Hierarchal online temporal and spatial EEG seizure detection

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%

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

Mansouri

Ledwidge

Sayood

et al. 2021

Neurotrauma Reports

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“…Each individual's brain activity is different. Therefore, we use an adaptive PBI threshold as opposed to a static threshold [34] to determine whether the activity in the epoch under consideration is abnormal. The adaptive threshold is calculated using the PBI values in three blocks of epochs:…”

Section: Adaptive Thresholdmentioning

confidence: 99%

Online EEG Seizure Detection and Localization

2019

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Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists—a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG signals. This computationally time-efficient and cost-effective automated epileptic seizure detection algorithm has a median latency of 8 s, a median sensitivity of 83%, and a median false alarm rate of 2.9%. Hence, it is capable of being used in portable EEG devices to aid in the process of detecting and monitoring epileptic patients.

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Hierarchal online temporal and spatial EEG seizure detection

Cited by 2 publications

References 16 publications

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

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

Online EEG Seizure Detection and Localization

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