A Deep Learning Method for Prediction of Benign Epilepsy with Centrotemporal Spikes

Yan, Ming; Liu, Ling; Chen, Sihan; Pan, Yi

doi:10.1007/978-3-319-94968-0_24

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Various DL models were developed to detect epileptic seizure using sMRI, fMRI, and PET scans with or without EEG signals [ 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ]. These models outperformed the conventional models in terms of automatic detection and monitoring of the disease.…”

Section: Non-eeg-based Epileptic Seizures Detectionmentioning

confidence: 99%

“…However, other neuroimaging modalities such as MRI are used for epileptic seizures detection. In [ 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ], MRI modalities coupled with DL methods have been used to diagnose epileptic seizures. Datasets with non-MRI modalities are not available, and this has led to limited research in this area.…”

Section: Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

283

109

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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

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Section: Non-eeg-based Epileptic Seizures Detectionmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

283

109

View full text Add to dashboard Cite

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“…Deep Neural Networks (DNNs) [1] have become the learning algorithm of choice for a lot of real-world large-scale machine learning tasks. They have been successfully used for solving problems in computer vision [2,3] , natural language processing [4] , machine learning for radio frequency domains [5] , robotics [6] , and bioinformatics [7][8][9][10] , to name a few areas of application. However, in spite of the success of deep neural networks, there are gaps in our understanding of Chaity Banerjee is with Department of Idustrial & Systems Engineering, University of Central Florida, Orlando, FL 32816-2368, USA.…”

Section: Introductionmentioning

confidence: 99%

Feature representations using the reflected rectified linear unit (RReLU) activation

Banerjee

Mukherjee

Pasiliao

2020

Big Data Min. Anal.

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Deep Neural Networks (DNNs) have become the tool of choice for machine learning practitioners today.One important aspect of designing a neural network is the choice of the activation function to be used at the neurons of the different layers. In this work, we introduce a four-output activation function called the Reflected Rectified Linear Unit (RReLU) activation which considers both a feature and its negation during computation. Our activation function is "sparse", in that only two of the four possible outputs are active at a given time. We test our activation function on the standard MNIST and CIFAR-10 datasets, which are classification problems, as well as on a novel Computational Fluid Dynamics (CFD) dataset which is posed as a regression problem. On the baseline network for the MNIST dataset, having two hidden layers, our activation function improves the validation accuracy from 0.09 to 0.97 compared to the well-known ReLU activation. For the CIFAR-10 dataset, we use a deep baseline network that achieves 0.78 validation accuracy with 20 epochs but overfits the data. Using the RReLU activation, we can achieve the same accuracy without overfitting the data. For the CFD dataset, we show that the RReLU activation can reduce the number of epochs from 100 (using ReLU) to 10 while obtaining the same levels of performance.

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“…For patients with BECT and other types of epilepsy, the localization analysis of epileptic spikes is more meaningful than seizure detection. In order to better diagnose patients, neurologists need to analyze a large number of EEG data to find millisecond-level epileptic spike discharges, which is extremely cumbersome and time-consuming (Yan et al, 2018). Therefore, positioning monitoring and recording analysis of various spike signals for the location of epileptic regions, the precise mechanism of epilepsy, and even predict of seizures are important (Chahid This paper proposes an improved BECT spike intelligent detection method using phase locking value (PLV).…”

mentioning

confidence: 99%

An improved BECT spike detection method with functional brain network features based on PLV

et al. 2023

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BackgroundChildren with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging.PurposeThis paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.MethodsTo obtain high detection effect, this method uses a specific template matching method and the ‘peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes.ResultsBased on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.

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A Deep Learning Method for Prediction of Benign Epilepsy with Centrotemporal Spikes

Cited by 3 publications

References 15 publications

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Feature representations using the reflected rectified linear unit (RReLU) activation

An improved BECT spike detection method with functional brain network features based on PLV

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