Cargando…
Modified Time-Frequency Marginal Features for Detection of Seizures in Newborns
The timely detection of seizure activity in the case of newborns can help save lives. Clinical signs of seizures in newborns are difficult to observe, so, in this study, we propose an automated method of detecting seizures in newborns using multi-channel electroencephalogram (EEG) recording acquired...
Autores principales: | Khan, Nabeel Ali, Ali, Sadiq, Choi, Kwonhue |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025536/ https://www.ncbi.nlm.nih.gov/pubmed/35459022 http://dx.doi.org/10.3390/s22083036 |
Ejemplares similares
-
An Efficient and Accurate Multi-Sensor IF Estimator Based on DOA Information and Order of Fractional Fourier Transform
por: Khan, Nabeel Ali, et al.
Publicado: (2022) -
Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures
por: Aung, Si Thu, et al.
Publicado: (2020) -
Symptomatic seizures in preterm newborns: a review on clinical features and prognosis
por: Spagnoli, Carlotta, et al.
Publicado: (2018) -
Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure
por: Şengür, Abdulkadir, et al.
Publicado: (2016) -
Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks
por: Tzallas, A. T., et al.
Publicado: (2007)