A review on the pattern detection methods for epilepsy seizure detection from EEG signals

Sharmila, A.; Geethanjali, P.

doi:10.1515/bmt-2017-0233

Cited by 35 publications

(21 citation statements)

References 127 publications

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“…Certainly, in the past, numerous reviews have been carried out on seizure detection along with applied features, classifiers, and claimed accuracy [27,[30][31][32][33] without focusing on the challenges faced by the data scientists whilst doing research on datasets of neurological disorders. Therefore, this article provides a detailed study of machine learning applications on epileptic seizure detection and other related knowledge discovery tasks.…”

Section: Role Of Data Scientists In Epileptic Seizure Detectionmentioning

confidence: 99%

A review of epileptic seizure detection using machine learning classifiers

et al. 2020

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Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

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Section: Role Of Data Scientists In Epileptic Seizure Detectionmentioning

confidence: 99%

A review of epileptic seizure detection using machine learning classifiers

et al. 2020

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“…Sharmila and Geethanjali [ 63 ] presented a rigorous review of all the methods employed for seizure prediction.…”

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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“…ESP can be detected through the electrodes placed over the scalp in EEGs [57]- [59] Similarly, EEG is well-known for the classification of brain activities such awake, sleeping, though processing. Similarly, abnormal brain function can be detected by means of electrical recordings [58], [60], and neuroimaging [61].…”

Section: Biomarkersmentioning

confidence: 99%

Intelligent Deep Brain Stimulation Systems: A General Review

Khan

Sikander

et al. 2021

IEEE Access

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Brain Stimulation is now becoming the preferred approach where pharmacological treatment is ineffective in neurological conditions such as Parkinson's Disease (PD), Tourette Syndrome, Addiction disorder, Depression, and Anxiety. The brain can be stimulated by electrical current, light, and sound energy, invasively or non-invasively. The article comprises of pathophysiology in Parkinson's, current pharmacological modalities used for the treatment (along with limitations), and deep brain stimulation as the last resort. The article also provides a comprehensive analysis of intelligent Deep Brain Stimulation methods (procedure, architecture, and type) from the studies and research conducted in the last five years. Finally, research gaps and associated challenges are discussed.

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A review on the pattern detection methods for epilepsy seizure detection from EEG signals

Cited by 35 publications

References 127 publications

A review of epileptic seizure detection using machine learning classifiers

A review of epileptic seizure detection using machine learning classifiers

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

Intelligent Deep Brain Stimulation Systems: A General Review

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