Name
Abstract | ||
|---|---|---|
The performance of seizure prediction is usually affected by various kinds of artifacts, especially by physiological artifacts. To improve the performance of seizure prediction, this paper proposed an automatic artifact reduction method based on multivariate empirical mode decomposition and independent component analysis (MEMD-ICA). The proposed method could identify electrooculography (EOG) and electromyographic (EMG) artifacts precisely while keeping the useful neural signals as much as possible. The performance of seizure prediction has been significantly improved with an accuracy of 90.59% and a sensitivity of 91.09% based on CHB-MIT database. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIOCAS.2018.8584675 | 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Keywords | Field | DocType |
artifact reduction,seizure prediction,phase synchronization,independent component analysis (ICA),multivariate empirical mode decomposition (MEMD) | Computer vision,Synchronization,Pattern recognition,Computer science,Multivariate empirical mode decomposition,Electrooculography,Independent component analysis,Artificial intelligence,Electroencephalography | Conference |
ISSN | ISBN | Citations |
2163-4025 | 978-1-5386-3604-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihan Tang | 1 | 0 | 1.69 |
| Menglian Zhao | 2 | 22 | 11.35 |
| Yizhao Zhou | 3 | 0 | 0.68 |
| Xiaobo Wu | 4 | 5 | 6.74 |