Cargando…
EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks
In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of sign...
Autores principales: | Liu, Quan, Chen, Yi-Feng, Fan, Shou-Zen, Abbod, Maysam F., Shieh, Jiann-Shing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4600924/ https://www.ncbi.nlm.nih.gov/pubmed/26491464 http://dx.doi.org/10.1155/2015/232381 |
Ejemplares similares
-
Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks
por: Sadrawi, Muammar, et al.
Publicado: (2015) -
Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries
por: Liu, Quan, et al.
Publicado: (2018) -
HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia
por: Liu, Quan, et al.
Publicado: (2017) -
Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform
por: Liu, Quan, et al.
Publicado: (2018) -
Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients' Consciousness Level Based on Anesthesiologists Experience
por: Jiang, George J. A., et al.
Publicado: (2015)