A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal

Jiwani, Nasmin; Gupta, Ketan; Sharif, Md Haris Uddin; Adhikari, Nirmal; Afreen, Neda

doi:10.1109/esmarta56775.2022.9935403

Cited by 34 publications

(9 citation statements)

References 16 publications

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“…It includes extracting only some ruling EEG frequencies and analyzing only those to detect seizures. Other techniques used are: algorithm based on Short Time Fourier Transform [16], random-forest classification algorithm [17], Long short-term memory (LSTM) [18]and CNN [1], [8], [18], Naïve Bayes [15], [19], Support Vector Machine (SVM) [15], [19], Linear Discriminant Analysis (LDA) [19], variational modal decomposition (VMD) [20] and a deep forest (DF) [20] model, pyramidal one-dimensional convolutional neural network (P-1D-CNN) [21], 1D CNN-LSTM [22], decision tree (DT) [9], shallow artificial neural network (ANN) [9], [15], K-Nearest Neighbors (KNN) [15], and convolutional neural networks [1], [8].…”

Section: Figure 1phases Of Seizure and Their Description Is Given In ...mentioning

confidence: 99%

“…It is then expected to give accurate outcome when same type of test data is fed in it. [3], [15], [17], [19]Many researchers have started using Machine Learning methods to solve their problems faster and also because lowcost processing and memory are at our disposal. Because of the availability of Machine Learning approaches, it is now feasible to study and analyze huge datasets to reveal the patterns and trends which might not be visible to the naked eye.…”

Section: Figure 1phases Of Seizure and Their Description Is Given In ...mentioning

confidence: 99%

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Machine Learning Model to Detect Seizures Using EEG Signals

Chandel

Kaur

Grover

et al. 2023

Preprint

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Epilepsy is a disease of grave concern these days due to the negligence in its treatment in many parts of the world. Its detection and diagnose requires high skill, large amount of time and money. Thus, due to lack of treatment, epilepsy which can be diagnosed with simple epileptic drugs turn refractory. This can be avoided if it is detected at an early stage. Also, the data received after a patient undergo EEG is quite complex. Visualizing that data in an effective way and knowing important timestamps in a recorded EEG signal can help one save time and increase accuracy of detection. An automated system utilizing conventional machine learning is thus proposed in this study that uses features extracted from EEG signals. We have used a seizure detection model and visualized data and the result using various python libraries. Seizure detection is a model which is able to identify the presence of abnormal activities in the brain. Seizure prediction is a model which is able to predict in advance if he/she is going to face seizures in coming time by just studying the EEG signals of present state of that patient. Supervised Machine learning (random forest classifier) was employed to analyze recorded EEG signals for epilepsy detection. Data in the datasets was visualized using matplotlib. Classifier was visualized using Graphviz and pydot. Random forest model predicted epilepsy with a good accuracy of 96.87%, Sensitivity came out to be 98.4% and Specificity was 90.7%.

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Section: Figure 1phases Of Seizure and Their Description Is Given In ...mentioning

confidence: 99%

Section: Figure 1phases Of Seizure and Their Description Is Given In ...mentioning

confidence: 99%

Machine Learning Model to Detect Seizures Using EEG Signals

Chandel

Kaur

Grover

et al. 2023

Preprint

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show abstract

“…In the last step, a comparison is made between the method that was presented and the most recent, cutting-edge methods that have been published. The CNN and LSTM are the two components of the model that Jiwani et al [20] devised for the purpose of identifying seizures. It integrates CNN and LSTM models to concentrate on the extraction of temporal and spatial characteristics.…”

Section: Literature Surveymentioning

confidence: 99%

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Prasanna,

Rahman,

Bayleyegn

2023

IEEE Access

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Brain Epilepsy seizure is a critical disorder, which is an uncontrolled burst of electrical activity of brain. The early detection of brain seizure can save the life of humans. The electroencephalogram (EEG) signals may be used to automatically identify brain seizures, which is one of the most prominent solutions for this issue. However, the conventional methods are failed to classify the brain seizure effectively. So, this work implemented the Brain Epilepsy Seizure-Detection-Network (BESD-Net) using deep learning, recurrent learning properties. Initially, the dataset pre-processing is performed, which eliminates the noise, unwanted data from EEG dataset. Then, the deep learning based customized convolution neural network (CCNN) is trained on pre-processed EEG data for precise extraction of disease correlated features. The machine learning based exhaustive random forest (ERF) feature selection is used to optimize features from the CCNN features, which are highly correlated with disease dependent properties. In conclusion, the recurrent neural network (RNN) based bi-directional long short-term memory (BLSTM) is used in order to detect brain seizures from chosen ERF features. Training and testing of suggested methodology had made use of CHB-MIT Scalp EEG Database. The aforementioned model has achieved the values of 98.36%, 97.54%, 97.91%, 98% and 95.08% respectively for precision, sensitivity, F1-Score, accuracy and specificity. The findings of the simulations demonstrate that the suggested BESD-Net led to superior performance when compared to the technologies that are already in use.

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“…Epilepsy can be diagnosed using EEG, a reliable and quasi tool for measuring brain activity. Recently, several techniques 7 have been introduced for this EEG signal acquisition. One of the alternatives is the conventional scalp electrode, in which the Electrolyte gel is used to apply the electrodes to the scalp.…”

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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A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal

Cited by 34 publications

References 16 publications

Machine Learning Model to Detect Seizures Using EEG Signals

Machine Learning Model to Detect Seizures Using EEG Signals

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

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

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