Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920872 | Computers in Biology and Medicine | 2016 | 7 Pages |
Abstract
Epilepsy is a brain disorder that affects about 1% of the population in the world. Seizure detection is an important component in both the diagnosis of epilepsy and seizure control. In this work a patient non-specific strategy for seizure detection based on Stationary Wavelet Transform of EEG signals is developed. A new set of features is proposed based on an average process. The seizure detection consisted in finding the EEG segments with seizures and their onset and offset points. The proposed offline method was tested in scalp EEG records of 24-48Â h of duration of 18 epileptic patients. The method reached mean values of specificity of 99.9%, sensitivity of 87.5% and a false positive rate per hour of 0.9.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Lorena Orosco, Agustina Garcés Correa, Pablo Diez, Eric Laciar,