Epileptic EEG Signal Classification Using Convolutional Neural Network Based on Multi-Segment of EEG Signal

Santoso, Irwan Budi; Adrianto, Yudhi; Sensusiati, Anggraini Dwi; Wulandari, Diah Puspito; Purnama, I Ketut Eddy

doi:10.22266/ijies2021.0630.15

Cited by 9 publications

(22 citation statements)

References 36 publications

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“…In a previous study in [22], we have reported the CNN model with low parameters for epilepsy classification based on EEG signals. To overcome the limitations of the dataset in training, we divided the EEG signal into many segments (multi-segment) and converted it into a spectrogram image.…”

Section: Related Workmentioning

confidence: 99%

“…In this layer, the convolution process will be carried out on the input image of each MR sequence or input from the previous layer by shifting a filter. This process produces a feature map or image sequence pattern from a low to a high level [22]. Therefore, this convolution process will use many feature maps to obtain the characteristics of an image [26], [27].…”

Section: ) Convolutional Layermentioning

confidence: 99%

“…In addition to the CNN architecture proposed in the scope of the study, we used three CNN architectures presented in the literature. The three architectures were CNN in [22], VGG16 [31], and ResNet50 [32], which we used as a comparison against the architectures proposed in this study. We transferred the architectures and trained these architectures with the dataset used in the study.…”

Section: ) Output (Classification) Layermentioning

confidence: 99%

“…We transferred the architectures and trained these architectures with the dataset used in the study. CNN in [22] has a simple architecture and consists of three convolution layers with an output layer of 2 (epilepsy and nonepilepsy).The VGG16 model has 19 layers arranged sequentially, consisting of 16 convolutional layers and three fully-connected layers. The input image dimensions of the original VGG16 architecture are 224x224x3 with a fully connected output layer of 1000.…”

Section: ) Output (Classification) Layermentioning

confidence: 99%

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Ensemble Convolutional Neural Networks With Support Vector Machine for Epilepsy Classification Based on Multi-Sequence of Magnetic Resonance Images

et al. 2022

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Classification of brain abnormalities as a pathological cue of epilepsy based on magnetic resonance (MR) images is essential for diagnosis. There are some types of brain structural abnormalities as a pathological cue of epilepsy. To identify it, a neurologist can involve some sequence of MR images at a time. Existing algorithms for abnormalities classification usually involve only one or two sequences of MR images. In this paper, we proposed ensemble convolutional neural networks with a support vector machine (SVM) scheme to classify brain abnormalities (epilepsy) vs. non-epilepsy based on the axial multi-sequence of MR images. The convolutional neural network (CNN) models on the proposed method are base-learner models with different architectures and have low parameters. The performance improvement on the proposed method is made by combining the output of the base-learner models and the combination of predictions from these models. The combination of predictions uses majority voting, weighted majority voting, and weighted average. Henceforth, the combined output becomes input in the meta-learning process with SVM for the final classification. The dataset for evaluation is the axial multi-sequences of MR images that include abnormal brain structures causing epilepsy and non-epilepsy with various subjects' histories. The experimental results show the proposed method can obtain an accuracy average and F1-score of 86.37% and 90.75%, respectively, and an improvement of accuracy of 6.7%-18.19% against the CNN models on the base-learner and 2.54%-2.65% against the combination of predictions. With these results, the proposed architecture also provides better performance compared to the two existing CNN architectures.

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Section: Related Workmentioning

confidence: 99%

Section: ) Convolutional Layermentioning

confidence: 99%

Section: ) Output (Classification) Layermentioning

confidence: 99%

Section: ) Output (Classification) Layermentioning

confidence: 99%

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Ensemble Convolutional Neural Networks With Support Vector Machine for Epilepsy Classification Based on Multi-Sequence of Magnetic Resonance Images

et al. 2022

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“…This is calculated as follows: K-fold cross-validation divides the sample randomly into k equal-sized sets. Each of the k shares contains a single set of validation data for testing the model, while the remaining k -1 shares contain training data [29]. The procedure of crossvalidation is then performed k times, with each of the k sets being validated exactly once.…”

Section: Implementation Details and Performance Measuresmentioning

confidence: 99%

Quad Convolutional Layers (QCL) CNN Approach for Classification of Brain Stroke in Diffusion Weighted (DW) - Magnetic Resonance Images (MRI)

2022

IJIES

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Commonly, clinicans have problems for recognising brain stroke injury images. However, with the advantages of Information technology it is expected that will be a new method that can support the clinicans' opinion for recognising the brain stroke injury for type of stroke (hemorrhagic, ischemic, and normal). Therefore, this study aim is to discovery a new model to classify hemorrhagic, ischemic and normal based on Diffusion Weighted (DW)-Magnetic Resonance (MR) images. This study argues by using Qual Convolutional Layers (QCL-CNN) which applied in CNN can classified type of stroke. For this study experiment, this research conducted two experiment to asses the performance of QCL-CNN. The first experiments partitioned the MR image dataset into 20 percent testing and 80 percent training sets. Then, the second testing performed ten-fold cross-validation on the image dataset. The result from the first experiment of the classification accuracies obtained 93.90 percent (1 st dataset) and 94.96 percent (2 nd dataset). As for the second experiment, the results shows that the classification accuracies obtained 95.91 percent ( 1 st data set) and 97.31 percent ( 2 nd data set). The data source for this study gained from Indonesian hospital and the web sources dataset public from Ischemic Stroke Lesion Segmentation (ISLES). This study also compared, the QCL-CNN model with other architecture model such as AlexNet, ResNet50, and VGG16. The result of the comparison experiment shows that QCL-CNN architectures model has excellent performance than the others model.

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Epilepsy Seizure Identification Using the One Dimensional Convolutional Neural Network from Electroencephalogram Signals

AlMohimeed

2024

2024 2nd International Conference on Networking and Communications (ICNWC)

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Epileptic EEG Signal Classification Using Convolutional Neural Network Based on Multi-Segment of EEG Signal

Cited by 9 publications

References 36 publications

Ensemble Convolutional Neural Networks With Support Vector Machine for Epilepsy Classification Based on Multi-Sequence of Magnetic Resonance Images

Ensemble Convolutional Neural Networks With Support Vector Machine for Epilepsy Classification Based on Multi-Sequence of Magnetic Resonance Images

Quad Convolutional Layers (QCL) CNN Approach for Classification of Brain Stroke in Diffusion Weighted (DW) - Magnetic Resonance Images (MRI)

Epilepsy Seizure Identification Using the One Dimensional Convolutional Neural Network from Electroencephalogram Signals

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