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A survey on pre-training requirements for deep learning models to detect obstructive sleep apnea events

  • The development of automatic solutions for the detection of physiological events of interest is booming. Improvements in the collection and storage of large amounts of healthcare data allow access to these data faster and more efficiently. This fact means that the development of artificial intelligence models for the detection and monitoring of a large number of pathologies is becoming increasingly common in the medical field. In particular, developing deep learning models for detecting obstructive apnea (OSA) events is at the forefront. Numerous scientific studies focus on the architecture of the models and the results that these models can provide in terms of OSA classification and Apnea-Hypopnea-Index (AHI) calculation. However, little focus is put on other aspects of great relevance that are crucial for the training and performance of the models. Among these aspects can be found the set of physiological signals used and the preprocessing tasks prior to model training. This paper covers the essential requirements that must be considered before training the deep learning model for obstructive sleep apnea detection, in addition to covering solutions that currently exist in the scientific literature by analyzing the preprocessing tasks prior to training.

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Author of HS ReutlingenSerrano Alarcon, Ángel; Martínez Madrid, Natividad
Erschienen in:Procedia computer science
Place of publication:Amsterdam
Document Type:Journal article
Publication year:2023
Tag:sleep efficiency; sleep study; subjective sleep assessment
Issue:27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)
Page Number:8
First Page:3805
Last Page:3812
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International