Epileptic Seizure Detection Using Deep Recurrent Neural Networks in EEG Signals

Janghel, Rekh Ram

doi:10.1007/978-981-15-6329-4_17

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…The present work emphasizes performing classification without involving any complex feature extraction, and the potential capacity of DL algorithms has provided a new roadmap to reduce the complexity of feature extraction. Different deep learning algorithms, such as decision tree [ 32 ], SVM [ 33 ], random forest [ 34 , 35 ], and recurrent neural networks (RNN) [ 36 ], based approaches are used widely for epileptic detection. Feature extraction is essential to perform before classification since it can directly process EEG samples before feeding them into the classifier.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

Dhar

Garg

Rahman

2022

Journal of Healthcare Engineering

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One of the most common neurological disorders is epilepsy, which disturbs the nerve cell activity in the brain, causing seizures. Electroencephalography (EEG) signals are used to detect epilepsy and are considered standard techniques to diagnose epilepsy conditions. EEG monitors and records the brain activity of epilepsy patients, and these recordings are used in the diagnosis of epilepsy. However, extracting the information from the EEG recordings manually for detecting epileptic seizures is a difficult cumbersome, error-prone, and labor-intensive task. These negative attributes of the manual process increase the demand for implementing an automated model for the seizure detection process, which can classify seizure and nonseizures from EEG signals to help in the timely identification of epilepsy. Recently, deep learning (DL) and machine learning (ML) techniques have been used in the automatic detection of epileptic seizures because of their superior classification abilities. ML and DL algorithms can accurately classify different seizure conditions from large-scale EEG data and provide appropriate results for neurologists. This work presents a feature extraction-based convolutional neural network (CNN) to sense and classify different types of epileptic seizures from EEG signals. Different features are analyzed to classify seizures via EEG signals. Simulation analysis was managed to investigate the classification performance of the hybrid CNN-RNN model in terms of different achievement metrics such as accuracy, precision, recall, f1 score, and false-positive rate. The results validate the efficacy of the CNN-RNN model for seizure detection.

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Section: Related Workmentioning

confidence: 99%

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

Dhar

Garg

Rahman

2022

Journal of Healthcare Engineering

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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%

“…Step 12: Finally, the output label y can be predicted with the values of the output hidden weight matrix, vector sequence, and bias vector, which is defined in equation (36).…”

Section: Classificationmentioning

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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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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“…Blinking, despite interfering with the AF7 and AF8 sensors, was neither encouraged nor discouraged from maintaining its natural nature. This was attributed to the blink rate's dynamic relationship with tasks demanding varying levels of attention [14], As a result, the categorization algorithms would take these signal spike patterns into consideration. Additionally, during any of the exercises, the individuals were not permitted to close their eyes.…”

Section: A Eeg Brainwave Dataset: Mental Statementioning

confidence: 99%

Analysis of Electroencephalography Signals using Particle Swarm Optimization

Elbohy¹,

Abdelhamed²,

Mousa³

et al. 2021

IJACSA

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Brain computer interface devices monitor the brain signals and convert them into control commands in an attempt to imitate certain human cognitive functions. Numerous studies and applications have developed, because of the researchers' interest in systems in recent years. The capacity to categorize electroencephalograms is essential for building effective braincomputer interfaces. In this paper, three experiments were performed in order to categorize the brain signals with the goal of improving a model for EEG data analysis. An investigation is carried out to detect the characteristics derived from interactions across channels that may be more accurate than features that could be taken from individuals. Many machine learning techniques were applied such as; K-Nearest Neighbors, Long Short-Term memory and Decision Tree in this paper in order to detect and analyze the EEG signals from three different datasets to determine the best accuracy results using the particle swarm optimization algorithm that obviously minimized the dimension of the feature vector and improved the accuracy results.

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Epileptic Seizure Detection Using Deep Recurrent Neural Networks in EEG Signals

Cited by 7 publications

References 25 publications

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

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

Analysis of Electroencephalography Signals using Particle Swarm Optimization

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