A Novel Multi-scale 3D CNN with Deep Neural Network for Epileptic Seizure Detection

Choi, Gwang-Ho; Park, Chulkyun; Kim, Junkyung; Cho, Kyoung‐in; Kim, Tae‐Joon; Bae, HwangSik; Min, Kyeong-Yuk; Jung, Ki-Young; Chong, Jong‐Wha

doi:10.1109/icce.2019.8661969

Cited by 32 publications

(15 citation statements)

References 2 publications

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“…In epilepsy, CNNs have been employed to detect abnormal electroencephalogram (EEG) signals in epilepsy. [26][27][28] We identified two studies on FCD focus recognition and location using CNNs; these studies obtained effective results. The first study employed a CNN model to automatically segment FCD lesions in FLAIR images.…”

Section: Introductionmentioning

confidence: 99%

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Feng

Zhao

et al. 2020

J Applied Clin Med Phys

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Purpose Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult. The purpose of this study is to detect and segment histologically confirmed FCD lesions in images of normal fluid‐attenuated inversion recovery (FLAIR)‐negative lesions using convolutional neural network (CNN) technology. Methods The technique involves training a six‐layer CNN named Net‐Pos, which consists of two convolutional layers (CLs); two pooling layers (PLs); and two fully connected (FC) layers, including 60 943 learning parameters. We employed activation maximization (AM) to optimize a series of pattern image blocks (PIBs) that were most similar to a lesion image block by using the trained Net‐Pos. We developed an AM and convolutional localization (AMCL) algorithm that employs the mean PIBs combined with convolution to locate and segment FCD lesions in FLAIR‐negative patients. Five evaluation indices, namely, recall, specificity, accuracy, precision, and the Dice coefficient, were applied to evaluate the localization and segmentation performance of the algorithm. Results The PIBs most similar to an FCD lesion image block were identified by the trained Net‐Pos as image blocks with brighter central areas and darker surrounding image blocks. The technique was evaluated using 18 FLAIR‐negative lesion images from 12 patients. The subject‐wise recall of the AMCL algorithm was 83.33% (15/18). The Dice coefficient for the segmentation performance was 52.68. Conclusion We developed a novel algorithm referred to as the AMCL algorithm with mean PIBs to effectively and automatically detect and segment FLAIR‐negative FCD lesions. This work is the first study to apply a CNN‐based model to detect and segment FCD lesions in images of FLAIR‐negative lesions.

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Section: Introductionmentioning

confidence: 99%

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Feng

Zhao

et al. 2020

J Applied Clin Med Phys

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“…Choi et al [3] proposed a multi-scale 3D-CNN with a bidirectional GRU model for cross-patient seizure detection. The authors used STFT to get spectral and temporal features from EEG signals.…”

Section: Epileptic Seizure Detectionmentioning

confidence: 99%

“…They also considered spatial features extracted from electrode positions. The proposed model [3] was evaluated on two data sets: the Boston Children's Hospital MIT scalp EEG data set (CHB-MIT) and the Seoul National University Hospital (SNUH) Scalp EEG data set. The approach achieved a sensitivity of 89% with a FPR of 0.5/h on the first database and 89% sensitivity with FPR of 0.6/h on SNUH data.…”

Section: Epileptic Seizure Detectionmentioning

confidence: 99%

“…In fact, RNNs are usually used when output at time t depends on the previous one. Regarding different proposed networks [3,8,12,19], GRU was demonstrated to be efficient for EEG data analysis.…”

Section: Epileptic Seizure Predictionmentioning

confidence: 99%

“…Existing approaches [3,8,[10][11][12]17,[19][20][21]23] can be divided into two categories. The first category [3,6,8,16,19,20,23] includes seizure detection approaches that are usually used by health professionals to improve diagnostic capabilities. In fact, they do not allow us to prevent the consequences of epileptic seizures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Convolutional Gated Recurrent Neural Network for Epileptic Seizure Prediction

Affes

Mdhaffar

Triki

et al. 2019

Lecture Notes in Computer Science

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In this paper, we present a convolutional gated recurrent neural network (CGRNN) to predict epileptic seizures based on features extracted from EEG data that represent the temporal aspect and the frequency aspect of the signal. Using a dataset collected in the Children's Hospital of Boston, CGRNN can predict epileptic seizures between 35 min and 5 min in advance. Our experimental results indicate that the performance of CGRNN varies between patients. We achieve an average sensitivity of 89% and a mean accuracy of 75.6% for the patients in the data set, with a mean False Positive Rate (FPR) of 1.6 per hour.

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Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier

Kumar

Janghel

Sahu

2022

Data Mining and Machine Learning Applications

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A Novel Multi-scale 3D CNN with Deep Neural Network for Epileptic Seizure Detection

Cited by 32 publications

References 2 publications

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

A Convolutional Gated Recurrent Neural Network for Epileptic Seizure Prediction

Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier

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