Epileptic seizure detection for multi-channel EEG with deep convolutional neural network

Park, Chulkyun; Choi, Gwang-Ho; Kim, Junkyung; Kim, Sangdeok; Kim, Tae‐Joon; Min, Kyeong-Yuk; Jung, Ki-Young; Chong, Jong‐Wha

doi:10.23919/elinfocom.2018.8330671

Cited by 46 publications

(30 citation statements)

References 8 publications

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“…In [13], Park et al (2018) suggested a new technique based on deep convolutional network for detecting the epileptic seizures. This paper suits for multi-channel EEG signals and 1D and 2D convolutional layers is used for spatio-temporal correlation, which is a feature observed in epileptic seizure detection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In earlier days representation based on Fourier transform and parametric methods are used. Variations in frequency sub bands due to epileptic seizure existing in EEG are given as δ(0.4-4 Hz), θ(4-8 Hz),α (8)(9)(10)(11)(12),and β (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30).Generally Conventional frequency based methods are suitable for decomposing EEG signals due to non stationary and multicomponent signals in EEG. The better performance is observed in time-frequency based methods compared with conventional frequency based methods.…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction and Enhanced Convolutional Neural Network (ECNN) for Detection and Diagnosis of Seizure using EEG Signals

Banu¹,

Suganya²

2019

IJEAT

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Seizure detection in non-stationary electroencephalography (EEG) is perplexing and difficult task. The human examination for detecting the seizure activities in EEG signals is liable to errors. Apart from the errors, it is a time driven task and also the detection is not precise. In order to detect epileptic seizures more precisely various automatic systems have been emerged to assist neurophysiologists by researchers in various attempts. There are various limitations such as time-consuming, technical artifact issues, result variation with respect to reader expertise level, abnormalities identification. Enhanced Convolutional Neural Network (ECNN) is a technique proposed to mitigate the above mentioned limitations and to categorize more accurate epileptic seizures results. A novel automatic method to sense epileptic seizures using feature extraction and detection is proposed in this research. Linear filter is helpful in reducing the noise along with artifacts when the EEG signals are preprocessed. The noise can be still removed by applying Least Mean Square algorithm. In this proposed research the features are extracted via analytic time frequency with Cascaded wavelet transform and fractal dimension (FD) in order to detect epileptic seizures. Lastly, to analyze the EEG signal for better classification performance of the given dataset, ECNN is adopted. During this research to classify normal, preictal, and seizure classes, a 13-layer deep ECNN algorithm is implemented. This research has special characteristics such that the model yields promising classification accuracy. The experimental result demonstrates that the proposed ECNN is superior in terms of higher sensitivity, specificity, accuracy and lower time complexity rather than the existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Enhanced Convolutional Neural Network (ECNN) for Detection and Diagnosis of Seizure using EEG Signals

Banu¹,

Suganya²

2019

IJEAT

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“…Deep Neural Networks (DNNs) have also been applied to event detection in Encephalography (EEG) and Electrocardiography (ECG). Networks incorporating convolutional layers [17]- [19] and Long Short-Term Memory (LSTM) [20]- [22] layers have been shown to provide good results when tasked with detecting abnormal events from long-term EEG and ECG data.…”

Section: Introductionmentioning

confidence: 99%

1D Convolutional Neural Networks for Detecting Nystagmus

Newman

Phillips

Cox

2021

IEEE J. Biomed. Health Inform.

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Vertigo is a type of dizziness characterised by the subjective feeling of movement despite being stationary. One in four individuals in the community experience symptoms of dizziness at any given time, and it can be challenging for clinicians to diagnose the underlying cause. When dizziness is the result of a malfunction in the innerear, the eyes flicker and this is called nystagmus. In this article we describe the first use of Deep Neural Network Architectures applied to detecting nystagmus. The data used in these experiments was gathered during a clinical investigation of a novel medical device for recording head and eye movements. We describe methods for training networks using very limited amounts of training data, with an average of 11 mins of nystagmus across four subjects, and less than 24 hours of data in total, per subject. Our methods work by replicating and modifying existing samples to generate new data. In a cross-fold validation experiment, we achieve an average F1 score of 0.59 (SD = 0.24) across all four folds, showing that the methods employed are capable of identifying periods of nystagmus with a modest degree of accuracy. Notably, we were also able to identify periods of pathological nystagmus produced by a patient during an acute attack of Ménière's Disease, despite training the network on nystagmus that was induced by different means.

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“…According to the International League Against Epilepsy (ILAE), drug-resistant epilepsy is referred to as the epileptic seizures which are unresponsive to the antiepileptic drugs and which results in the treatment through surgery. About more than 60% of the drug-resistant patients are affected with focal epilepsy, which affects the particular section of the neural system, whereas 20% of drug-resistant patients have generalized epilepsy; all parts of the brain are influenced [3]. The estimation of the epileptogenic zone (EZ) before the surgical procedure is a primary requirement for the neurosurgeons.…”

Section: Introductionmentioning

confidence: 99%

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Prasanna

Subathra

Mohammed

et al. 2020

Sensors

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The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

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Epileptic seizure detection for multi-channel EEG with deep convolutional neural network

Cited by 46 publications

References 8 publications

Feature Extraction and Enhanced Convolutional Neural Network (ECNN) for Detection and Diagnosis of Seizure using EEG Signals

Feature Extraction and Enhanced Convolutional Neural Network (ECNN) for Detection and Diagnosis of Seizure using EEG Signals

1D Convolutional Neural Networks for Detecting Nystagmus

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

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