Online EEG Seizure Detection and Localization

Mansouri, Amirsalar; Singh, Sanjay; Sayood, Khalid

doi:10.3390/a12090176

Cited by 19 publications

(19 citation statements)

References 48 publications

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“…These combined metrics were previously used for detecting seizures. 38 , 42 Each feature provides a different view of brain-network interactions.…”

Section: Methodsmentioning

confidence: 99%

“…The distances are calculated and extracted as coherency measurements in different frequency bands using a variety of distance metrics, including previously used metrics for detecting and localizing seizures. 38 The metrics investigated include some that combine measures of connectivity as well as measures of power in the frequency bands (the metrics are described in the Supplementary Materials). All coherencies in the frequency domain are studied over different scalp regions of the brain to capture any significant spatial differences between regions in classifying participants with a concussion and those without concussion.…”

Section: Introductionmentioning

confidence: 99%

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Retracted: A Routine Electroencephalography Monitoring System for Automated Sports-Related Concussion Detection

Mansouri

Ledwidge

Sayood

et al. 2021

Neurotrauma Reports

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Cases of concussions in the United States keep increasing and are now up to 2 million to 3 million incidents per year. Although concussions are recoverable and usually not life-threatening, the degree and rate of recovery may vary depending on age, severity of the injury, and past concussion history. A subsequent concussion before full recovery may lead to more-severe brain damage and poorer outcomes. Electroencephalography (EEG) recordings can identify brain dysfunctionality and abnormalities, such as after a concussion. Routine EEG monitoring can be a convenient method for reducing unreported injuries and preventing long-term damage, especially among groups with a greater risk of experiencing a concussion, such as athletes participating in contact sports. Because of the relative availability of EEG compared to other brain-imaging techniques (e.g., functional magnetic resonance imaging), the use of EEG monitoring is growing for various neurological disorders. In this longitudinal study, EEG was analyzed from 4 football athletes before their athletic season and also within 7 days of concussion. Compared to a control group of 4 additional athletes, a concussion was detected with up to 99.5% accuracy using EEG recordings in the Theta-Alpha band. Classifiers that use data from only a subset of the EEG electrodes providing reliable detection are also proposed. The most effective classifiers used EEG recordings from the Central scalp region in the Beta band and over the Temporal scalp region using the Theta-Alpha band. This proof-of-concept study and preliminary findings suggest that EEG monitoring may be used to identify a sports-related concussion occurrence with a high level of accuracy and thus reduce the chance of unreported concussion.

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“…These combined metrics were previously used for detecting seizures. 38 , 42 Each feature provides a different view of brain-network interactions.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retracted: A Routine Electroencephalography Monitoring System for Automated Sports-Related Concussion Detection

Mansouri

Ledwidge

Sayood

et al. 2021

Neurotrauma Reports

Self Cite

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“…Moreover, they also focused on the reduction of ECoG electrodes. Mansouri et al [135] proposed an algorithm for Seizure localization, which was tested on 10 sec of EEG dataset from Karuniya University. Here, they have taken the small-size dataset, because recording usually takes several hours.…”

Section: Seizure Localizationmentioning

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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“…Studies that are addressing real-time epileptic seizure detection techniques can be found in [145][146][147][148][149]. Hanosh et al [145] used Passive Infrared Sensors (PIR) for realtime epileptic seizure detection during sleep.…”

Section: Epileptic Seizure Detectionmentioning

confidence: 99%