ArticleActa epileptologica2023
Detection method of absence seizures based on Resnet and bidirectional GRU.
Article in Acta epileptologica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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4 citing papers in PubMed.
- A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.Scientific reports · 2024Article
- Combining data augmentation and deep learning for improved epilepsy detection.Frontiers in neurology · 2024Article
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
- Correction: Detection method of absence seizures based on Resnet and bidirectional GRU.Acta epileptologica · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
backgroundEpilepsy is a common chronic neurological disease. Its repeated seizure attacks have a great negative impact on patients' physical and mental health. The diagnosis of epilepsy mainly depends on electroencephalogram (EEG) signals detection and analysis. There are two main EEG signals detection methods for epilepsy. One is the detection based on abnormal waveform, the other is the analysis of EEG signals based on the traditional machine learning. The feature extraction method of the traditional machine learning is difficult to capture the high-dimension information between adjacent sequences.
methodsIn this paper, redundant information was removed from the data by Gaussian filtering, downsampling, and short-time Fourier transform. Convolutional Neural Networks (CNN) was used to extract the high-dimensional features of the preprocessed data, and then Gate Recurrent Unit (GRU) was used to combine the sequence information before and after, to fully integrate the adjacent information EEG signals and improve the accuracy of the model detection.
resultsFour models were designed and compared. The experimental results showed that the prediction model based on deep residual network and bidirectional GRU had the best effect, and the test accuracy of the absence epilepsy test set reached 92%.
conclusionsThe prediction time of the network is only 10 sec when predicting four-hour EEG signals. It can be effectively used in EEG software to provide reference for doctors in EEG analysis and save doctors' time, which has great practical value.
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