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J Clin Sleep Med. 2018 Jun 15;14(6):1063-1069. doi: 10.5664/jcsm.7182.

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

Author information

1
Department of Electrical and Computer Engineering, College of Engineering, University of Arizona, Tucson, AZ.
2
Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
3
Asthma and Airway Disease Research Center, College of Medicine, University of Arizona, Tucson, AZ.
4
Biobehavioral Health Science Division, College of Nursing, University of Arizona, Tucson, AZ.
5
Disability and Psychoeducational Studies, College of Education, University of Arizona, Tucson, AZ.
6
Department of Biomedical Engineering, College of Engineering, University of Arizona, Tucson, AZ.

Abstract

STUDY OBJECTIVES:

This study evaluated a novel artificial neural network (ANN) based sleep-disordered breathing (SDB) screening tool incorporating nocturnal pulse oximetry with demographic, anatomic, and clinical data. The tool was compatible with 6 categories of apnea-hypopnea index (AHI) with 4% oxyhemoglobin desaturation threshold, ≥ 5, 10, 15, 20, 25, and 30 events/h.

METHODS:

Using a general population dataset, the training set included 2,280 subjects, whereas the test set included 470 subjects. The input of this tool was a set of 22 variables. The tool had six neural network models for each AHI threshold. Several metrics were explored to evaluate the performance of the tool: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and 95% confidence interval (CI).

RESULTS:

The AUC was 0.904, 0.912, 0.913, 0.926, 0.930, and 0.954, respectively, with models of AHI ≥ 5, 10, 15, 20, 25, and 30 events/h thresholds. The sensitivities of all neural network models were higher than 95%. The AHI ≥ 30 events/h model had the maximum sensitivity: 98.31% (95% CI: 95.01%-100%).

CONCLUSIONS:

The results of this study suggested that the ANN based SDB screening tool can be used to identify the presence or absence of SDB. Future validation should be performed in other populations to determine the practicability of this screening tool in sleep clinics and other at-risk populations.

KEYWORDS:

artificial neural network; general population; screening; sleep-disordered breathing.

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