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Sci Rep. 2019 Sep 13;9(1):13200. doi: 10.1038/s41598-019-49330-7.

Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea.

Author information

1
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland. sami.nikkonen@kuh.fi.
2
Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland. sami.nikkonen@kuh.fi.
3
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
4
Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
5
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.

Abstract

The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual analysis and complicated registration setup making it expensive and labor intensive. Partially for these reasons, OSA is a heavily underdiagnosed disease as only 7% of women and 18% of men suffering from OSA have diagnosis. To resolve these issues, we introduce an artificial neural network (ANN) that estimates AHI and oxygen desaturation index (ODI) using only the blood oxygen saturation signal (SpO2), recorded during ambulatory polygraphy, as an input. Therefore, hypopneas associated only with an arousal were not considered in this study. SpO2 signals from 1692 patients were used for training and 99 for validation. Two test sets were used consisting of 198 and 1959 patients. In the primary test set, the median absolute errors of ANN estimated AHI and ODI were 0.78 events/hour and 0.68 events/hour respectively. Based on the ANN estimated AHI and ODI, 90.9% and 94.4% of the test patients were classified into the correct OSA severity category. In conclusion, AHI and ODI can be reliably determined using neural network analysis of SpO2 signal. The developed method may enable a more affordable screening of OSA.

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