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Apnea-hypopnea index using deep learning models with whole and window-based time series

  • oday many scientific works are using deep learning algorithms and time series, which can detect physiological events of interest. In sleep medicine, this is particularly relevant in detecting sleep apnea, specifically in detecting obstructive sleep apnea events. Deep learning algorithms with different architectures are used to achieve decent results in accuracy, sensitivity, etc. Although there are models that can reliably determine apnea and hypopnea events, another essential aspect to consider is the explainability of these models, i.e., why a model makes a particular decision. Another critical factor is how these deep learning models determine how severe obstructive sleep apnea is in patients based on the apnea-hypopnea index (AHI). Deep learning models trained by two approaches for AHI determination are exposed in this work. Approaches vary depending on the data format the models are fed: full-time series and window-based time series.

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Author:Ángel Serrano AlarcónORCiD, Natividad Martínez MadridORCiD, Ralf SeepoldORCiDGND, Juan Antonio OrtegaORCiD
Parent Title (English):Hardware and software supporting physiological measurement (HSPM-2022), Workshop, October 27-28, 2022, Konstanz, Germany
Publisher:Hochschule Reutlingen
Place of publication:Reutlingen
Document Type:Conference Proceeding
Year of Publication:2022
Release Date:2023/01/10
Tag:Deep Learning; Obstructive Sleep Apnea; OSA; Precision Medicine; AHI
First Page:13
Last Page:15
Institutes:Institut für Angewandte Forschung - IAF
Open Access?:Ja
Relevance:Sonstige Publikation
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International