Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks
Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results...
主要な著者: | Shaoqiang Wang, Shudong Wang, Song Zhang, Yifan Wang |
---|---|
フォーマット: | 論文 |
言語: | English |
出版事項: |
SpringerOpen
2020-10-01
|
シリーズ: | EURASIP Journal on Wireless Communications and Networking |
主題: | |
オンライン・アクセス: | http://link.springer.com/article/10.1186/s13638-020-01810-5 |
類似資料
-
SC-ResNeXt: A Regression Prediction Model for Nitrogen Content in Sugarcane Leaves
著者:: Zihao Lu, 等
出版事項: (2025-01-01) -
Art appreciation model design based on improved PageRank and ECA-ResNeXt50 algorithm
著者:: Hang Yang, 等
出版事項: (2023-12-01) -
A Method for Speaker Recognition Based on the ResNeXt Network Under Challenging Acoustic Conditions
著者:: Dongbo Liu, 等
出版事項: (2023-01-01) -
G2-ResNeXt: A Novel Model for ECG Signal Classification
著者:: Shengnan Hao, 等
出版事項: (2023-01-01) -
Research on the Classification of Sun-Dried Wild Ginseng Based on an Improved ResNeXt50 Model
著者:: Dongming Li, 等
出版事項: (2024-11-01)