Channel selection for epilepsy seizure prediction method based on machine learning
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages
5162-5165
Date Issued
2012
Author(s)
Abstract
The studies on seizure prediction problem have shown great improvement these years. Machine learning based seizure prediction method shows great performance by doing pattern recognition on high-dimensional bivariate synchronization features. However, the computation loading of the machine learning based method may be too high to meet wearable or implantable devices with the power and area constraints. In this work, channel selection is proposed to reduce the channel number from 22 to less than 6 channels and therefore more than 93.73% of the computation loading is saved through the method. The best result shows successful rate of 60.6% in 3-channel cases of ECoG database and successful rate of 70% in 3-channel cases of EEG database. © 2012 IEEE.
Event(s)
34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Other Subjects
Bivariate; Channel number; Channel selection; High-dimensional; Implantable devices; Learning-based methods; Seizure prediction; Forecasting; Implants (surgical); Learning systems; Pattern recognition; Loading; algorithm; article; artificial intelligence; automated pattern recognition; brain mapping; computer assisted diagnosis; electroencephalography; epilepsy; human; methodology; reproducibility; sensitivity and specificity; Algorithms; Artificial Intelligence; Brain Mapping; Diagnosis, Computer-Assisted; Electroencephalography; Epilepsy; Humans; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity
Type
conference paper