TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Serrano Alarcon, Ángel
A1  - Gaiduk, Maksym
A1  - Martínez Madrid, Natividad
A1  - Seepold, Ralf
T1  - A survey on pre-training requirements for deep learning models to detect obstructive sleep apnea events
JF  - Procedia computer science
N2  - 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.
KW  - sleep efficiency
KW  - sleep study
KW  - subjective sleep assessment
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-46548
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2023.10.376
DO  - https://doi.org/10.1016/j.procs.2023.10.376
VL  - 255
IS  - 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)
SP  - 3805
EP  - 3812
S1  - 8
PB  - Elsevier
CY  - Amsterdam
ER  -