Cargando…
Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients
Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic...
Autores principales: | Li, Jing, Zhen, Xiantong, Liu, Xianzeng, Ouyang, Gaoxiang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4000972/ https://www.ncbi.nlm.nih.gov/pubmed/24977196 http://dx.doi.org/10.1155/2014/459636 |
Ejemplares similares
-
Epileptic Tissue Localization through Skewness-Based Functional Connectivity in the High-Frequency Band of Intracranial EEG
por: Shen, Mu, et al.
Publicado: (2023) -
Topolnogical classifier for detecting the emergence of epileptic seizures
por: Piangerelli, Marco, et al.
Publicado: (2018) -
Sleep and respiratory abnormalities in adults with developmental and epileptic encephalopathies using polysomnography and video‐EEG monitoring
por: Sivathamboo, Shobi, et al.
Publicado: (2023) -
Estimation of vital signs from facial videos via video magnification and deep learning
por: Lin, Bin, et al.
Publicado: (2023) -
Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data
por: Abualsaud, Khalid, et al.
Publicado: (2015)