Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG

Xiao, Cao; Yao, Bin; Chen, Binqiang; Sun, Weifang; Tan, Guowei

doi:10.3389/fnins.2021.760987

Cited by 18 publications

(14 citation statements)

References 26 publications

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“…While generalization across different patients is favored, the tradeoff between accuracy and generalization has always been an issue [ 31 , 32 ]. Additionally, the process of developing a model for each patient is not scalable as the number of patients grows.…”

Section: Discussionmentioning

confidence: 99%

“…This process regularizes the model to resist overfitting and increases its generalization ability across unseen data. Similar concepts were adopted using squeeze-and-excitation networks (SENet) [ 31 ] to extract spatiotemporal features from two different datasets with different domain distributions. The model was able to generalize well in cases when the training data was sufficient.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Predominant usage of deep learning techniques [18][19][20] are witnessed. Based on domain invariant deep representation [21], autonomous deep feature extraction [22], convolutional neural networks (CNN) [22], recurrent neural networks (RNN) [24][25] etc. deep learning algorithms are used.…”

Section: Introductionmentioning

confidence: 99%

Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages Using Machine Learning Techniques

Dutta

Manohar

Indira

et al. 2022

Preprint

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Background Based on the symptoms experienced during the episode and the Electroencephalograph (EEG) recording made during the inter-ictal phase, the doctor makes the epileptic seizure type diagnosis. The fundamental issue, however, is that patients frequently struggle to explain their symptoms in the absence of an observer and identify traces in inter-ictal EEG patterns. Aims This study examines electroencephalographic (EEG) signals from epileptic seizures in order to diagnose seizures in pre-ictal, ictal, and inter-ictal stages and to categorize them into seven groups. Methods For the investigation, a licensed dataset from Temple University Hospital was used. Seven different seizure types are pre-processed from the seizure corpus and divided into pre-ictal, ictal, and inter-ictal stages. K-Nearest Neighbor (KNN), Random Forest, and other machine and deep learning techniques were used to perform the multi-class categorization. Result With 20 channels and an 80 − 20 train-test ratio, multiclass classification of seven different types of epileptic seizures was accomplished. For the pre-ictal, ictal, and inter-ictal stages, weighted KNN achieved accuracy levels of 94.7%, 94.7%, 69.0% during training and 94.46%, 94.46%, and 71.11% during testing. Conclusion Seven epileptic seizure type classification using machine learning techniques carried out with MATLAB software and weighted KNN shows better accuracy comparatively.

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“…In this context, DL-based algorithms might be suitable as it bypasses hand-crafted feature engineering and already have evidenced outstanding performance in image-based classification, including biomedical signals, and applied in seizure type classification [2][3]. For instance, Cao et al [13], classified three types of seizures by a hybrid deep neural network that combines squeeze-and-excitation networks (SENet) and long short-term memory (LSTM); Roy et al [14], used CNN for eight types of epileptic seizure discrimination and achieved F1-score up to 72.20%; David et al [8] classified seven seizure types using raw EEG signals as input where stacked auto-encoder, CNN, recurrent neural network (RNN), and hybrid network recurrent CNN (RCNN) were used for classification and achieved weighted F1-score of 94.50%.…”

Section: Introductionmentioning

confidence: 99%

“…In this view, the DL-based models, especially CNN have displayed remarkable performance in image classification and recognition [2][3], [13], [18][19][20]. Such benefit of the CNN has been exploited in seizure types classification by constructing 2D input images from 1D EEG signals by several researchers.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

Shankar

Dandapat

Barma

2022

IEEE Open J. Instrum. Meas.

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Discrimination of types of seizure using the Electroencephalogram (EEG) signal has always been a challenging task due to minuscule differences among different types of seizures. In this regard, deep learning (DL) which has already evidenced notable performance in image recognition could be suitable. However, a few attempts have been made so far in this regard mainly by constructing 2D input images for DL from 1D EEG signals directly using various techniques. Besides, the quality of the generated images has not been verified. Therefore, in this work, 2D images for the DL pipeline have been generated from brain rhythms, which already displayed remarkable performance in analyzing various brain activities. For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. Further, the quality of the 2D image along with appropriate brain rhythms have also been investigated. For experimental validation, EEG recordings of six different types of seizure that are provided by the Temple University EEG dataset (TUH v1.5.2) has been taken into account. The proposed method has achieved the highest classification accuracy and weighted F1-score up to 91.1% and 91.0% respectively. Further analysis shows that higher image resolution can provide the best classification accuracy. In addition, the δ rhythm has been found the most suitable in seizure type classification. In a comparative study, the proposed idea demonstrated its superiority by displaying the uppermost classification performance.

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Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG

Cited by 18 publications

References 26 publications

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

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

Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages Using Machine Learning Techniques

Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

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