Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

Mahjoub, Chahira; Jeannès, Régine Le Bouquin; Lajnef, Tarek; Kachouri, Abdennaceur

doi:10.1515/bmt-2019-0001

Cited by 37 publications

(14 citation statements)

References 57 publications

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“…It was computationally less complex with a high accuracy of 99.6%. Mahjoub et al [145] conducted feature extraction of epileptic EEGs with tunable-Q wavelet transform (TQWT) and intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD) and directly from the EEG raw data. This approach was a mix of linear and non-linear parameters and multiple features as its edge; an accuracy of 98.7% was recorded with SVM.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Saminu

Zhang

et al. 2021

Brain Sciences

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The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Saminu

Zhang

et al. 2021

Brain Sciences

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“…Frequency domain approach was reported by [12,13,19,20], time-frequency domain features were reported by [21][22][23][24] which they used wavelet transform with approximate entropy for feature extraction and employ artificial neural network for classification [25][26][27][28][29][30]. Other researchers that used time domain, time-frequency approach for feature extraction includes [30][31][32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

Saminu

Zhang

et al. 2020

Elektrika

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These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.

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“…Therefore, time-frequency analysis tools such as wavelet transform are widely used in EEG preprocessing and feature engineering. Mahjoub adapted tunable-Q wavelet transform and multivariate empirical mode decomposition to the preprocessing and feature extraction of EEG (Mahjoub et al, 2020). Slimen removed artifact by Savitzky-Golay filter and multi-scale principal component analysis (Slimen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG

et al. 2021

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Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications. In order to improve the clinical adaptability of the algorithm, this paper proposes an adversarial learning-driven domain-invariant deep feature representation method, which enables the hybrid deep networks (HDN) to reliably identify seizure types. In the train phase, we first use the labeled multi-lead EEG short samples to train squeeze-and-excitation networks (SENet) to extract short-term features, and then use the compressed samples to train the long short-term memory networks (LSTM) to extract long-time features and construct a classifier. In the inference phase, we first adjust the feature mapping of LSTM through the adversarial learning between LSTM and clustering subnet so that the EEG of the target patient and the EEG in the database obey the same distribution in the deep feature space. Finally, we use the adjusted classifier to identify the type of seizure. Experiments were carried out based on the TUH EEG Seizure Corpus and CHB-MIT seizure database. The experimental results show that the proposed domain adaptive deep feature representation improves the classification accuracy of the hybrid deep model in the target set by 5%. It is of great significance for the clinical application of EEG automatic analysis equipment.

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Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

Cited by 37 publications

References 57 publications

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG

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