Grey Wolf Optimization with Deep Recurrent Neural Network for Epileptic Seizure Detection in EEG signals

Jaffino, G.; Jose, J. Prabin; Sundaram, M.

doi:10.1109/icaect49130.2021.9392541

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…When compared to other classifiers, the deep recurrent neural network (RNN) algorithm has a high learning rate. 22 To categorize seizures, we specifically employed DeepRNN by using gated recurrent unit (GRU) as a hidden unit. Recurrent neural networks (RNNs) with complex recurrent hidden units, such the long-short-term memory (LSTM) unit and the gated-recurrent unit (GRU), have recently gained popularity as a method for modeling temporal sequences.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Jaffino et al 22 applied grey wolf optimization (GWO) based on the deep recurrent neural network (RNN) approach for accurate detection of epileptic seizure in brain waves. The proposed method achieved a precision of 93.4% by decomposing brain waves into sub-bands using discrete wavelet transform, extracting features, and applying a GWO-based deep RNN for classification.…”

Section: Related Workmentioning

confidence: 99%

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Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Jibon,

Jamil Chowdhury,

Miraz

et al. 2024

DIGITAL HEALTH

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Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Jibon,

Jamil Chowdhury,

Miraz

et al. 2024

DIGITAL HEALTH

View full text Add to dashboard Cite

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“…Jaffino et al [42] have focused on discovering and analyzing epileptic seizure diseases by monitoring people's brain activity using EEG signals to identify the usual signals and signal epileptic seizures. To accurately identify epileptic seizures in brain waves, this is addressed by using GWO-based on a deep recurrent neural network (RNN) technique.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Survey analysis for optimization algorithms applied to electroencephalogram

Hakem,

Al-Shammary,

Mahdi

2023

IJECE

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<span lang="EN-US">This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.</span>

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“…M. Radman et al [102] adopted time-frequency-based feature extraction for a classification model of epileptic seizure detection, where they used DSET-based feature fusion to enhance feature selection confidence. G. Jaffino et al [104] adopted DWT-based MRA analysis for a seizure detection framework using deep learning-based classification. This method's analysis uses a real-time database and yields 93.4 percent precision.…”

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

EEG Feature Engineering Methods-A Comprehensive Review

Martin¹

2022

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Today, the primary topic of discussion in the signal processing domain is the analysis of non-stationary and nonlinear signal data. The use of biomedical equipment generates enormous amounts of physiological data that can be analyzed and used for clinical diagnosis. Manual inference of specific decisions from such signals is laborious due to artifacts and the time-series nature of the data, particularly for electroencephalography (EEG) signals. As a result, it is critical to employ appropriate methods for signal analysis. The purpose of this survey is to gain an understanding of the various techniques used to process EEG signal data in epileptic seizure detection frameworks. A variety of classification and regression frameworks based on machine learning have been reviewed. This systematic review will adhere to the PRISMA guidelines. This survey uncovered several significant findings.

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Grey Wolf Optimization with Deep Recurrent Neural Network for Epileptic Seizure Detection in EEG signals

Cited by 12 publications

References 16 publications

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Survey analysis for optimization algorithms applied to electroencephalogram

EEG Feature Engineering Methods-A Comprehensive Review

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