A Review on EEG based Epileptic Seizures Detection using Deep Learning Techniques

Cherukuvada, Srikanth; Kayalvizhi, R.

doi:10.1109/icssit53264.2022.9716374

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…In [17], a projected hybrid cuckoo finch optimizer tuned DCNN classifier for predicting and recognizing the incidence of ES using EEG signal data gained by IoT. The author [18] explained each component's detailed explanation and an overview of the many diagnostic approaches used for epilepsy. In conclusion, the study proposed several novel ideas for seizure detection using DL methods, which are gaining popularity.…”

Section: Literature Surveymentioning

confidence: 99%

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

Cherukuvada¹,

Kayalvizhi²

2023

Computers, Materials &Amp; Continua

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The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of ES analysis use traditional deep learning (DL), and machine learning (ML) approaches. This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection (OAOFS-DBNECD) technique using EEG signals. The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs. The suggested OAOFS-DBNECD technique transforms the EEG signals into .csv format at the initial stage. Next, the OAOFS technique selects an optimal subset of features using the preprocessed data. For seizure classification, the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer (AEO) with a deep belief network (DBN) model. An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm. The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies. In addition, the result of the suggested approach has been evaluated using the CHB-MIT database, and the findings demonstrate accuracy of 97.81%. These findings confirmed the best seizure categorization accuracy on the EEG data considered.

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Section: Literature Surveymentioning

confidence: 99%

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

Cherukuvada¹,

Kayalvizhi²

2023

Computers, Materials &Amp; Continua

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“…One of the alternatives is the conventional scalp electrode, in which the Electrolyte gel is used to apply the electrodes to the scalp. This gel is helpful because it makes the scalp attachment sticky and thereby enhances the signal quality 8,9 . The headset system is another way to acquire signals.…”

Section: Introductionmentioning

confidence: 99%

“…This gel is helpful because it makes the scalp attachment sticky and thereby enhances the signal quality. 8,9 The headset system is another way to acquire signals. Moreover, there are numerous EEG headsets that may be purchased commercially, and each system excels in a specific application.…”

Section: Introductionmentioning

confidence: 99%

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

Golla,

Maloji

2023

Numer Methods Biomed Eng

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Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time‐consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non‐linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time‐frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE‐GTRN‐LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE‐based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure‐affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE‐GTRN‐LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state‐of‐the‐art techniques.

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“…Deep learning (DL) techniques have been used for detecting epileptic seizures in EEG data [15]. These techniques can help diagnose and locate epileptogenic zones and predict medical and surgical treatment outcomes.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Epilepsy Diagnosis and Prediction on EEG Signals with GPU-Based Frequency Domain Analysis and Resampling.

Romero,

Lupión,

Romero

et al. 2023

Preprint

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This paper underscores the importance of accurate seizure detection in epilepsy. Current diagnostic methods, such as Video-Electroencephalography, are often costly and time-consuming. However, recent research demonstrates the potential of artificial intelligence for seizure detection using EEG or ECG data. To overcome challenges related to high-frequency data acquisition, the paper proposes employing Fast Fourier Transform for frequency analysis and GPUs for efficient data processing, allowing for reduced sampling frequencies. These advancements enhance data storage, transmission, and research efficiency, thereby facilitating neural network training for epilepsy prediction and real-time execution. Ultimately, these innovations hold significant promise for improving medical care and enhancing the quality of life for epilepsy patients.

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A Review on EEG based Epileptic Seizures Detection using Deep Learning Techniques

Cited by 8 publications

References 48 publications

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

Accelerating Epilepsy Diagnosis and Prediction on EEG Signals with GPU-Based Frequency Domain Analysis and Resampling.

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