscreeningsleep-disordered breathing.

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Area of Science:

  • Sleep Medicine
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Sleep-disordered breathing (SDB) is a prevalent condition requiring effective screening tools.
  • Current screening methods may lack accuracy or accessibility.
  • Novel approaches integrating diverse data are needed for improved SDB detection.

Purpose of the Study:

  • To evaluate a novel artificial neural network (ANN) based screening tool for sleep-disordered breathing (SDB).
  • To assess the tool's performance using nocturnal pulse oximetry, demographic, anatomic, and clinical data.
  • To determine the tool's compatibility with various apnea-hypopnea index (AHI) thresholds.

Main Methods:

  • A general population dataset was utilized, with 2,280 subjects for training and 470 for testing.
  • The ANN tool incorporated 22 input variables and six models for different AHI thresholds (≥ 5 to ≥ 30 events/h).
  • Performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

Main Results:

  • The ANN tool demonstrated high performance across all AHI thresholds, with AUCs ranging from 0.904 to 0.954.
  • All ANN models achieved sensitivities exceeding 95%.
  • The model for AHI ≥ 30 events/h exhibited the highest sensitivity at 98.31%.

Conclusions:

  • The ANN-based SDB screening tool shows significant potential for identifying the presence or absence of SDB.
  • The tool's high accuracy and sensitivity suggest its utility in clinical settings.
  • Further validation in diverse populations is recommended to establish its practicability in sleep clinics and for at-risk groups.