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Deep convolutional architecture‐based hybrid learning for sleep arousal events detection through single‐lead EEG signals
INTRODUCTION: Detecting arousal events during sleep is a challenging, time‐consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression. MET...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275555/ https://www.ncbi.nlm.nih.gov/pubmed/37199053 http://dx.doi.org/10.1002/brb3.3028 |