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.
Author: | Ángel Serrano AlarcónORCiD, Maksym GaidukORCiD, Natividad Martínez MadridORCiD, Ralf SeepoldORCiDGND |
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DOI: | https://doi.org/10.1016/j.procs.2023.10.376 |
ISSN: | 1877-0509 |
Parent Title (English): | 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES2023, 6 - 8 September, Athens, Greece (Procedia Computer Science, Vol. 225) |
Volume: | 225 |
Publisher: | Elsevier |
Place of publication: | Amsterdam |
Document Type: | Conference Proceeding |
Language: | English |
Year of Publication: | 2023 |
Release Date: | 2023/12/14 |
Tag: | Sleep efficiency; Sleep study; Subjective sleep assessment |
First Page: | 3805 |
Last Page: | 3812 |
Note: | Corresponding author: Ángel Serrano Alarcón |
Institutes: | Institut für Angewandte Forschung - IAF |
DDC functional group: | 500 Naturwissenschaften und Mathematik |
600 Technik, Medizin, angewandte Wissenschaften | |
Open Access?: | Ja |
Relevance: | Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren) |
Licence (German): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |