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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Su Yang Weiting Chen Yang Liu Lei Li Zhizhong Wang |
| Copyright Year | 2012 |
| Abstract | Amplitude-integrated electroencephalography (aEEG) has been widely used in continuous monitoring of neonatal brain function. This paper proposes an aEEG recognition method based on revised D-S Theory. The revised D-S Theory improves traditional D-S theory by introducing weight factor into the algorithm. Combining judgments with different weights can attenuate the conflict among them and get a more sound one. The efficiency of the proposed method is validated by classifying 103 aEEG recordings into normal and abnormal groups. Approximate entropy (ApEn) and amplitudes are used as the features to characterize aEEG signals. Compared with the traditional D-S theory, the classification accuracy of the revised method increases by 4.88%. This method could be helpful in monitoring newborn brain function. |
| Starting Page | 575 |
| Ending Page | 578 |
| File Size | 499981 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467348737 |
| DOI | 10.1109/CIT.2012.123 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Pediatrics Uncertainty D-S theory amplitude-integrated EEG Educational institutions Approximation algorithms Electroencephalography Entropy approximate entropy Monitoring |
| Content Type | Text |
| Resource Type | Article |
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