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Integrating Deep Learning and Ensemble Techniques for Improved Epileptic Seizure Detection

Abstract: 








Keywords: Seizure Detection, Deep Learning, Support Vector Machine, Random Forest, Multi-Dimensional Bayesian Convolutional Network, Deep Neural Network, XGBClassifier, Voting Classifier, Discrete Wavelet Transform.


	Introduction
Seizures, especially those linked to epilepsy, are among the most widespread neurological conditions, affecting around 50 million people globally, according to the World Health Organization. Their unpredictable nature not only disrupts daily life but also poses serious health risks, including injury and, in severe cases, sudden unexpected death in epilepsy (SUDEP). Improving the accuracy of seizure detection and prediction could significantly enhance patient care by enabling timely interventions, personalized treatments, and even the possibility of preventing seizures altogether.
Electroencephalography (EEG) is the primary diagnostic tool used in epilepsy, offering critical insights into brainwave activity and electrical dynamics. Electrodes are strategically placed on the scalp to record EEG signals, as shown in Figure 1. Different types of seizures produce distinct EEG patterns, including:
	Interictal Spikes: Brief bursts of high-frequency activity between seizures, indicating an elevated risk of seizure onset.
	Ictal Activity: EEG patterns during a seizure, which vary depending on the type and location of the seizure.
	Slow Waves: Low-frequency waves following a seizure, reflecting decreased brain activity.
	High-Frequency Oscillations (HFOs): Rapid oscillations often seen with interictal spikes, associated with epileptic activity.
Figure 1: EEG recording system [5].
In recent years, machine learning (ML) has become a crucial tool in neurology for enhancing predictive analytics and medical diagnostics. Traditionally, seizure detection involves manually reviewing EEG or video-EEG data by trained professionals. However, this method is not only time-consuming and subjective but also lacks scalability for continuous, real-time monitoring. Machine learning offers a breakthrough in this area, enabling automated, real-time seizure detection and, potentially, predictive capabilities. This technological advancement is shifting the management of seizure disorders from a reactive to a proactive approach.


The primary objectives of this study are:


Problem Statement:



Research Question:



Research Gap:





Contributions:




Outline of the Study:


	Related Work
(Survey)
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Table 3. Feature-based ML methods from a systematic review of literature for seizure detection in scalp EEG data
Classifier	Feature(s)	Dataset(s)	Performance	Validation	Segment	Year	Reference
SVM (Classification) 	 Covariance matrices of channels (Riemannian geometry) 	 CHB-MIT (22 channels) 	 Accuracy: 99.87%, Sensitivity: 99.91%, Specificity: 99.82% 	 10-fold CV 	 2s, no overlap 	 2022 	[1]

 Naive Bayes 	 10 geometric features extracted in each frequency band (θ, β, δ, α) 	 CHB-MIT 	 Accuracy: 94.54% 	 10-fold CV 	 20s, 15s overlap 	 2022 	[2]

Fuzzy k-NN	GNMF decomposed SSTFT maps	CHB-MIT, Bonn	Accuracy: 98.99%, Sensitivity: 99.27%, Specificity: 98.53%	10-fold CV	1s, no overlap	2023	[3]

k-NN (feature selection), RF	Weighted degree, clustering coefficient	CHB-MIT, Siena scalp	CHB-MIT: F1: 86.69%, AUC: 84.33%, Accuracy: 84.83%, Precision: 85.60%, Sensitivity: 87.81%, Specificity: 81.01%	5-fold CV	4s	2023	[4]

SVM	Kurtosis, skewness, line length, quartile values, correlation coefficient matrix (PCA dimensionality reduction)	CHB-MIT, Siena	Accuracy: 96.67%, Specificity: 95.62%, Sensitivity: 97.72%	Bootstrap	1s, 0.5s overlap	2023	[5]


Table 4. DL methods from systematic review of literature for seizure detection in scalp EEG data
Classifier	Dataset(s)	Performance	Validation	Segment Length	Year	References
CNN (Feature Extraction), ANN, LR, RF, SVM, GB, k-NN, SGD, Ensembles (Classification)	CHB-MIT, Bonn	ANN: 94.4%, LR: 91.7%, RF: 92.4%, SVM: 95.7%, GB: 94.6%, k-NN: 96.8%, SGD: 87%, Ensembles: 97%	10-fold CV	5s, no overlap	2022	[6]

BERT (LLM)	TUSZ	Accuracy: ~77%	-	1s	2022	[7]

Graph-Generative Neural Network (GGN)	TUH	Accuracy: 91%	Train-test (70-30)	5s	2022	[8]

CNN and RNN	CHB-MIT, Bonn, Bern-Barcelona	Accuracy: 96.23%	8-fold CV	N/A	2023	[9]

CNNs with an Attention Mechanism	TUH	Accuracy: 86%, F1: 81%	LOO	3s, no overlap	2023	[10]

CNN and CBAM (Feature Extraction), GRU (Classification)	CHB-MIT (13 patients)	Accuracy: 91.73%, Sensitivity: 88.09%, FPR: 0.053/h, Specificity: 92.09%, AUC: 91.56%	10-fold CV	30s, 1s overlap	2023	[11]

CNN	CHB-MIT (8 channels, 16 patients)	Accuracy: 97.57%, Sensitivity: 98.90%, FPR: 2.13%, Delay: 10.46s	LOO	5s, 1s overlap	2023	[12]

Scalp Swarm Algorithm (SSA) (Feature Selection), LSTM (Classification)	TUSZ	Sensitivity: 98.99%, FDR: 98.43%, Specificity: 99.01%, Accuracy: 99.2%, F1: 97.54%	80-20 train-test	1s	2024	[13]



	Preliminaries
(SVM, ……..)






 
Model	Description	Steps	Pros	Cons
MDBCN	. MDBCN is a deep learning model that combines the principles of Convolutional Neural Networks (CNNs) with multi-scale feature extraction to enhance classification performance, particularly in complex datasets. It utilizes a multi-layer architecture to learn hierarchical representations of input data.	1. Data Preparation: Preprocess the dataset, including normalization and augmentation.
2. Feature Extraction: Apply convolutional layers with different kernel sizes to capture multi-scale features.
3. Pooling Layers: Use pooling layers to reduce dimensionality while retaining essential features.
4. Training: Utilize a suitable optimizer (e.g., Adam) to train the model with a labeled dataset.
5. Fine-tuning: Adjust hyperparameters and layers to improve accuracy.
6. Evaluation: Assess the model's performance using metrics like accuracy, precision, and recall on a validation dataset.	- Effective at capturing multi-scale features from data.
- Improved classification accuracy for complex datasets.
- Hierarchical representation learning enhances generalization.
- Suitable for various applications, including image and time series classification.	- Computationally intensive and requires significant resources for training.
- May be prone to overfitting on small datasets if not properly regularized.
- Complexity in model design and tuning hyperparameters can be challenging.
- Requires a large amount of labeled data for effective training.
DWT- based with DNN 				
SVM	Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that maximizes the margin between different classes in the feature space. SVM can also handle non-linear data using kernel functions to transform input into higher dimensions.	1. Data Preparation: Preprocess the dataset, including normalization and handling missing values.
2. Choosing the Kernel: Select an appropriate kernel function (linear, polynomial, RBF, etc.) based on the data characteristics.
3. Training the Model: Fit the SVM model to the training data by optimizing the hyperplane that separates classes.
4. Parameter Tuning: Adjust hyperparameters (like C and gamma) using techniques such as GridSearchCV for better performance.
5. Prediction: Use the trained model to predict classes on new or test data.
6. Evaluation: Assess the model's performance using metrics such as accuracy, precision, recall, and F1-score.	1. Data Preparation: Preprocess the dataset, including normalization and handling missing values.
2. Choosing the Kernel: Select an appropriate kernel function (linear, polynomial, RBF, etc.) based on the data characteristics.
3. Training the Model: Fit the SVM model to the training data by optimizing the hyperplane that separates classes.
4. Parameter Tuning: Adjust hyperparameters (like C and gamma) using techniques such as GridSearchCV for better performance.
5. Prediction: Use the trained model to predict classes on new or test data.
6. Evaluation: Assess the model's performance using metrics such as accuracy, precision, recall, and F1-score.	- Computationally intensive, particularly for large datasets.
- Performance is heavily dependent on the choice of kernel and parameters.
- Less effective on very large datasets or noisy data.
- Requires careful tuning of hyperparameters to achieve optimal results.
Random Forest	Random Forest is an ensemble learning method that constructs a multitude of decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. It leverages bagging (bootstrap aggregating) to reduce variance and improve prediction accuracy.	1. Data Preparation: Preprocess the dataset, including handling missing values and encoding categorical variables.
2. Bootstrap Sampling: Create multiple subsets of the original dataset by random sampling with replacement.
3. Building Decision Trees: For each subset, build a decision tree using a random selection of features at each split.
4. Aggregating Results: For classification, take the majority vote of the predictions from all trees; for regression, calculate the average of the predictions.
5. Evaluation: Assess the model's performance using metrics such as accuracy, precision, recall, and F1-score on a test dataset.	- Handles large datasets with higher dimensionality well.
- Reduces the risk of overfitting compared to individual decision trees.
- Provides feature importance, which helps in understanding data.
- Robust to noise and can handle missing values well.	- Can be slower to predict than individual decision trees due to the ensemble nature.
- Requires more computational resources and memory, especially with a large number of trees.
- Less interpretable than a single decision tree due to its complexity.
- Performance can degrade if the number of trees is too high, leading to diminishing returns.
XGB
Classifier	XGBoost (Extreme Gradient Boosting) is a scalable and flexible implementation of gradient boosting framework that is designed for speed and performance. It is an ensemble learning method that builds decision trees in a sequential manner, where each tree aims to correct the errors of the previous trees. It is widely used for classification and regression tasks due to its accuracy and efficiency.	1. Data Preparation: Preprocess the dataset, handling missing values, and encoding categorical variables.
2. Feature Selection: Choose relevant features for model training, as XGBoost can handle large feature sets.
3. Model Initialization: Specify hyperparameters like learning rate, max depth, and number of estimators.
4. Training: Fit the model to the training data, building trees iteratively and adjusting based on the error of previous trees.
5. Evaluation: Assess the model's performance using metrics such as accuracy, precision, recall, and F1-score on a test dataset.	- High predictive accuracy and performance due to its ensemble nature and optimization techniques.
- Regularization capabilities help to reduce overfitting.
- Handles missing data internally, making preprocessing simpler.
- Highly efficient in terms of speed and memory usage, especially for large datasets.
- Supports parallel and distributed computing for faster training.	- Can be complex to tune due to the number of hyperparameters available.
- Interpretation of the model can be challenging compared to simpler models.
- May require more computational resources compared to simpler models (e.g., logistic regression).
- Sensitive to noisy data and outliers, which can negatively impact performance.

Table 1. Summary of Relevant Research on EEG Classification
 













	Methodology

3.1 Dataset Description
https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition/data 

Table 2 presents a summary of classification techniques used in the analysis of  EEG dataset. Each model is described along with its steps, equations, pros, and cons.






Table 3: Descriptive Statistics of the Dataset Features




Figure 1 shows the correlation between the dataset features. 

Figure 2 depicts a comprehensive framework for a machine learning model aimed at predicting the "Diagnosis" feature within a EEG  dataset. 


3.2 Data Pre-processing Phase

1. Handling Missing Values and Outliers:
   - Missing Values: Missing data can affect the accuracy and reliability of models. It is important to either remove these null entries or fill them with appropriate values (imputation) to maintain data consistency.
   - Outliers: Outliers, which are data points that significantly differ from the norm, can distort model performance. Removing these outliers ensures the model accurately captures the typical data patterns. Common techniques for detecting and handling outliers include Z-score, Interquartile Range (IQR), and visualization methods like box plots.

2. Normalization and Addressing Data Imbalance:
   - Normalization: Normalizing the dataset by scaling features to a common range, such as [0, 1] or [-1, 1], helps prevent features with large ranges from dominating the analysis. Methods such as Min-Max Scaling or Z-score normalization are commonly applied.
   - Balancing: Epileptic seizure datasets may be imbalanced, where one class (e.g., non-seizure) has significantly more instances than the other (e.g., seizure). Techniques like SMOTE (Synthetic Minority Over-sampling Technique) or undersampling the majority class can be used to balance the dataset, ensuring the model performs well across all classes.

3. Class Labeling:
   - The target variable, "Diagnosis," is labeled as binary: "0" represents the absence of seizures (not affected), and "1" represents the presence of seizures (affected). Proper labeling ensures that the model correctly identifies and classifies seizure events.
4. Data Splitting and Evaluation Phase
- Training (70%): The model is trained on 70% of the dataset, where it learns the underlying patterns and relationships in the data. This phase helps the model adjust its parameters to minimize errors and improve predictive accuracy.
- Validation (30%): The remaining 30% of the data is used to evaluate the model's ability to generalize to unseen data. This step assesses the model's performance, helping detect overfitting and ensuring it can make accurate predictions on new, real-world data.


3.3 Model Architecture  (Voting Classifier)

A Voting Classifier is an ensemble machine learning method that combines the predictions of multiple models to improve overall performance. It works by aggregating the predictions from different classifiers (e.g., decision trees, support vector machines, logistic regression) to make a final decision.

There are two main types of Voting Classifiers:
	Hard Voting: Each model votes for a class, and the class with the most votes is chosen.
How it Works:
	Each model in the ensemble predicts a class label (e.g., "Class 1" or "Class 0").
	The class with the most votes from the models becomes the final prediction.
	In case of a tie (i.e., equal number of votes for multiple classes), a random class among the tied classes is selected.
In hard voting, each model in the ensemble predicts the class label for an input, and the final prediction is the class that receives the majority vote from the individual models. The mathematical equation for hard voting can be expressed as:
y^=mode(y1,y2,…,yM)\hat{y} = \text{mode}(y_1, y_2, \dots, y_M)y^=mode(y1,y2,…,yM)
Where:
	y^\hat{y}y^ is the final predicted class.
	y1,y2,…,yMy_1, y_2, \dots, y_My1,y2,…,yM are the predicted classes from the MMM individual classifiers.
	mode\text{mode}mode refers to the class label that appears most frequently among the predictions.

2. Soft Voting: The class with the highest average predicted probability across models is selected.
How it Works:
	Each model predicts the probability of an instance belonging to each class.
	The probabilities for each class are averaged across all models.
	The class with the highest average probability is selected as the final prediction.
In soft voting, each model outputs the probability distribution over classes, and the final prediction is based on the average of these probabilities. The class with the highest average probability is selected. The mathematical equation for soft voting is:
y^=arg⁡max⁡k(1M∑i=1MPi(k))\hat{y} = \arg\max_k \left( \frac{1}{M} \sum_{i=1}^{M} P_i(k) \right)y^=argkmax(M1i=1∑MPi(k))
Where:
	y^\hat{y}y^ is the final predicted class.
	MMM is the number of classifiers.
	Pi(k)P_i(k)Pi(k) is the predicted probability of class kkk from the iii-th classifier.
	The sum ∑i=1MPi(k)\sum_{i=1}^{M} P_i(k)∑i=1MPi(k) averages the probabilities over all classifiers, and the class kkk with the highest averaged probability is selected.



Key Points:
- Advantages: Improves accuracy, reduces overfitting, and is easy to implement.
- Disadvantages: Requires diverse models, can be computationally expensive, and may not outperform more advanced techniques like boosting.

Voting Classifiers are effective for both classification and regression tasks, especially when combining diverse models.
Advantages and Disadvantages of the Voting Classifier

Advantages	Disadvantages
Improved accuracy due to combining models

	Requires diversity among base models


More robust to noise and variance

	May not outperform advanced methods (boosting)


Simple and easy to implement

	Computationally expensive


Flexible—can combine different algorithms

	Hard voting ignores model confidence


Reduces overfitting

	Bias-variance trade-off can be challenging


Applicable to classification and regression

	Complexity in parameter tuning


	Dependent on good individual models
	
In summary, while the Voting Classifier offers simplicity and robust performance, it may not always outperform more advanced ensemble methods like boosting or stacking. However, for many practical use cases, it provides a good balance between model diversity and ease of implementation

3.4 Performance Metrics

The performance of each model is assessed using several key evaluation metrics:

1. Confusion Matri:  
   - A matrix that provides an overview of the classification results by displaying the counts of:
     - True Positives (TP): Correctly predicted malignant cases (classified as "M").
     - False Positives (FP): Benign cases incorrectly predicted as malignant (classified as "M").
     - True Negatives (TN): Correctly predicted benign cases (classified as "B").
     - False Negatives (FN): Malignant cases incorrectly predicted as benign (classified as "B").
   This matrix allows for a more detailed breakdown of classification performance beyond simple accuracy.

2. Accuracy:  
   - Represents the overall proportion of correct predictions. It is calculated as:
Accuracy=(TP + TN)/(TP + FP + TN + FN)

     Accuracy measures how often the model makes correct predictions, but it may not be sufficient when dealing with imbalanced datasets.

3. Precision:  
   - The ratio of correctly predicted positive instances to all predicted positive instances. It measures the reliability of the model when it predicts a positive case:
Precision=(TP )/(TP + FP )

     High precision indicates a low false positive rate, making it a valuable metric when false positives are costly.

4. Recall (Sensitivity or True Positive Rate):  
   - The proportion of actual positive cases (malignant) that the model correctly identifies. It evaluates the model’s ability to detect true positives:
Recall=(TP )/(TP +  FN)

     High recall is essential in cases where missing a positive instance (false negative) is more critical.

5. Specificity (True Negative Rate):  
   - The proportion of actual negative cases (benign) that the model correctly identifies. Specificity measures the ability of the model to avoid false positives:
Specificity =(TN )/(TN +  FP)

     A high specificity ensures that benign cases are not incorrectly classified as malignant.

6. F1 Score (F-measure):  
   - The harmonic mean of Precision and Recall, providing a balanced metric that considers both false positives and false negatives:
F1 - score =2*  ((Precision × Recall))/((Precision + Recall) )   

     The F1 score is particularly useful when there is an uneven class distribution, as it balances the trade-off between precision and recall.





















	Results and Analysis
Model	Accuracy	Precision	Recall	F1-Score	Time (seconds)
Multi-DenseBlock Concatenation Network (MDBCN)	0.97	0.97 (No Seizure) / 0.98 (Seizure)	1.00 (No Seizure) / 0.88 (Seizure)	0.98 (No Seizure) / 0.93 (Seizure)	351.10 (Training)
DWT-based feature with DNN	0.98	0.99 (No Seizure) / 0.93 (Seizure)	0.98 (No Seizure) / 0.97 (Seizure)	0.99 (No Seizure) / 0.95 (Seizure)	12.40 (Training)
CNN	0.99	0.99 (No Seizure) / 0.98 (Seizure)	1.00 (No Seizure) / 0.95 (Seizure)	0.99 (No Seizure) / 0.97 (Seizure)	531.76 (Training)
SVM	0.98	0.98 (No Seizure) / 0.97 (Seizure)	0.99 (No Seizure) / 0.91 (Seizure)	0.99 (No Seizure) / 0.94 (Seizure)	11.23 (Training)
Random Forest	0.98	0.99 (No Seizure) / 0.95 (Seizure)	0.99 (No Seizure) / 0.95 (Seizure)	0.99 (No Seizure) / 0.95 (Seizure)	19.96 (Training)
XGBClassifier	0.98	0.98 (No Seizure) / 0.97 (Seizure)	0.99 (No Seizure) / 0.91 (Seizure)	0.99 (No Seizure) / 0.94 (Seizure)	4.24 (Training)
Voting Classifier (Proposed)	1.00	1.00 / 1.00	1.00 / 1.00	1.00 / 1.00	9.63 (Training)



Figure: The heatmap correlation of the features.

	Discussion and Limitations

	Conclusions and Future Work





Declarations
Authors' Contributions
This work was carried out in collaboration among all authors. All Authors designed the study, performed the statistical analysis and wrote the protocol. All authors read and approved the final manuscript.

Data and code availability
The data that support the findings of this study are available in https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition/data 

Attribute Information:
The original dataset from the reference consists of 5 different folders, each with 100 files, with each file representing a single subject/person. Each file is a recording of brain activity for 23.6 seconds. The corresponding time-series is sampled into 4097 data points. Each data point is the value of the EEG recording at a different point in time. So we have total 500 individuals with each has 4097 data points for 23.5 seconds.
We divided and shuffled every 4097 data points into 23 chunks, each chunk contains 178 data points for 1 second, and each data point is the value of the EEG recording at a different point in time. So now we have 23 x 500 = 11500 pieces of information(row), each information contains 178 data points for 1 second(column), the last column represents the label y {1,2,3,4,5}.

  
The code that support the findings of this study are available in: 


Funding: N/A

Declaration of competing interest
The authors affirm that there is no conflict of interest. They declare the absence of any known competing financial interests or personal relationships that could have been perceived to influence the work reported in this paper.
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Ethical Statement: “This article does not contain any studies with human participants or animals performed by any of the authors.”                         
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References

[1]	A. Shariat, A. Zarei, S. A. Karvigh, and B. M. Asl, “Automatic detection of epileptic seizures using Riemannian geometry from scalp EEG recordings,” Med Biol Eng Comput, vol. 59, no. 7–8, pp. 1431–1445, Aug. 2021, doi: 10.1007/s11517-021-02385-z.
[2]	R. Wang, H. Wang, L. Shi, C. Han, and Y. Che, “Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA,” Entropy, vol. 24, no. 11, Art. no. 11, Nov. 2022, doi: 10.3390/e24111540.
[3]	M. Li, W. Chen, and M. Xia, “GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection,” Biomedical Signal Processing and Control, vol. 80, p. 104274, Feb. 2023, doi: 10.1016/j.bspc.2022.104274.
[4]	Y. Xiong et al., “Seizure detection algorithm based on fusion of spatio-temporal network constructed with dispersion index,” Biomedical Signal Processing and Control, vol. 79, p. 104155, Jan. 2023, doi: 10.1016/j.bspc.2022.104155.
[5]	L. Jiang, J. He, H. Pan, D. Wu, T. Jiang, and J. Liu, “Seizure detection algorithm based on improved functional brain network structure feature extraction,” Biomedical Signal Processing and Control, vol. 79, p. 104053, Jan. 2023, doi: 10.1016/j.bspc.2022.104053.
[6]	F. Hassan, S. F. Hussain, and S. M. Qaisar, “Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data,” Journal of Healthcare Engineering, vol. 2022, no. 1, p. 9579422, 2022, doi: 10.1155/2022/9579422.
[7]	S. Davidson et al., “Seizure Classification Using BERT NLP and a Comparison of Source Isolation Techniques with Two Different Time-Frequency Analysis,” in 2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Dec. 2022, pp. 1–7. doi: 10.1109/SPMB55497.2022.10014769.
[8]	Z. Li, K. Hwang, K. Li, J. Wu, and T. Ji, “Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity,” Sci Rep, vol. 12, no. 1, p. 18998, Nov. 2022, doi: 10.1038/s41598-022-23656-1.
[9]	M. Varlı and H. Yılmaz, “Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning,” Journal of Computational Science, vol. 67, p. 101943, Mar. 2023, doi: 10.1016/j.jocs.2023.101943.
[10]	A. Einizade, S. Nasiri, M. Mozafari, S. H. Sardouie, and G. D. Clifford, “Explainable automated seizure detection using attentive deep multi-view networks,” Biomedical Signal Processing and Control, vol. 79, p. 104076, Jan. 2023, doi: 10.1016/j.bspc.2022.104076.
[11]	H. Ji et al., “An effective fusion model for seizure prediction: GAMRNN,” Front. Neurosci., vol. 17, Aug. 2023, doi: 10.3389/fnins.2023.1246995.
[12]	M. Shen, P. Wen, B. Song, and Y. Li, “Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network,” Biomedical Signal Processing and Control, vol. 82, p. 104566, Apr. 2023, doi: 10.1016/j.bspc.2022.104566.
[13]	T. J. Rani and D. Kavitha, “Effective Epileptic Seizure Detection Using Enhanced Salp Swarm Algorithm-based Long Short-Term Memory Network,” IETE Journal of Research, vol. 70, no. 2, pp. 1538–1555, Feb. 2024, doi: 10.1080/03772063.2022.2153090.








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