Use of Deep Learning Techniques for Motor Events Detection in Polysomnographic Records
Use este enlace para citar
http://hdl.handle.net/2183/34132
A non ser que se indique outra cousa, a licenza do ítem descríbese como Attribution 4.0 International (CC BY 4.0)
Metadatos
Mostrar o rexistro completo do ítemTítulo
Use of Deep Learning Techniques for Motor Events Detection in Polysomnographic RecordsData
2023Resumo
[Abstract] Sleep medicine deals with the diagnosis and treatment of sleep-related disorders. The
diagnosis is carried out through the manual analysis and labeling of polysomnographic studies,
which record various electrophysiological and pneumological signals of the patient throughout
the night. This process involves the analysis of long duration signals, is complex, and demands
considerable resources and time on the part of the clinical expert. The purpose of this proyect
is the construction of automatic analysis algorithms that considerably reduce the analysis duration,
reducing the manual workload, and minimizing possible human errors, providing repeatability
and robustness. In particular, the objective is to use machine learning algorithms, based on Deep Learning techniques, for the identification and location of physiological events in these
polysomnographic records. Specifically, the goal is to locate physiological events associated with
involuntary motor movements that occur in the limbs, known as Limb Movements
Palabras chave
Algoritmos de aprendizaje
Aprendizaje automático
Registros polisomnográficos
Deep learning
Aprendizaje automático
Registros polisomnográficos
Deep learning
Descrición
Cursos e Congresos , C-155
Versión do editor
Dereitos
Attribution 4.0 International (CC BY 4.0)