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Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor

  • Background: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL). Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM). Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy. Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.

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Metadaten
Author of HS ReutlingenSerrano Alarcón, Ángel; Martínez Madrid, Natividad
URN:urn:nbn:de:bsz:rt2-opus4-44884
DOI:https://doi.org/10.3389/fnins.2023.1155900
ISSN:1662-4548
eISSN:1662-453X
Erschienen in:Frontiers in neuroscience
Publisher:Frontiers Research Foundation
Place of publication:Lausanne
Document Type:Journal article
Language:English
Publication year:2023
Tag:1D-CNN; deep learning; obstructive sleep apnea; portable monitor; sleep apnea
Volume:17
Page Number:19
First Page:1
Last Page:19
Article Number:1155900
DDC classes:610 Medizin, Gesundheit
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International