Epileptic seizure classification using shifting sample difference of EEG signals

Fasil, O. K.; Rajesh, R

doi:10.1007/s12652-022-03737-9

Cited by 13 publications

(3 citation statements)

References 63 publications

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“…Here, the hybridized feature set has been utilized to capture the temporal features of the signals. Fasil et al 23 deployed a lightweight sample difference mode for designing an effective epileptic seizure detection framework. The authors intends to extract the most popular features such as entropy, fractal dimension, Hjorth, and statistical parameters for enhancing the process of detection.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Deep learning model also plays a good role in mapping nonlinear dynamic systems that are difficult to be expressed by accurate mathematical models. They have been well applied, covering anomaly signal detection [9][10][11], autonomous driving technology [12], surface topography reconstruction [13], image processing [14][15][16], natural language processing [17][18][19], medical image analysis [20][21][22][23], medical diagnosis [24][25][26][27][28][29], and software effort estimates [30]. In terms of dynamic data prediction, Hemavathy and Indumathi [31] proved neural networks can accurately identify the best relay node of underwater acoustic sensor networks combined with dynamic bias tracking algorithms.…”

Section: Related Workmentioning

confidence: 99%

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

Chen,

Xia,

Song

et al. 2023

Meas. Sci. Technol.

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In metrology and industrial design, the evaluation of measurement uncertainty and error is crucial to the measurement process. The Guide to the Expression of Uncertainty in Measurement and its supplementary documents have established a unified framework and standard for evaluating measurement uncertainty. However, a reasonable method for evaluating dynamic measurement uncertainty has not yet been proposed. By analyzing the dynamic measurement system, and using the long short-term memory time neural network to model the nonlinear dynamics represented by a piezoelectric drive platform, this paper evaluates the system’s dynamic measurement uncertainty through deep integration methods. Bayesian theory is used to propagate probability densities, and experimental results demonstrate the effectiveness of this method for assessing dynamic measurement uncertainty.

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“…Among these techniques, methods utilizing modal parameters such as frequencies [6], mode shapes [7], and modal curvatures [8] have been widely applied in civil engineering, aviation, and other domains. In the realm of computational techniques, various optimization algorithms [9][10][11][12][13][14][15], signal analysis [16][17][18][19], and data processing methods [20,21] are employed. The field of structural damage identification also encompasses the integration of multiple methods and technologies.…”

Section: Introductionmentioning

confidence: 99%

An improved convolutional neural network approach for damage localization in composite materials based on modal analysis

Guo,

Cao,

Gao

et al. 2023

Meas. Sci. Technol.

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Damage detection of composite materials using modal parameters has limitations in terms of sensitivity to small or localized damage and limited accuracy in damage localization. To address this issue, an enhanced channel attention residual network (ECARNet) damage detection model for composite laminates is proposed. First, finite element analysis is used to obtain training samples, which are processed as two-dimensional data to take full advantage of the convolutional neural network. Then, the residual module uses a multilayer perceptron instead of the traditional convolutional layers to learn the correlation between channels to enhance the generalization ability of the model, and uses the tanh activation function to retain negative information. Finally, a channel focus mechanism is introduced to enable the network to learn key features adaptively. Experimental results on two datasets with different levels of damage demonstrate the superior detection performance of ECARNet, achieving average detection accuracies of 98.13% and 97.94% respectively. A comparison with other methods verifies the effectiveness and reliability of the proposed approach. Furthermore, the effectiveness of the new method is validated on real-world test data.

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Epileptic seizure classification using shifting sample difference of EEG signals

Cited by 13 publications

References 63 publications

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

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

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

An improved convolutional neural network approach for damage localization in composite materials based on modal analysis

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