Name
Title | ||
|---|---|---|
Ultra-fast Epileptic seizure detection using EMD based on multichannel electroencephalogram |
Abstract | ||
|---|---|---|
We present a system to detect seizure and spike in Epilepsy Electroencephalogram (EEG) analysis and characterize different epilepsy EEG types. After extracting features from three EEG types, Normal, Seizure and Spike, with Empirical Mode Decomposition (EMD), we do Analysis of variance (ANOVA) to classify conspicuous features and low-resolution features, and build Gaussian distributions of conspicuous features for probability density function (PDF) to do classification. Using EMD, the recognition rate improved from 70% to 90%. With ANOVA, the recognition rate can reach 99%. The linear model accelerates the system from 2 hours to 90 seconds compare to the previous approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BIBE.2013.6701576 | BIBE |
Keywords | Field | DocType |
medical signal detection,probability density function,electroencephalography,anova,gaussian distribution,epilepsy electroencephalogram analysis,spike detection,multichannel electroencephalogram,feature extraction,empirical mode decomposition,ultrafast epileptic seizure detection | Pattern recognition,Linear model,Computer science,Speech recognition,Epilepsy,Gaussian,Epileptic seizure,Artificial intelligence,Probability density function,Electroencephalography,Hilbert–Huang transform,Analysis of variance | Conference |
ISSN | Citations | PageRank |
2471-7819 | 1 | 0.38 |
References | Authors | |
11 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Chen | 1 | 3 | 1.11 |
| Yan-Yu Lam | 2 | 1 | 0.72 |
| Chia-ping Shen | 3 | 43 | 6.07 |
| Hsiao-Ya Sung | 4 | 6 | 1.93 |
| Jeng-Wei Lin | 5 | 35 | 7.52 |
| Ming-Jang Chiu | 6 | 82 | 8.56 |
| Feipei Lai | 7 | 846 | 81.35 |