Epileptic Seizure Prediction using EEG Images

George, Felix; Joseph, Alex; Baby, Bibin; John, Alex St.; John, T. Nimmy; Deepak, M.; R, Nithin G; Sathidevi, P. S.

doi:10.1109/iccsp48568.2020.9182327

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…George et al [ 62 ] proposed an automatic seizure detection system that classify the EEG data into subsequently ictal, nonictal, and preictal groups using ResNet-50, a subclass of convolutional neural networks (CNN), by changing one-dimensional EEG data into two-dimensional EEG images. By this original method, the present model calculates an impending seizure with a correctness of 94.98%.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Natu

Bachute

Gite

et al. 2022

Computational and Mathematical Methods in Medicine

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Natu

Bachute

Gite

et al. 2022

Computational and Mathematical Methods in Medicine

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show abstract

“…Multiple EEG datasets, namely Freiburg (Ihle et al, 2012), CHB-MIT (Shoeb, 2009b), Kaggle (George et al, 2020), Bonn (Andrzejak et al, 2001), Flint-Hills (Assi et al, 2017), Bern-Barcelona (Andrzejak et al, 2012), Hauz Khas (Assi et al, 2017), and Zenodo (Stevenson et al, 2019) are the main ones for developing automatic systems for epileptic seizure detection. The signals forming each datapoint of these datasets are recorded either intracranial or from the scalp of humans or animals.…”

Section: Epileptic Seizures Datasetsmentioning

confidence: 99%

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Shoeibi,

Moridian,

Khodatars

et al. 2021

Preprint

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Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. Neuroimaging modalities assist specialist physicians considerably in analyzing brain tissue and the changes made in it. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional

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“…Accuracy is the ratio of the number of correctly classified samples by the classifier to the total number of samples. Calculated as shown in Eq (10).…”

Section: Performance Evaluation Indicatorsmentioning

confidence: 99%

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Epileptic Seizure Prediction using EEG Images

Cited by 13 publications

References 7 publications

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

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

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

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