Convolutional Neural Networks for Epileptic Seizure Prediction

Eberlein, Matthias; Hildebrand, Raphael; Tetzlaff, Ronald; Hoffmann, Nico; Kuhlmann, Levin; Brinkmann, Benjamin H.; Müller, Jens

doi:10.1109/bibm.2018.8621225

Cited by 44 publications

(39 citation statements)

References 36 publications

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“…From signal processing to classification, state-of-the-art algorithms have been implemented with different levels of success [21]. Several theoretical investigations examine the pre-ictal state to find a signature that helps anticipate and predict an epileptiform seizure [22][23][24][25][26]. The authors in [27] presented a pseudo-prospective seizure prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

Romney

Manian

2020

Computers

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Epilepsy patients who do not have their seizures controlled with medication or surgery live in constant fear. The psychological burden of uncertainty surrounding the occurrence of random seizures is one of the most stressful and debilitating aspects of the disease. Despite the research progress in this field, there is a need for a non-invasive prediction system that helps disrupt the seizure epileptiform. Electroencephalogram (EEG) signals are non-stationary, nonlinear and vary with each patient and every recording. Full use of the non-invasive electrode channels is impractical for real-time use. We propose two frontal-temporal electrode channels based on ensemble empirical mode decomposition (EEMD) and Relief methods to address these challenges. The EEMD decomposes the segmented data frame in the ictal state into its intrinsic mode functions, and then we apply Relief to select the most relevant oscillatory components. A deep neural network (DNN) model learns these features to perform seizure prediction and early detection of patient-specific EEG recordings. The model yields an average sensitivity and specificity of 86.7% and 89.5%, respectively. The two-channel model shows the ability to capture patterns from brain locations for non-fontal-temporal seizures.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

Romney

Manian

2020

Computers

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“…We recently proposed three Convolutional Neural Networks (CNN) topologies for seizure forecasting [3], showing that these networks produce promising results on different patients for several long-term data sets. With the nv1x16-topology, arbitrary electrode placements in the implantation scheme can be processed.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…The topology comprises subsequent convolution and pooling steps on single channels for feature extraction and two fully connected layers for classification. Details about the topology and training of the network are given in [3].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Evaluation of machine learning methods for seizure prediction in epilepsy

Eberlein

Müller

Yang

et al. 2019

Current Directions in Biomedical Engineering

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Epilepsy affects about 50 million people worldwide of which one third is refractory to medication. An automated and reliable system that warns of impending seizures would greatly improve patient’s quality of life by overcoming the uncertainty and helplessness due to the unpredicted events. Here we present new seizure prediction results including a performance comparison of different methods. The analysis is based on a new set of intracranial EEG data that has been recorded in our working group during presurgical evaluation. We applied two different methods for seizure prediction and evaluated their performance pseudoprospectively. The comparison of this evaluation with common statistical evaluation reveals possible reasons for overly optimistic estimations of the performance of seizure forecasting systems.

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“…Additional signal processing techniques might be required to fine-tune their methods for practical usage. Recently, an increasing number of researchers start to utilize neural network (NN) methods due to its inherent automatic feature extraction characteristics [12][13][14]. San-segundo et al analyzed the use of deep neural network for epileptic EEG signal classification with different inputs and suggested empirical mode decomposition for better performance in focal versus nonfocal classification and Fourier transform for seizure detection [15].…”

Section: Introductionmentioning

confidence: 99%

“…However, this work treats the EEG channels as feature channels at the beginning of the model, failing to learn signal patterns of individual EEG channels. Eberlein et al [12] performed convolution on EEG signals with kernels ranging over multiple channels to detect local patterns instead of a single channel. Although the authors tried several topologies over the number of channels to be convoluted together, the accuracy is limited due to insufficient representative features in EEG recordings.…”

Section: Introductionmentioning

confidence: 99%

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

et al. 2020

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Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

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Convolutional Neural Networks for Epileptic Seizure Prediction

Cited by 44 publications

References 36 publications

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

Evaluation of machine learning methods for seizure prediction in epilepsy

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

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