Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

Lian, Jian; Zhang, Yan; Luo, Rong; Han, Guoyong; Jia, Weikuan; Li, Chengjiang

doi:10.1109/access.2020.2976751

Cited by 14 publications

(8 citation statements)

References 113 publications

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“…The advances in the computational performance is the main reason for the increase in popularity of raw signal methodologies the latest years. These systems usually take advantage of a series of convolution blocks to perform the feature extraction process [94], [95], [96], [97], [98]. However, other NN architectures that do not rely on a convolution block have also been used taking raw signal as input for the epilepsy detection [99].…”

Section: D: Non-linear Analysismentioning

confidence: 99%

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

et al. 2023

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Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.

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Section: D: Non-linear Analysismentioning

confidence: 99%

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

et al. 2023

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“…More significant CNN-based models for EEG diagnose and prediction have been achieved by more researchers and developers like Lian et al [20] and Mousavi et al [4].…”

Section: Convolution Neural Network (Cnn) For Eeg Classificationmentioning

confidence: 99%

“…Table 2 shows resultant accuracy by Artificial Neural Network (ANN) classification for [5,[13][14][15][16][17]. Table 3 shows resultant accuracy by Convolution Neural Network (CNN) classification for [4,[18][19][20]. Table 4 shows resultant accuracy by K-Nearest Neighbor (K-NN) classification for [7,21,22].…”

Section: Analysis and Evaluationmentioning

confidence: 99%

Survey for Electroencephalography EEG Signal Classification Approaches

Al-Fraiji

Al‐Shammary

2021

Mobile Computing and Sustainable Informatics

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This paper presents a literature survey for electroencephalogram (EEG) signal classification approaches based on machine learning algorithms. EEG classification plays a vital role in many health applications using machine learning algorithms. Mainly, they group and classify patient signals based on learning and developing specific features and metrics. In this paper, 32 highly reputed research publications are presented focusing on the designed and implemented approach, applied dataset, their obtained results and applied evaluation. Furthermore, a critical analysis and statement are provided for the surveyed papers and an overall analysis in order to have all the papers under an evaluation comparison. SVM, ANN, KNN, CNN, LDA, multi-classifier and more other classification approaches are analyzed and investigated. All classification approaches have shown potential accuracy in classifying EEG signals. Evidently, ANN has shown higher persistency and performance than all other models with 97.6% average accuracy.

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“…The accuracy of classification reached 96.0%. Lian J et al [7] introduced a convolutional neural network (CNN). Input the two-dimensional matrix generated by brain signal into the convolutional neural network to enhance the correlation between different channel signals.…”

Section: Introductionmentioning

confidence: 99%

Classification of Epileptic Brain Signals of College Students Based on Deep Learning

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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Epilepsy, as a common nervous system disease in the world, urgently needs effective methods for diagnosis and treatment. Scalp electroencephalogram (EEG) contains a lot of physiological and pathological information, which plays a very important role in the diagnosis of brain diseases such as epilepsy. At present, the clinical analysis of EEG signal is mainly based on the clinician’s visual analysis, which makes the clinician’s task heavy, and there is no quantitative standard for the analysis results. Therefore, the automatic classification of epileptic EEG signals has great potential in current clinical application. Based on the deep learning VGG16 convolutional neural network model, a method for automatic detection of seizure signals is proposed in this paper. Firstly, the samples are preprocessed, then enter the VGG16 convolutional neural network model for training, and finally input the validation set for validation. The experimental results indicate that this model can distinguish the pre-ictal EEG, interictal EEG and ictal EEG.

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Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

Cited by 14 publications

References 113 publications

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

Survey for Electroencephalography EEG Signal Classification Approaches

Classification of Epileptic Brain Signals of College Students Based on Deep Learning

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