Weak supervision as an efficient approach for automated seizure detection in electroencephalography
Automated seizure detection from electroencephalography (EEG) would improve the quality
of patient care while reducing medical costs, but achieving reliably high performance across …
of patient care while reducing medical costs, but achieving reliably high performance across …
Six-center assessment of CNN-Transformer with belief matching loss for patient-independent seizure detection in EEG
Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by
visual inspection. This process is often time-consuming, especially for EEG recordings that …
visual inspection. This process is often time-consuming, especially for EEG recordings that …
Deep architectures for automated seizure detection in scalp EEGs
M Golmohammadi, S Ziyabari, V Shah… - arXiv preprint arXiv …, 2017 - arxiv.org
Automated seizure detection using clinical electroencephalograms is a challenging machine
learning problem because the multichannel signal often has an extremely low signal to …
learning problem because the multichannel signal often has an extremely low signal to …
Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy
Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The
reference standard diagnostic monitoring is continuous video-electroencephalography …
reference standard diagnostic monitoring is continuous video-electroencephalography …
Automatic seizure detection based on imaged-EEG signals through fully convolutional networks
Seizure detection is a routine process in epilepsy units requiring manual intervention of well-
trained specialists. This process could be extensive, inefficient and time-consuming …
trained specialists. This process could be extensive, inefficient and time-consuming …
Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals
Objective Automatic detection of epileptic seizures based on deep learning methods
received much attention last year. However, the potential of deep neural networks in seizure …
received much attention last year. However, the potential of deep neural networks in seizure …
Seizure detection using least EEG channels by deep convolutional neural network
This work aims to develop an end-to-end solution for seizure onset detection. We design the
SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with …
SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with …
Automated inter-patient seizure detection using multichannel convolutional and recurrent neural networks
We present an end-to-end deep learning model that can automatically detect epileptic
seizures in multichannel electroencephalography (EEG) recordings. Our model combines a …
seizures in multichannel electroencephalography (EEG) recordings. Our model combines a …
Continental generalization of a human-in-the-loop AI system for clinical seizure recognition
Electroencephalogram (EEG) monitoring and objective seizure identification is an essential
clinical investigation for some patients with epilepsy. Accurate annotation is done through a …
clinical investigation for some patients with epilepsy. Accurate annotation is done through a …
Epileptic seizure detection in EEG via fusion of multi-view attention-gated U-net deep neural networks
Electroencephalography (EEG) is an essential tool in clinical practice for the diagnosis and
monitoring of people with epilepsy. Manual annotation of epileptic seizures is a time …
monitoring of people with epilepsy. Manual annotation of epileptic seizures is a time …