.png){kind=icon}
返回
Modeling Neonatal EEG Using Multi-Output Gaussian Processes
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3159653.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Neonatal seizures are sudden events in brain activity with detrimental effects in neurological functions usually related to epileptic fits. Though neonatal seizures can be identified from electroencephalography (EEG), this is a challenging endeavour since expert visual inspection of EEG recordings is time consuming and prone to errors due the data's nonstationarity and low signal-to-noise ratio. Towards the greater aim of automatic clinical decision making and monitoring, we propose a multi-output Gaussian process (MOGP) framework for neonatal EEG modelling. In particular, our work builds on the multi-output spectral mixture (MOSM) covariance kernel and shows that MOSM outperforms other commonly-used covariance functions in the literature when it comes to data imputation and hyperparameter-based seizure detection. To the best of our knowledge, our work is the first attempt at modelling and classifying neonatal EEG using MOGPs. Our main contributions are: i) the development of an MOGP-based framework for neonatal EEG analysis; ii) the experimental validation of the MOSM covariance kernel on real-world neonatal EEG for data imputation; and iii) the design of features for EEG based on MOSM hyperparameters and their validation for seizure detection (classification) in a patient specific approach.
Keyword:
Electroencephalography
Pediatrics
Brain modeling
Kernel
Gaussian processes
Data models
Artificial neural networks
Electroencephalography
Gaussian processes
multi-output
data imputation
seizure detection
spectral mixture kernels
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Grading hypoxic-ischemic encephalopathy in neonatal EEG with convolutional neural networks and quadratic time-frequency distributions基于卷积神经网络和二次时频分布的新生儿缺氧缺血性脑病脑电图分级研究
OAAI
OAAI分享收藏Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Membrane Localization, Topology, and Mutual Stabilization of thernfABCGene Products inRhodobacter capsulatusand Implications for a New Family of Energy-Coupling NADH OxidoreductasesBiochemistry
BiochemistryIF0
PREAI
分享收藏
查看更多内容