A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

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.

Keywords: EEG; SVM; deep learning; disorders of consciousness; epileptic seizure; random forest; statistical parameters; wavelet.

Figure 1

Block diagram of an epileptic seizure detection system.

Figure 2

Example of epileptic seizure signals for ictal and interictal conditions.

Figure 3

Adaptive filtering method.

Figure 4

Discrete wavelet decomposition.

Figure 5

Example of epileptic seizure signal decomposed into various levels.

Figure 6

An example of a separable problem in a 2D space.

Figure 7

The percentage of conventional techniques involved in epilepsy studies.

Figure 8

Comparison of conventional techniques and deep learning models used by researchers from 2014 to 2020.