Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches

Abstract

Epileptic seizures occur due to brain abnormalities that can indirectly affect patient’s health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world’s population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to “pops” in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

“…It is partly because of their ensemble nature and multiple logic rules [127]. ey can achieve fairly good classification results as shown in the previous sections [134][135][136][137][138][139][140][141][142]. Decision tree-based models can handle a relatively large number of datasets and are less time-consuming and mostly yield high accuracy, precision, and recall.…”

“…(c) Because a large amount of dataset is required for the proper validation of a machine learning model for epileptic seizure detection and classification, plenty of efforts have been made to combine available EEG datasets for this purpose. However, it is still difficult to combine these datasets because they have different parameters and were acquired under relatively different sampling conditions [142]. (d) Because machine/deep learning models mostly require substantial computational resources for their implementation in practical settings, which are sometimes difficult to access, a piece of good knowledge about how to optimize the models' performance is necessary for realizing a practical epileptic seizure detection and classification system.…”

Brain Gray Matter Alterations in Hepatic Encephalopathy: A Voxel-Based Meta-Analysis of Whole-Brain Studies

“…Machine learning algorithms can analyze records containing a large number of variables and can find complex linear or non-linear relationships between variables ( Gazda et al, 2021 ). Machine learning and deep learning methods have been used to analyze EEG to predict various chronic psychiatric illnesses ( Natu et al, 2022 ; Paul et al, 2022 ). Using artificial intelligence and machine learning to comprehensively analyze MRI and EEG to diagnose HE more accurately may be the direction of future research.…”

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

“…Similarly, Nair et al [ 85 ] concluded that the AI-based approaches have been tremendously contributed to epilepsy detection, prediction, and management for an improved healthcare society. Likewise, Natu et al [ 86 ] presented a wholistic view of AI and ML applications in epilepsy detection. It encompasses, data preprocessing approaches, channel selection methods followed by the classifier or the prediction model.…”