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ZENODO
Software . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2025
License: CC BY
Data sources: Datacite
ZENODO
Software . 2025
License: CC BY
Data sources: Datacite
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A 3D-SERESNet Framework for Patient-Specific and Multi-Patient Generalization

Authors: Zhou;

A 3D-SERESNet Framework for Patient-Specific and Multi-Patient Generalization

Abstract

3D-SERESNet 3D-SERESNet is a epileptic seizure prediction model. Implementation of paper - [Robust Epileptic Seizure Prediction: A 3D-SERESNet Framework for Patient-Specific and Multi-Patient Generalization] Requirements numpy pyedflib scipy torch datas CHB-MIT Dataset: Official download link: https://physionet.org/content/chbmit/1.0.0/ Train python train.py --i input_Data_dir --0 outputdir Test python predict.py --model model/weights/best.pt --i inputdir --0 outputdir

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average