Published June 5, 2018
| Version v1
Dataset
Open
Trained SVMs
- 1. Aalto University
- 2. University of Helsinki/QIMR Berghofer Medical Research Institute
Contributors
Researcher:
- 1. University of Helsinki/QIMR Berghofer Medical Research Institute
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)
Files
Files
(24.6 GB)
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md5:4ca91c59251957cfec683d62a0a01215
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74.6 MB | Download |
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md5:b5d7042849d401dbc137b05e4f20502c
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32.4 MB | Download |
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md5:3dcdfba33f5537ebcc41decbcea94ebd
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192.4 MB | Download |
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md5:1c3534103cb997967f29366ca899ea0a
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6.0 GB | Download |
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md5:09cdb74358b343d166e02e8652c3242e
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2.6 GB | Download |
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md5:3575a85601338fb46567ed9fd83bf40e
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15.8 GB | Download |