Electrical & Computer Engineering, Department of
ORCID IDs
https://orcid.org/0000-0001-9014-5024
https://orcid.org/0000-0001-8010-5272
Date of this Version
8-23-2019
Citation
2019 by the authors.
Abstract
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.
Comments
Algorithms 2019, 12, 176; doi:10.3390/a12090176 www.mdpi.com/journal/algorithms