TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Serrano Alarcón, Ángel
A1  - Gaiduk, Maksym
A1  - Martínez Madrid, Natividad
A1  - Seepold, Ralf
A1  - Ortega, Juan
T1  - Deployment of artificial intelligence models for sleep apnea recognition in the sleep laboratory
T2  - Procedia computer science
N2  - There are a large number of scientific publications that focus on the development and evaluation of artificial intelligence (AI) models for the detection of various pathologies in the field of sleep medicine. However, most of these publications do not show the process or methodology to be followed for the final deployment of these models in a complete diagnostic system (in terms of software and hardware). This is a major drawback when translating from the development or research environment to the real clinical setting. This work focuses on a methodology for deploying an AI model for sleep apnea detection with the end user in mind: the clinician. For the deployment, the transmission of data between the device, the cloud platform and the machine learning server, as well as the protocols used, were considered. In addition, the storage and visualization of the data has been taken into account so that it can be analyzed accurately by experts.
KW  - deep learning
KW  - sleep/wake state
KW  - artificial intelligence
KW  - sleep disorder
Y1  - 2024
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-53108
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2024.09.665
DO  - https://doi.org/10.1016/j.procs.2024.09.665
VL  - 246
SP  - 5388
EP  - 5395
S1  - 8
PB  - Elsevier
CY  - Amsterdam
ER  -