An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Shoeibi, Afshin; Moridian, Parisa; Khodatars, Marjane; Ghassemi, Navid; Jafari, Mahboobeh; Alizadehsani, Roohallah; Kong, Yinan; Gorriz, Juan Manuel; Ramírez, Javier; Khosravi, Abbas; Nahavandi, Saeid; Acharya, U. Rajendra

doi:10.48550/arxiv.2105.14278

Cited by 4 publications

(6 citation statements)

References 116 publications

(154 reference statements)

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“…In the neuroimaging modalities, an epileptic seizure diagnosis based on EEG signals has remarkable popularity. EEG signal recording includes scalp EEG (sEEG) and intracranial EEG (IEEG) modalities [9]. EEG modalities include essential information from the functions of the brain in the epileptic seizures period.…”

Section: Introductionmentioning

confidence: 99%

“…EEG modalities include essential information from the functions of the brain in the epileptic seizures period. In comparison with other neuroimaging modalities, some benefits of EEG are a lower cost, the easiness of carrying, and suitable performance in epileptic seizure detection [9]. To diagnose epileptic seizures, doctors need to have a long record of the patient's EEG signals.…”

Section: Introductionmentioning

confidence: 99%

“…To diagnose epileptic seizures, doctors need to have a long record of the patient's EEG signals. The EEG signals also usually have many various channels and artifacts, which cause some difficulties and challenges for doctors in the epileptic seizures diagnosis process [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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confidence: 99%

“…In recent years, various investigations have provided automated SZ diagnosis via EEG signals using Artificial Intelligence (AI) methods [18], [19], [20], [21]. The AI investigations in this field include conventional machine learning and DL methods [18], [19], [20], [21]. The AI-based SZ diagnosis algorithm includes preprocessing sections, features extraction and selection, and in the end, classification.…”

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confidence: 99%

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

Shoeibi,

Sadeghi,

Moridian

et al. 2021

Preprint

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Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent Deep Learning (DL)based methods for automated SZ diagnosis via EEG signals.The obtained results are compared with those of conventional intelligent methods. In order to implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals are divided into 25-seconds time frames and then were normalized by zscore or norm L2. In the classification step, two different approaches are considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals is first carried out by conventional DL methods, e.g., KNN, DT, SVM, Bayes, bagging, RF, and ET. Various proposed DL models, including LSTMs, 1D-CNNs, and 1D-CNN-LSTMs, are used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function and the z-score and L2 combined normalization are used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that in order to perform all simulations, the k-fold cross-validation method with k=5 has been used.

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Moridian

Ghassemi

Jafari

et al. 2022

Front. Mol. Neurosci.

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Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

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An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Cited by 4 publications

References 116 publications

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

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