Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture

He, Jiatong; Cui, Jia; Zhang, Gaobo; Xue, Mingrui; Chu, Dengyu; Zhao, Yanna

doi:10.1016/j.bspc.2022.103908

Cited by 36 publications

(15 citation statements)

References 35 publications

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“…Janjarasjitt [ 50 ] extracted wavelet features from scalp EEG recordings and classified them using support vector machines (SVM), achieving an accuracy of 96.87%, sensitivity of 72.99%, and specificity of 98.13%. He et al [ 51 ] used graph attention networks (GAT) and BiLSTM as the front-end for extracting spatial features and the back-end for exploring temporal relationships. Through extensive experiments, they demonstrated that this model can effectively detect epileptic seizures from raw EEG signals.…”

Section: Discussionmentioning

confidence: 99%

Automatic Seizure Detection Based on Stockwell Transform and Transformer

Zhong,

Liu,

Dong

et al. 2023

Sensors

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Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.

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Section: Discussionmentioning

confidence: 99%

Automatic Seizure Detection Based on Stockwell Transform and Transformer

Zhong,

Liu,

Dong

et al. 2023

Sensors

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“…A t-seconds window is defined to determine the overlapping duration of each segment. This approach has been employed in [ 37 , 41 , 60 , 62 , 117 , 121 , 132 ] to balance the desired class samples. Depending on the length of the segments, the number of created samples via overlapping may be insufficient to balance out the dataset, hence under-sampling can be used as an additional step to balance the class samples [ 117 ].…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.

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“…The mainstream seizure detection methods are based on deep neural networks due to their high accuracy and end-to-end computation. Commonly used network architectures include the convolutional neural network (Hu et al, 2018 ; Wei et al, 2019 ; Abiyev et al, 2020 ; O'Shea et al, 2020 ; Ke et al, 2021 ; Shen et al, 2023 ), recurrent neural network (RNN; Abdelhameed et al, 2018 ; Hu et al, 2020 ), graph neural network (Wang et al, 2020 ; He et al, 2022 ; Tang et al, 2022 ), Transformer (Ke et al, 2022 ; Sun et al, 2022 ), and their combination (Abdelhameed et al, 2018 ; Jia et al, 2020 ; Ke et al, 2022 ). However, these kinds of networks treat EEG signals as image-liked inputs, which may not better utilize biological information.…”

Section: Related Workmentioning

confidence: 99%

“…The mainstream seizure detection methods are based on deep learning with artificial neural networks (ANNs; Abdelhameed et al, 2018 ; Daoud and Bayoumi, 2019 ; Wei et al, 2019 ; Abiyev et al, 2020 ; Li et al, 2020 ; O'Shea et al, 2020 ; Ke et al, 2021 , 2022 ; He et al, 2022 ; Shen et al, 2023 ). To achieve better performance, existing methods mostly treat EEG signals as image-like input, and thus they can learn from state-of-the-art computer vision models and techniques.…”

Section: Introductionmentioning

confidence: 99%

Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network

Zhang,

Xiao,

et al. 2024

Front. Neurosci.

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IntroductionEpilepsy is a global chronic disease that brings pain and inconvenience to patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that can be applied to any patient, an automatic cross-patient epilepsy seizure detection algorithm is of great significance. Spiking neural networks (SNNs) are modeled on biological neurons and are energy-efficient on neuromorphic hardware, which can be expected to better handle brain signals and benefit real-world, low-power applications. However, automatic epilepsy seizure detection rarely considers SNNs.MethodsIn this article, we have explored SNNs for cross-patient seizure detection and discovered that SNNs can achieve comparable state-of-the-art performance or a performance that is even better than artificial neural networks (ANNs). We propose an EEG-based spiking neural network (EESNN) with a recurrent spiking convolution structure, which may better take advantage of temporal and biological characteristics in EEG signals.ResultsWe extensively evaluate the performance of different SNN structures, training methods, and time settings, which builds a solid basis for understanding and evaluation of SNNs in seizure detection. Moreover, we show that our EESNN model can achieve energy reduction by several orders of magnitude compared with ANNs according to the theoretical estimation.DiscussionThese results show the potential for building high-performance, low-power neuromorphic systems for seizure detection and also broaden real-world application scenarios of SNNs.

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Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture

Cited by 36 publications

References 35 publications

Automatic Seizure Detection Based on Stockwell Transform and Transformer

Automatic Seizure Detection Based on Stockwell Transform and Transformer

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network

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