The document discusses using deep learning techniques to detect epileptic seizures through electroencephalography (EEG) and magnetic resonance imaging (MRI) data. It provides an overview of previous feature extraction methods used in standard machine learning and how deep learning automates this process. The paper then reviews studies that have used various deep learning tools like convolutional neural networks to perform automated detection of epileptic seizures from neuroimaging data like EEG signals. It describes preprocessing steps like noise removal and signal preparation used before applying deep learning models to the EEG data. Finally, it discusses how both one-dimensional and two-dimensional convolutional neural networks have been implemented for epileptic seizure detection.

1. Epileptic Seizures
Detection Using Deep
Learning Techniques

2. Abstract
Using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities, a
number of screening strategies have been put forth to identify epileptic seizures. Deep
learning is one branch of artificial intelligence, which covers a wide range of topics (DL).
Prior to the development of deep learning, feature extraction was used in standard
machine learning techniques. As a result, they were only as effective as the people who had
manually created the features. DL, on the other hand, completely automates the features'
extraction and categorization processes. Significant advancements in medicine have been
made thanks to the introduction of these procedures in a number of fields, including the
identification of epileptic seizures. This paper provides a thorough summary of studies that
have looked at automated epileptic seizure identification using DL approaches and
neuroimaging modalities.
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3. Keywords
◎ Epileptic seizures
◎ Diagnosis
◎ EEG
◎ MRI
◎ Feature extraction
◎ Classification
◎ Deep learning
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4. Introduction
One of the most widespread neurological conditions
affecting people, epilepsy is a non-communicable
disease that is frequently accompanied by sudden
attacks. A quick and early disturbance in the brain's
electrical activity known as a sudden episode of
seizures disturbs a region of the body or the entire
body. There are over 60 million epileptic seizures of
various types each year. Occasionally, these attacks
result in cognitive problems that can seriously harm
the patient's body. In addition, epileptic seizure
patients can experience emotional anguish as a
result of embarrassment and an unsuitable social
status. As a result, patients can benefit and have a
higher quality of life if epileptic seizures are detected
early.
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Number of times each DL tool was used for
automated detection of epileptic seizure by various
studies.

5. Preprocessing
Three processes make up the preprocessing for creating CADS when employing DL models
with EEG signals: noise removal, normalisation, and signal preparation for DL network
applications. Finite impulse response (FIR) or infinite impulse response (IIR) filters are
typically employed to remove excess signal noise during the noise removal process. Then
normalisation is carried out through different techniques, such as the z-score technique.
Finally, many techniques are used to prepare the signals for the deployment of deep
networks in the time domain, frequency, and time-frequency domains.
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6. Review of Deep Learning Techniques
Deep neural networks are models that have more than two hidden layers, as opposed to
normal neural networks, or so-called shallow networks. As networks grow in size, the
number of network parameters also explodes, necessitating the use of appropriate
learning techniques as well as precautions to prevent the learnt network from becoming
overly optimised. The number of trainable parameters is drastically decreased by
convolutional networks since they use filters convolved with input patterns rather than
multiplying a weight vector (matrix).
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7. Convolutional Neural Networks (CNNs)
One class of the most well-liked DL networks is the CNN, to which the majority of machine
learning research has been focused. In recent years, they have been used to one- and two-
dimensional architectures for the detection and prediction of diseases utilising biological
signals even though they were first proposed for image-processing applications. Epileptic
seizure detection utilising EEG signals is a common application for this class of DL
networks. In two-dimensional convolutional neural networks (2D-CNN), one-dimensional
(1D) EEG signals are converted into two-dimensional plots using visualisation techniques
like spectrograms, higher-order bispectrum, and wavelet transforms before being used as
the convolutional network's input. The input of convolutional networks in 1D architectures
is applied using the EEG signals in a one-dimensional form. To enable it to process the 1D-
EEG signals, these networks modify the 2D-basic CNN's architecture. Since one-
dimensional convolutional neural networks (1D-CNNs) and two-dimensional convolutional
neural networks (2D-CNNs) are both employed in the field of epileptic seizures detection,
they are each examined independently.
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8. 8
2D Convolutional Neural Networks (2D-
CNNs)
Deep 2D networks are now
employed in a variety of medical
applications, including the diagnosis
of COVID-19 in CT and X-ray and
autistic spectrum disorders from
MRI modalities. In an effort to avoid
the challenges of earlier networks
and perform better on more
challenging problems, Krizovsky et
al. first proposed this network in
2012 to solve image classification
problems. Shortly after, other
researchers quickly used similar
networks for other tasks, such as
medical image classification.
A typical 2D-CNN for epileptic seizure detection

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