Research output per year
Research output per year
Description
These SVMs are needed to run 3 out of 4 implementations of neonatal seizure detection algorithms (https://github.com/ktapani/Neonatal_Seizure_Detection).
The fullSVM_XXXs are trained on all data in https://zenodo.org/record/1280684#.Wxh3QkiFNaQ, and can be used to detect seizures in new EEG data.
The svmXXXs are trained on cross validated data and can be used to reproduce results presented in:
K. Tapani, S. Vanhatalo and N. Stevenson, Time-varying EEG correlations improve automated neonatal seizure detection, International Journal of Neural Systems. (accepted for publication)
The fullSVM_XXXs are trained on all data in https://zenodo.org/record/1280684#.Wxh3QkiFNaQ, and can be used to detect seizures in new EEG data.
The svmXXXs are trained on cross validated data and can be used to reproduce results presented in:
K. Tapani, S. Vanhatalo and N. Stevenson, Time-varying EEG correlations improve automated neonatal seizure detection, International Journal of Neural Systems. (accepted for publication)
| Date made available | 5 Jun 2018 |
|---|---|
| Publisher | Zenodo |
Dataset Licences
- CC-BY-4.0
Research output
- 1 Article
-
Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection
Tapani, K. T., Vanhatalo, S. & Stevenson, N. J., 1 May 2019, In: International Journal of Neural Systems. 29, 4, 15 p., 1850030.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile68 Citations (Scopus)116 Downloads (Pure)