Automatic epileptic seizure detection approach based on multi-stage Quantized Kernel Least Mean Square filters

Eltrass, Ahmed S.; Tayel, Mazhar B.; Elqady, A. F.

doi:10.1016/j.bspc.2021.103031

Cited by 10 publications

(5 citation statements)

References 56 publications

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“…From the survey [31][32][33][34][35][36][37] , it is studied that the existing works utilized different types of machine-learning classification techniques for detecting the epileptic seizure from the input signals with respect to varying classes. Yet, some of the limitations could degrade the effectiveness and accuracy of the seizure detection system, which include:…”

Section: Related Workmentioning

confidence: 99%

“…Figure 8 compares the sensitivity and specificity values of various machine learning-based classification approaches used for epileptic seizure detection. The techniques taken for this analysis are thresholding, MLP, LSTM, ELM, ANN, KNN, LDA, GRNN, SVM, RF, CNN, domain matching, and FMGDA [35][36][37]45 . The evaluation proves that the proposed GBSO-TAENN technique provides an increased performance value by accurately classifying the seizure-affected signals from the given dataset.…”

Section: Comparative Analysis Between Existing and Proposed Techniquesmentioning

confidence: 99%

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An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Kantipudi,

Kumar,

Aluvalu

et al. 2024

Sci Rep

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Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.

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Section: Related Workmentioning

confidence: 99%

Section: Comparative Analysis Between Existing and Proposed Techniquesmentioning

confidence: 99%

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Kantipudi,

Kumar,

Aluvalu

et al. 2024

Sci Rep

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“…Then, a Neural Network classifier using the Particle Swarm Optimization algorithm achieved a 99.30% Classification accuracy (ACC) for the Z-F-S classification problem. Also, other studies such as the research of Eltrass et al [12] have also relied on similar features such as the energy of the signal as a feature for training a Quantized Kernel Least Mean Square classifier.…”

Section: ) Time Domain Analysismentioning

confidence: 99%

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

et al. 2023

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Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.

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“…However, identifying epileptic activity in a long-term EEG signal is boring and complex because such activity occupies a small percentage of the whole EEG recording. Several automated techniques were developed to identify the occurrence of a seizure with all its stages, including pre-ictal, ictal, and post-ictal states [14][15][16][17][18][19][20][21][22][23][24][25][26]. Some studies investigated the use of empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) in classifying epileptic EEG signals [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…However, EMD techniques have several drawbacks such as the absence of a formal mathematical framework that allows a theoretical analysis and performance evaluation along with the mode mixing problem [16]. In [17], a cascade adaptive filter design was employed to predict multiple signal samples from short-term EEG signals, providing an accuracy of 97.88% for seizure versus seizure-free classification.…”

Section: Introductionmentioning

confidence: 99%

Identification and classification of epileptic EEG signals using invertible constant-Q transform-based deep convolutional neural network

Eltrass¹,

Tayel²,

Elqady³

2022

J. Neural Eng.

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Context. Epilepsy is the most widespread disorder of the nervous system, affecting humans of all ages and races. The most common diagnostic test in epilepsy is the Electroencephalogram (EEG). Objective. In this paper, a novel automated Deep Learning (DL) approach based on integrating a pre-trained Convolutional Neural Network (CNN) structure, called AlexNet, with the Constant-Q Non-Stationary Gabor Transform (CQ-NSGT) algorithm is proposed for classifying seizure versus seizure-free EEG records. Approach. The CQ-NSGT method is introduced to transform the input 1-D EEG signal into 2-D spectrogram which is sent to the AlexNet CNN model. The AlexNet architecture is utilized to capture the discriminating features of the 2-D image corresponding to each EEG signal in order to distinguish seizure and non-seizure subjects using Multi-Layer Perceptron (MLP) algorithm. Main Results. The robustness of the introduced CQ-NSGT technique in transforming the 1-D EEG signals into 2-D spectrograms is assessed by comparing its classification results with the Continuous Wavelet Transform (CWT) method, and the results elucidate the high performance of the CQ-NSGT technique. The suggested epileptic seizure classification framework is investigated with clinical EEG data acquired from the Bonn University database, and the experimental results reveal the superior performance of the proposed framework over other state-of-the-art approaches with an accuracy of 99.56 %, sensitivity of 99.12 %, specificity of 99.67 %, and precision of 98.69 %. Significance. This elucidates the importance of the proposed automated system in helping neurologists to accurately interpret and classify epileptic EEG records without necessitating tedious visual inspection or massive data analysis for long-term EEG signals.

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Automatic epileptic seizure detection approach based on multi-stage Quantized Kernel Least Mean Square filters

Cited by 10 publications

References 56 publications

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

Identification and classification of epileptic EEG signals using invertible constant-Q transform-based deep convolutional neural network

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