Automated EEG-Based Epileptic Seizure Detection Using Deep Neural Networks

Birjandtalab, Javad; Heydarzadeh, Mehrdad; Nourani, Mehrdad

doi:10.1109/ichi.2017.55

Cited by 50 publications

(20 citation statements)

References 11 publications

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“…EEG, which is one of these devices, is more preferred than other tools because it is noninvasive, economical, practical, and easy to operate. The accuracy, robustness, and reliability of the EEGrelated methodology combined with deep learning (DL) has proven with many research in brain-computer interface (BCI), especially in motor imagery task classification, [5][6][7][8][9][10][11][12][13] epileptic seizure prediction and detection, [14][15][16][17][18][19][20][21][22][23] drivers fatigue prediction, 24,25 emotion and affective state classification, [26][27][28][29][30][31][32] sleep stage detection, [33][34][35][36] prognosis in rapid eye movement behavior disorder, 37 EEG-based diagnosis of various neurodegenerative diseases, including attention deficit/hyperactivity disorder, 38 schizophrenia, 39,40 Creutzfeldt-Jacob disease, 41 Parkinson's disease, 42 Alzheimer's disease, 43 mild cognitive impairment, 44 predicting transcranial direct current stimulation treatment outcomes of patients with MDD has been studied in the recent literature. 45 The performance of a machine learning methodology based on the pretreatment EEG for MDD pro...…”

Section: Introductionmentioning

confidence: 99%

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

et al. 2020

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The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

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Section: Introductionmentioning

confidence: 99%

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

et al. 2020

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“…In recent decades researchers have developed various methods in this field of research. Application of classic statistical methods like Fourier transform (FT) [2], wavelet transform (WT) [3], and principal component analysis (PCA) [4], etc., has been developed for epilepsy seizure detection. Classic machine learning techniques like the support vector machine (SVM) optimized using genetic algorithm and trained with features extracted by double density discrete wavelet transform (DWT) have shown a competitive performance in seizure detection [5].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform

Panda

Das

Mishra

et al. 2021

Measurement Science Review

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Epileptic seizure attack is caused by abnormal brain activity of human subjects. Certain cases will lead to death. The detection and diagnosis is therefore an important task. It can be performed either by direct patient activity during seizure or by electroencephalogram (EEG) signal analysis by neurologists. EEG signal processing and detection of seizures using machine learning techniques make this task easier than manual detection. To overcome this problem related to a neurological disorder, we have proposed the ensemble learning technique for improved detection of epilepsy seizures from EEG signals. In the first stage, EEG signal decomposition is done by utilizing empirical wavelet transform (EWT) for smooth analysis in terms of sub-bands. Further, features are extracted from each sub. Time and frequency domain features are the two categories used to extract the statistical features. These features are used in a stacked ensemble of deep neural network (DNN) model along with multilayer Perceptron (MLP) for the detection and classification of ictal, inter-ictal, and pre-ictal (normal) signals. The proposed method is verified using two publicly available datasets provided by the University of Bonn (UoB dataset) and Neurology and Sleep Center - New Delhi (NSC-ND dataset). The proposed algorithm resulted in 98.93 % and 98 % accuracy for the UoB and NSC-ND datasets, respectively.

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“…Raghu et al classified seizure types using CNN and transfer learning based on EEG alone without using motor symptoms, level of consciousness, or video EEG [6]. The application of CNN to the classification of epilepsy has been implemented in several recent studies, such as [7], [8], and [9]. Neonatal seizure detection using CNN with 26 neonates achieved a seizure detection rate of 77% [10].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Classification using Deep Batch Normalization Neural Network

Purnomo¹,

Tjandrasa²

2020

LKJITI

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Epilepsy is a chronic noncommunicable brain disease. Manual inspection of long-term Electroencephalogram (EEG) records for detecting epileptic seizures or other diseases that lasted several days or weeks is a time-consuming task. Therefore, this research proposes a novel epileptic seizure classification architecture called the Deep Batch Normalization Neural Network (Deep BN3), a BN3 architecture with a deeper layer to classify big epileptic seizure data accurately. The raw EEG signals are first to cut into pieces and passed through the bandpass filter. The dataset is very imbalanced, so an undersampling technique was used to produce a balanced sample of data for the training and testing dataset. Furthermore, the balanced data is used to train the Deep BN3 architecture. The resulting model classifies the EEG signal as an epileptic seizure or non-seizure. The classification of epileptic seizures using Deep BN3 obtained pretty good results compared to other architectures used in this research, with an accuracy of 53.61%.

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Automated EEG-Based Epileptic Seizure Detection Using Deep Neural Networks

Cited by 50 publications

References 11 publications

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform

Epileptic Seizure Classification using Deep Batch Normalization Neural Network

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