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Finally the Evaluation: Evaluate the trained model on a sepa- reducing subjectivity. Moreover, using extensive datasets for
rate test dataset to assess its performance metrics, including training purposes empowers deep learning models to learn
accuracy, sensitivity, specificity, and F1 score. Deployment from diverse examples, progressively refining their accuracy
step (Optional): and performance. Despite the considerable potential of deep
learning approaches in epileptic detection, several challenges
• If the model performs satisfactorily, deploy it in clinical demand attention. Foremost among these is the absence of
settings to assist healthcare professionals in epilepsy standardized datasets for training and evaluating deep learning
diagnosis. models. While several publicly accessible datasets exist, they
often exhibit variation in terms of patient numbers, seizure
• Implement necessary security and privacy measures if types, and recording equipment employed. This variability
the model involves patient data. poses difficulties in comparing outcomes across different stud-
ies and hinders the generalizability of deep learning models.
Research papers have proposed various deep learning models Another pertinent challenge pertains to the interpretability of
for EEG classification, each with specific objectives, datasets, deep learning models. While these models can attain impres-
preprocessing techniques, classification methods, and soft- sive accuracy in detecting epileptic events, deciphering the
ware/tools used present in Table II. These papers focus on underlying rationale for their predictions can prove intricate.
different EEG classification approaches with varying prepro- The intricate nature of deep learning architectures often makes
cessing techniques and achieve different levels of accuracy it challenging to gain insights into the decision-making pro-
based on their specific objectives and datasets. Every research cess of these models. In summary, the recent strides made
endeavor has limitations that researchers should strive to mit- by deep learning techniques in epileptic detection, facilitated
igate for more precise diagnoses. Simultaneously, there are by their automatic feature extraction and extensive learning
also advantages that researchers can leverage to enhance their capabilities, hold substantial promise for improving diagnosis
work. These advantages and limitations are detailed in the and treatment. Nevertheless, addressing issues such as dataset
accompanying Table II. Fig. 3 illustrates the frequency with standardization and model interpretability is essential to fully
which distinct deep learning neural network (DNN) methods realize the potential benefits of deep learning in enhancing
were employed across the reviewed papers. epileptic detection.
IV. DATASETS FOR EPILEPTIC DETECTION VI. CONCLUSION
Recording EEG signals is a challenging and laborious pro- This review has conducted a thorough examination of the
cess. Nowadays, numerous internet datasets may be utilized use of deep learning techniques in the detection of epilep-
in research. Recording EEG signals is a challenging and labo- tic seizures. The advances in using deep learning to iden-
rious process. Additionally, it takes a lot of time to evaluate tify epileptic seizures using electroencephalogram (EEG) sig-
complex, slow, and fast-varying EEG patterns. Various used nals are promising. Combining different deep learning ar-
certain freely available web datasets, while others require per- chitectures, such as convolutional neural networks (CNNs),
mission from the owners. Table III contains a list of the most recurrent neural networks (RNNs), and hybrid models, has
popular EEG datasets. The description of the most popular greatly improved the precision and efficiency of epileptic de-
datasets and the website for each one is given in Table IV. tection systems. With the growing integration of electronic
healthcare, the clinical imperative of an accurate, automated,
V. DISCUSSIONS computer-assisted seizure diagnosis system is more important
than ever. However, this pursuit is not without its difficul-
Deep learning techniques have exhibited remarkable advance- ties. The inherent weakness, instability, and noise of EEG
ments in recent years when applied to epileptic detection, signals—the most commonly used diagnostic signal in this
employing methods such as Convolutional Neural Networks context—underscores the need for novel approaches. Dealing
(CNNs), Recurrent Neural Networks (RNNs), and Deep Belief with scarce datasets emphasizes the importance of addressing
Networks (DBNs) to analyze Electroencephalogram (EEG) data scarcity through augmentation techniques. Furthermore,
signals. These techniques have demonstrated the capability to the possibility of automatic EEG signal recording via syn-
identify epileptic activity from EEG data with notable accu- chronized camera monitoring and artifact removal to isolate
racy automatically. A fundamental strength of deep learning seizure-related activity holds promise for improved diagnostic
methods lies in their capacity to extract pertinent features accuracy. These developments are critical, given the lengthy
from complex EEG signals autonomously. Unlike traditional recording times frequently required for definitive diagnosis.
methods that necessitate manual feature extraction, deep learn- Deep learning-based epileptic detection has enormous poten-
ing obviates this step, mitigating the risk of human error and
