A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal
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Date
2021-05-20
Journal Title
Journal ISSN
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Publisher
MDPI
Abstract
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent internet
of medical things (IoMT) devices can never be overemphasized. The success of these applications
largely depends on the accuracy of the detection and classification techniques employed. Several
methods have been investigated, proposed and developed over the years. This paper investigates
various seizure detection algorithms and classifications in the last decade, including conventional
techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the
steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding.
A performance comparison was carried out on the different algorithms investigated, and their
advantages and disadvantages were explored. From our survey, much attention has recently been
paid to exploring the efficacy of deep learning algorithms in seizure detection and classification,
which are employed in other areas such as image processing and classification. Hybrid deep learning
has also been explored, with CNN-RNN being the most popular.
Description
Keywords
epileptic seizure, EEG, wavelet, statistical parameters, SVM, random forest, deep learning, disorders of consciousness