EEG-Based Epileptic Seizure Detection Using Machine Learning Techniques

 

EEG-Based Epileptic Seizure Detection Using Machine Learning Techniques

Smirty Sharma(1), Kshitiz Boral(1), Rahul Kumar Sharma(1), Dibya Thapa(2)

(1)Student, Department of Computer Engineering, (2)Assistant Professor, Department of Computer Engineering, Sikkim Institute of Science and Technology, Namchi, Sikkim, India

Abstract Epileptic seizures are sudden neurological events that require timely detection and intervention. Electroencephalogram (EEG) signals, due to their non-invasive nature, are widely used for seizure analysis. This paper presents a complete automated seizure detection framework using the Childrens Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) Scalp EEG Database. The system integrates preprocessing, feature extraction, data balancing, data augmentation, and classification into a robust pipeline. Key features were derived using power spectral density, wavelet decomposition, entropy, and statistical measures. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was employed. Multiple machine learning classifiers, including Support Vector Machine (SVM), Random Forest (RF), Extra Trees (ET), Logistic Regression (LR), XG Boost (XGB), and Light GBM (LGBM) were evaluated. Performance was assessed using 5-fold stratified cross- validation, demonstrating that the integration of spectral and nonlinear EEG features with advanced machine learning methods significantly improves seizure detection accuracy.

Keywords: Epileptic seizure detection; Electroencephalogram (EEG); Machine learning, Feature extraction; Childrens Hospital Boston- Massachusetts Institute of Technology (CHB-MIT).

1. INTRODUCTION

   Epilepsy is a chronic neurological condition characterized by recurrent, unprovoked seizures that result from sudden, abnormal, and excessive neuronal discharges in the brain. Epilepsy is the most common chronic brain disease and affects people of all ages. More than 50 million people worldwide have epilepsy; nearly 80% of them live in low- and middle-income countries. An estimated 70% of people with epilepsy could be seizure free if properly diagnosed and treated [1]. However, about three quarters of people with epilepsy in low-income countries do not get the treatment they need, and this rises to 90% in some countries. In many such countries, many health professionals do not have the training to recognize, diagnose and treat epilepsy

   EEG is one of the most widely used techniques for diagnosing epilepsy because it directly records the brains electrical activity with high temporal resolution. Clinicians rely on EEG to identify abnormal waveforms, such as spikes and sharp waves, that indicate seizure activity. However, manually reviewing EEG data is extremely time-consuming, especially when dealing with long-term monitoring, and results can vary between experts. These challenges highlight the need for automated seizure detection systems that can analyse large amounts of data quickly, accurately, and consistently.

   Machine learning [2]-[4] has shown great promise in this area. Traditional methods use handcrafted features, such as spectral

   energy, wavelet coefficients, entropy, and statistical measures, together with classifiers like SVM, RF, and LR.

   While these approaches are computationally efficient, they often struggle with imbalanced datasets, patient-to-patient variability, and reduced generalizability. Deep learning methods, such as CNNs and RNNs, have achieved strong results but typically require very large labelled datasets and heavy computational resources, making them less practical for real-time or low-resource clinical settings.

   In this study, we propose a complete EEG-based seizure detection framework that addresses these challenges. The system includes preprocessing, multi-domain feature extraction, and class balancing using SMOTE. Multiple machine learning classifiers, including SVM, RF, ET, LR, XGB, and LGBM are evaluated to determine their effectiveness in seizure detection. Using the CHB-MIT EEG database and 5- fold cross-validation, our method achieves high accuracy and efficiency, demonstrating strong potential for reliable real-time clinical use.

2. LITERATURE REVIEW

   EEG based seizure detection has received significant research attention due to its ability to capture brain activity directly, yet the complex and non-stationary nature of EEG signals makes reliable classification difficult. Deep learning approaches have been widely explored to address this problem. Dili et al. [5] proposed a Continuous Wavelet Transform based depthwise CNN model that extracts timefrequency representations from EEG signals to identify seizure patterns. Although the method achieved strong detection performance, it required large training data and high computational cost, limiting practical deployment.

   To better understand the overall research landscape, Saadoon et al. [6] reviewed machine learning and deep learning techniques used for seizure prediction. Their study highlighted persistent issues such as data imbalance, inter-patient variability and limited interpretability of models, indicating that accuracy alone is insufficient for clinical usage. Ensemble learning has therefore been explored as an alternative. Al-Adhaileh et al. [7] introduced a multi-model classification approach that combines multiple classifiers to improve robustness and reduce individual model bias, demonstrating more stable performance on EEG datasets.

   Another major direction focuses on interpretability of deep models. Jonna and Natarajan [8] developed a hybrid Wavelet CNN-LSTM framework that learns spatial and temporal EEG features while using SHAP analysis to explain prediction behaviour. This approach improved transparency of

   predictions, making deep learning outputs easier to understand in medical settings. Similarly, Tan et al. [9] reviewed nonlinear dynamics and deep learning based seizure detection methods and emphasized the trade-off between model complexity and efficiency, suggesting that lightweight yet reliable approaches are essential for real-time applications.

   Beyond seizure detection, EEG classification research also shows that efficient machine learning models can perform competitively. Thapa and Rai [10] proposed the FREQ-EER framework, which analyzes EEG frequency bands and applies data augmentation with an ensemble of lightweight classifiers. Their results demonstrated that interpretable and computationally efficient models can achieve high accuracy without relying on heavy deep neural networks.

   Overall, existing studies show a clear progression from complex deep learning models toward interpretable and efficient hybrid or ensemble approaches. However, achieving a balance between accuracy, robustness, interpretability and computational cost remains an open challenge, motivating further exploration of optimized machine learning based seizure detection frameworks.

3. PROPOSED METHODOLOGY

   Epileptic seizure detection from EEG signals is a complex process that requires careful handling of high-dimensional, noisy brainwave data. Raw EEG recordings often contain artifacts caused by eye movements, muscle activity, or environmental interference, which can obscure the subtle patterns indicative of seizures. To develop a reliable, reproducible, and generalizable detection model, a structured workflow combining signal processing, data augmentation, and machine learning was implemented. This section outlines the materials used, the dataset characteristics, and the systematic procedures employed to preprocss, transform, and analyze the EEG signals for accurate seizure detection.

   1. Proposed Methodology

      The proposed seizure detection framework, as illustrated in Figure 1, is a multi-stage pipeline designed to convert preprocessed EEG signals into actionable diagnostic insights. Each stage of the proposed seizure detection pipeline serves a specific purpose in ensuring accurate and robust classification of EEG signals.

      Fig.1. The proposed framework for EEG-based epileptic seizure detection

   2. Dataset and Organization

      The study uses the CHB-MIT Scalp EEG dataset [11] as described by Table 1, which contains long continuous brain signal recordings from children suffering from epilepsy. The data was recorded in a real hospital environment, so it includes normal activity, sleep patterns, and noise along with seizures. All signals were recorded at a sampling rate of 256 Hz and stored in EDF format. To make the analysis manageable, the recordings were divided into fixed 5-second segments. After segmentation, a total of 29,197 epochs were obtained. Among them, 4,785 segments represent seizure activity and 24,412 represent normal brain activity.

      Table 1 Characteristics of CHB-MIT dataset used in the study

      Parameter	Description
      Sampling Frequency	256 Hz
      Recording Format	European Data Format (.edf)
      Number of Subjects	23
      Total EEG files	42 files containing both seizure (ictal) and non-seizure (interictal) data
      Segmentation Strategy	EEG signals segmented into 5 second non- overlapping epochs
      Total number of Epochs	29,197 epochs
      Seizure epochs	4,785 epochs (16.4% in total)
      Non-Seizure epochs	24,412 epochs (83.6% of total)

   3. Signal Preprocessing

      Preprocessing is critical for removing noise, eliminating irrelevant artifacts, and standardizing

      EEG signals before feature extraction. Several techniques are widely employed in the preprocessing of epileptic EEG signals, including

      1. Re-referencing: If all EEG channels were available, Common Average Referencing (CAR) was used. This means the average signal of all channels was subtracted from each channel to reduce shared noise and make the brain signals clearer.
      2. Band-pass filtering: A zero-phase finite impulse response (FIR) filter was applied between 0.5 and 100Hz:

         () = 1((). {()}) (1) where () is the original EEG signal in the time domain,

         {()} is the Fourier transform of the signal, converting it to the frequency domain, () is the frequency response of the filter, allowing frequencies between 0.5100 Hz to pass and removing unwanted low (<0.5 Hz) and high (>100 Hz) frequency components, 1{} is the inverse Fourier transform, converting the filtered signal back to the time domain. This filtering step removes slow baseline drifts and high-frequency

         noise such as muscle artifacts, retaining only the physiologically relevant EEG components for seizure detection.

      3. Notch filtering: EEG signals often contain power-line interference from electrical equipment, which usually appears as a constant noise at 50 Hz (or 60 Hz depending on the region). To remove this, a notch filter was applied . A notch filter is a very narrow band-stop filter that specifically attenuates signals at a particular frequency while leaving other frequencies mostly unchanged.

         Mathematically, if ()is the EEG signal, the notch-filtered signal ()can be represented as:

         () = 1notch() () (2)

         where notch() is the frequency response of the notch filter, which strongly suppresses the 50 Hz component but allows other frequencies to pass. This step effectively removes line noise without affecting the rest of the EEG signal, preserving the relevant brain activity.

      4. Epoch segmentation:The continuous EEG recordings were divided into 5-second segments (called epochs), with 1,280 data points per channel. This makes it easier to analyze the signals in smaller, consistent time windows.
      5. Artifact rejection: Epochs exhibiting peak-to-peak amplitudes greater than 500V were automatically discarded to avoid contamination by non-brain sources (e.g., eye blinks, motion).
   4. Data Augmentation

      To enhance the robustness and generalization of seizure detection models, various data augmentation techniques were applied to the EEG signals. EEG signals are highly variable due to factors such as patient-specific brain activity, electrode placement, environmental noise, and physiological changes. This variability often makes models prone to overfitting and reduces their ability to generalize across different subjects or recording conditions. Data augmentation addresses this challenge by artificially expanding the training dataset and introducing controlled variability, thereby improving the models ability to recognize seizure patterns under diverse conditions.

      1. Gaussian Noise Augmentation

         Gaussian noise augmentation involves adding random

      2. Frequency Shift Augmentation

         This method involves slightly shifting the frequency components of EEG signals to simulate variations due to electrode placement or individual differences [13]. It helps in making models invariant to such frequency shifts.

         Mathematically it is denoted by:

         where F denotes the Fourier transform, 1denotes the inverse Fourier transform, f is the frequency shift applied to the signal.

      3. Magnitude Warping

         Magnitude warping involves applying a smooth scaling function to the amplitude of EEG signals. This technique simulates variations in signal amplitude due to recording conditions or physiological changes.

         Mathematically it is denoted by:

         where () is a scaling factor determined by cubic spline interpolation of randomly generated nodes, () is the original EEG signal at time .

      4. Time Warping

         Time warping involves stretching or compressing the time axis of EEG signals to simulate variations in signal duration. This method helps in making models invariant to such temporal variations [14].

         Mathematically it is denoted by:

         where is the warped time index, determined by a smooth warping function, aug()is the original EEG signal at time

      5. Random Channel Dropping

      This technique involves randomly dropping or masking certain EEG channels during training to simulate electrode failures or noisy channels. It helps in making models robust to incomplete or noisy data [15].

      The mathematical expression of Random Channel Dropping is

      () if channel is not dropped

      noise to EEG signals to simulate real-world interference, such as electrical noise or muscle artifacts. This technique helps in

      0 if channel is dropped (7)

      making models more robust to such disturbances [12]. Mathematically it can be expressed as:

      where x(t) is the original EEG signal at time t,

      () (0, 2) is Gaussian noise with mean 0 and variance

      2 .

      where () is the original EEG signal at time .

      Some channels are randomly set to zero to simulate electrode failure.

   5. Feature Extraction

      Feature extraction converts raw EEG signals into meaningfl representations for seizure detection. M ulti- domain features capture temporal, spectral, and nonlinear characteristics that distinguish seizure and non-seizure states.

      1. Power Spectral Density

         Spectral analysis using Power Spectral Density (PSD) quantifies EEG activity across standard frequency bands: delta (0.54 Hz), theta (48 Hz), alpha (813 Hz), beta (1330 Hz), and gamma (30100 Hz). PSD features, estimated using Welchs method, capture abnormal energy shiftsparticularly in delta and theta bandsassociated with seizure activity.

         Mathematically, the PSD estimate at frequency f is given by:

         where L is the number of segments, M is the length of each segment, () is the signal in the lth segment, () is the window function (e.g., Hamming window),

         = 1 1 2() is a normalization factor accounting for

         the window energy.

      2. Wavelet Features

         For feature extraction, a Discrete Wavelet Transform (DWT) was applied using the Daubechies-4 (db4) mother

   6. Class Imbalance Handling

      Seizure events made up only 16.4% of the dataset, creating a strong imbalance between seizure and non-seizure classes. To overcome this issue, the Synthetic Minority Oversampling Technique (SMOTE) was applied within each training fold. SMOTE generates synthetic seizure samples by interpolating between an existing minority instance and one of its k-nearest neighbours xnn, as defined by:

      Here, is a random interpolation factor sampled from a uniform distribution. This balancing step reduces the dominance of non- seizure events and improves the classifiers sensitivity to seizure detection, leading to more reliable and robust performance [19].

   7. Feature Scaling and Selection

      Robust scaling was applied to minimize the influence of outliers in the EEG feature set. Each feature was normalized using the transformation:

      wavelet with four levels of decomposition. Each EEG epoch was decomposed into multiple sub-bands, and from the

      resulting coefficients, statistical descriptors such as mean, standard deviation, maximum absolute value, and signal energy were calculated.

      The wavelet coefficients, were obtained as

      where ,() denotes the wavelet basis function at scale j and position k. Wavelet-based features are widely used in seizure detection due to their ability to represent both time and frequency information in non-stationary EEG signals [16].

      1. Entropy Features

         Entropy-based measures were employed to capture nonlinear signal dynamics. Shannon entropy was calculated as

         IQR denotes the interquartile range. To further improve discriminative power, an ANOVA F-test (Select K Best) was used to retain the top 50 features. This reduced redundancy, improved classification efficiency, and ensured that only the most informative attributes were preserved for downstream modelling [20].

   8. Classification Models

   To classify seizure and non-seizure EEG segments, six supervised machine learning algorithms were implemented and systematically evaluated. These models were selected to cover linear, kernel-based, tree-based, neural network, and gradient boosting approaches, ensuring diversity in decision-making mechanisms.

   1. Logistic Regression (LR)

      Logistic Regression is a linear probabilistic classifier

      where pi is the normalized histogram probability of the signal amplitude. Entropy quantifies the degree of randomness in EEG signals, which typically increases during seizure events [17].

      that estimates the likelihood of an input belonging to the seizure class using the logistic function:

      1. Statistical Features

      Several time-domain statistical features were extracted from each EEG epoch, such as mean, variance, skewness, kurtosis, interquartile range (IQR), and zero-crossing rate. These features capture the overall distribution, asymmetry, variability, and shape of the signal, providing compact yet informative descriptions of waveform dynamics. When combined with frequency-domain and nonlinear measures, they offer complementary insights that strengthen the representation of seizure and non-seizure activity [18].

      where represents the model coefficients and x denotes the feature vector. Its advantages include simplicity, interpretability, and low computational cost. However, its reliance on linear decision boundaries limits its ability to capture complex EEG dynamics [21].

      3) Support Vector Machine

      SVM aims to find an optimal separating hyperplane that maximizes the margin between seizure and non-seizure data points. The decision function is expressed as:

      where (, )is the kernel function. The Radial Basis Function (RBF) kernel, given as:

      It enables nonlinear mapping into higher dimensions, making SVM well-suited for EEG. Its performance is highly dependent on tuning of and the penalty parameter [22].

      1. Random Forest (RF)

         Random Forest is an ensemble of decision trees, where each tree is trained on a random subset of features and data samples. The final prediction is obtained by majority voting:

4. PERFORMANCE MEASURES

   The proposed models were evaluated using 5-fold stratified cross-validation, preserving the seizure-to-non-seizure ratio in each fold. SMOTE was applied only to the training data to address class imbalance, while feature scaling and selection were restricted to the training set to prevent information leakage. Models were trained on the processed training folds and evaluated on the corresponding validation folds, with final results averaged across all five folds.

   1. 1. Confusion Matrix Analysis

         A confusion matrix is used to evaluate the performance of a classification model by comparing predicted labels with actual labels. It consists of four components: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). From these values, important performance metrics are

         calculated. Accuracy measures overall correctness of

         predictions. Precision indicates how many predicted seizure

         where ()denotes the prediction of tree t. RF is robust to overfitting and captures nonlinear dependencies but can be less interpretable when the ensemble grows large [23].

         1. Extra Trees (ET)

            Extra Trees extend RF by selecting split thresholds at random, rather than optimizing them. This additional randomness increases model diversity and reduces variance, often leading to improved generalization. Like RF, predictions are aggregated via averaging across trees [24].

         2. XG Boost (XGB)

            Extreme Gradient Boosting is a gradient boosting algorithm that builds decision trees sequentially, where each tree corrects errors made by previous trees [25]. The objective function combines a differentiable loss function LLL and a regularization term :

            cases are actually seizures. Recall (Sensitivity) shows how well the model detects actual seizure cases. Specificity measures how accurately the model identifies non-seizure cases [27].

      2. ROC Curve Analysis

   The Receiver Operating Characteristic (ROC) curve evaluates binary cassification performance by plotting the True Positive Rate (Sensitivity) against the False Positive Rate (FPR

   = 1 Specificity) across varying decision thresholds. Each point on the curve represents a different classification threshold, illustrating the trade-off between seizure detection sensitivity and false alarm rate. The Area Under the Curve (AUC) provides a single summary measure of classifier performance:

   AUC = 1.0 indicates perfect discrimination, AUC = 0.5 represents random classification.

   Higher AUC values indicate stronger discriminative capability and robustness. In EEG-based seizure detection, a high AUC confirms that the model reliably distinguishes seizure activity from normal EEG signals across different thresholds [28].

   where, (, ) Loss function, which measures how well predictions match true labels and

   (fk)is the Regularization, penalizes tree complexity to prevent overfitting.

   1. Light GBM (LGB)

   Light GBM is a leaf-wise gradient boosting algorithm optimized for speed and memory efficiency. It grows trees by choosing leaf nodes with the highest loss reduction, improving accuracy:

   2

5. RESULTS AND DISCUSSION

   This section presents the experimental results of the proposed EEG-based epileptic seizure detection framework using the CHB-MIT dataset. The performance of multiple machine learning classifiers is evaluated using 5-fold stratified cross-validation, with emphasis on accuracy, robustness, and the impact of data augmentation and class imbalance handling. The results are analysed to identify the most effective models for reliable seizure detection.

   1. Comparative Performance Analysis

      Table 2 presents the comparative performance of six

      machine learning classifiers under different data augmentation strategies. It is evident that XGBoost consistently achieves the

      where, gi is gradient (error), hi is the second-order gradient (curvature), is L2 regularization on leaf, is minimum split loss.It measures improvement from a split; higher Gain leads to better split. Regularization prevents overfitting [26].

      highest accuracy across all augmentation techniques, followed closely by LightGBM. Under Frequency Shift augmentation, XGBoost attains the best accuracy (97.19%), while Gaussian Noise also maintains strong performance (96.74%), indicating that frequency-preserving augmentations effectively retain discriminative EEG features.

      Random Channel Dropping and Magnitude Warping introduce moderate reductions in performance across all classifiers. However, ensemble-based models, particularly boosting methods, remain comparatively stable, demonstrating their robustness to controlled signal variations.

      In contrast, Time Warping causes a significant decline in accuracy, precision, and specificity for all models. This suggests that temporal distortion disrupts important seizure- related patterns in EEG signals. Overall, the results highlight the superiority of gradient boosting methods and confirm that preserving spectral characteristics is essential for reliable seizure detection.

      Table 2 Classification Performance (%) of All Classifiers Under Data Augmentation Techniques

      *Best results obtained are indicated in bold

   2. ROC Curve Analysis of XGBoost

      Figure 2(ae) shows the ROC curves of the XGBoost classifier under different augmentation strategies. The baseline model achieves the highest AUC (0.98), while Frequency Shift and Gaussian Noise maintain strong discrimination (AUC

      0.97). Random Channel Dropping and Magnitude Warping show moderate degradation, and Time Warping yields the lowest AUC (0.84), indicating sensitivity to temporal distortion.

      1. Frequency shift augmentation ( b) Gaussian noise augmentation

         (c) Magnitude warping augmentation (d) Random channel dropping

         (e) Time warping augmentation

         Fig. 2. Comparison of ROC curves for XGBoost under different data augmentation strategies

   3. ROC Curve Analysis of Random Forest

      Figure 3(ae) presents the ROC curves for Random Forest under different augmentation strategies. The baseline and frequency-based augmentations achieve moderate AUC values (0.95), while Time Warping significantly degrades the ROC performance.

      1. Frequency shift augmentation ( b) Gaussian noise augmentation

         (c) Magnitude warping augmentation (d) Random channel dropping

         E. ROC Curve Analysis of SVM

         Figure 5 (ae) illustrates the ROC curves for SVM. The baseline and frequency-based augmentations achieve AUC values around (0.94), while Time Warping shows reduced discrimination capability.

         (e) Time warping augmentation

         Fig. 3. Comparison of ROC curves for Random Forest under different data augmentation strategies

   4. ROC Curve Analysis of Extra Trees

      Figure 4(ae) shows the ROC curves of Extra Trees under different augmentation strategies. Moderate AUC values are observed for baseline and frequency-based augmentations, while Time Warping shows the weakest ROC performance (0.79).

      1. Frequency shift augmentation (b) Gaussian noise augmentation

   (c) Magnitude warping augmentation (d) Random channel dropping

   (e) Time warping augmentation

   Fig. 4. Comparison of ROC curves for Extra Trees under different data augmentation strategies

   (a) Frequency shift augmentation (b) Gaussian noise augmentation

   (c) Magnitude warping augmentation (d) Random channel dropping

   (e) Time warping augmentation

   Fig. 5. Comparison of ROC curves for SVM under different data augmentation strategies.

   F. ROC Curve Analysis of Logistic Regression

   Figure 6(ae) presents the ROC curves of Logistic Regression. The AUC values remain consistently lower (0.88 baseline), with significant degradation under Time Warping (0.75), confirming limited discriminative capability.

   (a) Frequency shift augmentation (b) Gaussian noise augmentation

   (c) Magnitude warping augmentation (d) Random channel dropping

   (e) Time warping augmentation

   Fig. 6. Comparison of ROC curves for Logistic Regression under different data augmentation strategies.

   G. ROC Curve Analysis

   Figure 7(ae) shows the ROC curves of the LightGBM classifier under different data augmentation strategies. The baseline model achieves a high AUC (0.98), while Frequency Shift and Gaussian Noise maintain AUC values close to 0.97. Magnitude Warping and Random Channel Dropping show moderate degradation, whereas Time Warping results in the lowest AUC (0.83), confirming reduced discriminative capability.

   1. Frequency shift augmentation (b) Gaussian noise augmentation

   (c) Magnitude warping augmentation (d) Random channel dropping

   (e) Time Warping augmentation

   Fig. 7. Comparison of ROC curves for LightGBM under different data augmentation strategies.

6. CONCLUSIONS AND FUTURE WORK

This study presented a comprehensive machine learningbased framework for epileptic seizure detection using EEG signals from the CHB-MIT Scalp EEG Database. The proposed pipeline integrated signal preprocessing, multi-domain feature extraction, class balancing using SMOTE, feature selection, and rigorous evaluation through 5-fold stratified cross- validation. A total of 29,197 EEG epochs were processed, with seizure and non-seizure samples balanced to ensure unbiased training. Among the tested classifiers, XG Boost achieved the highest accuracy of 97.95%, closely followed by Light GBM at 97.85%. The very low cross-validation variance further demonstrated the stability andreliability of the framework.

These results indicate that gradient boosting methods are highly effective for EEG-based seizure detection, providing both robustness and computational efficiency. The proposed framework shows promise for real-time applications and can potentially be adapted for deployment in portable or wearable EEG monitoring systems.

Despite the high performance achieved, several avenues remain open for future research and improvement:

Cross-Subject Generalization: Future work should evaluate the model on unseen patients to ensure clinical reliability.

time and wearable EEG systems using low-power hardware is required.

Artifact Robustness: Incorporating automated artifact removal can improve performance in noisy EEG conditions.

IoT Integration: Cloud- and IoT-based deployment can enable continuous remote seizure monitoring.

Explainability and Validation: Interpretable models and clinical trials are necessary for medical acceptance.

In summary, the proposed EEG-based seizure detection framework demonstrates high accuracy, robustness, and computational efficiency, establishing a solid foundation for

future enhancements, real-time applications, and clinical translation.

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