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

Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Kader, Isselmou Abd El; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim

doi:10.3390/brainsci11050668

Cited by 59 publications

(29 citation statements)

References 163 publications

(121 reference statements)

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“…Researchers have proposed several traditional techniques for feature extraction from EEG signals, such as time domain, frequency domain, time-frequency analyses, wavelet analyses, entropy analyses, and energy distribution [9], or the combination of two or more of such methods [26]. A problem with feature extraction is that it is not only computationcostly, but also laborious and time-consuming.…”

Section: Feature Extraction and Machine Learningmentioning

confidence: 99%

“…Finally, image features of the signals, such as Fourier feature maps [ 33 ] or 3D grids [ 34 ], are some feature-based methods. Saminu et al [ 9 ] provide a summary of techniques that combine traditional feature extraction methods with machine learning classifiers for EEG signal classification.…”

Section: Background and Related Workmentioning

confidence: 99%

“…It measures the potentials that replicate the electrical activity of the human brain. EEG signals are a good source of obtaining information about the neurological conditions of a person [ 9 ]. Thanks to its low cost and noninvasiveness, automatic EEG signal classification is an important step towards their use in practical applications and less reliance on trained professionals [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

Mukhtar

Qaisar

Zaguia

2021

Sensors

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Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

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Section: Feature Extraction and Machine Learningmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

Mukhtar

Qaisar

Zaguia

2021

Sensors

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“…Therefore, to differentiate these epileptic states, these patterns in the EEG signals are highly useful so that the occurrence of a seizure can be known thereby reducing the deadly effects it has on the patients [6] . Seizure detection and classification has been studied for the past two decades with the help of machine learning and deep learning techniques, and a good survey about it can be found in [7] , [8] enabling the authors not to repeat the past works again and again. However, the most important ideas incorporating machine learning and deep learning since the past four years is discussed here for the better understanding of the readers.…”

Section: Introductionmentioning

confidence: 99%

SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification

Prabhakar

Lee

2022

IEEE Open J. Eng. Med. Biol.

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The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated well with the help of EEG signals. Goal : In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. Methods : The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems. Therefore, taking advantage of these factors, the first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique, Cuckoo Search Optimization (CSO) technique and Bat Algorithm (BA) technique; and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique. Results and Conclusions : Both these two novel deep learning techniques are tested on epilepsy and schizophrenia EEG datasets and the results are analyzed comprehensively, and a good classification accuracy of more than 93% is obtained for all the datasets.

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“…WT is used for EEG decomposition into time-frequency fragments for the subsequent detection of epileptic seizures (ESs). Currently, there are many publications on the use of various classifiers for the detection and prediction of an epileptic seizure in EEG signals using various classifiers [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Initial data on epilepsy monitoring should be preliminarily processed, including removal of artifacts and filtering noise to get a clean epilepsy EEG signal for the next step, feature extraction and classification [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury

Obukhov

Kershner

Tolmacheva

et al. 2021

Sensors

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Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for example, after traumatic brain injury, the destruction of interneuronal connections occurs, which leads to a violation of interchannel synchronization when performing motor or cognitive tests. Within the framework of a unified approach to the analysis of interchannel EEG synchronization using the ridges of wavelet spectra, two problems were solved. First, the segmentation of the initial data of long-term monitoring of scalp EEG with various artifacts into fragments suspicious of epileptic seizures in order to reduce the total duration of the fragments analyzed by the doctor. Second, assessments of recovery after rehabilitation of cognitive functions in patients with moderate traumatic brain injury. In the first task, the initial EEG was segmented into fragments in which at least two channels were synchronized, and by the adaptive threshold method into fragments with a high value of the EEG power spectral density. Overlapping in time synchronized fragments with fragments of high spectral power density was determined. As a result, the total duration of the fragments for analysis by the doctor was reduced by more than 60 times. In the second task, the network of phase-related EEG channels was determined during the cognitive test before and after rehabilitation. Calculation-logical and spatial-pattern cognitive tests were used. The positive dynamics of rehabilitation was determined during the initialization of interhemispheric connections and connections in the frontal cortex of the brain.

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

Cited by 59 publications

References 163 publications

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification

Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury

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