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...
Những tác giả chính: | Shaoqiang Wang, Shudong Wang, Song Zhang, Yifan Wang |
---|---|
Định dạng: | Bài viết |
Ngôn ngữ: | English |
Được phát hành: |
SpringerOpen
2020-10-01
|
Loạt: | EURASIP Journal on Wireless Communications and Networking |
Những chủ đề: | |
Truy cập trực tuyến: | http://link.springer.com/article/10.1186/s13638-020-01810-5 |
Những quyển sách tương tự
-
SC-ResNeXt: A Regression Prediction Model for Nitrogen Content in Sugarcane Leaves
Bằng: Zihao Lu, et al.
Được phát hành: (2025-01-01) -
Art appreciation model design based on improved PageRank and ECA-ResNeXt50 algorithm
Bằng: Hang Yang, et al.
Được phát hành: (2023-12-01) -
A Method for Speaker Recognition Based on the ResNeXt Network Under Challenging Acoustic Conditions
Bằng: Dongbo Liu, et al.
Được phát hành: (2023-01-01) -
G2-ResNeXt: A Novel Model for ECG Signal Classification
Bằng: Shengnan Hao, et al.
Được phát hành: (2023-01-01) -
Research on the Classification of Sun-Dried Wild Ginseng Based on an Improved ResNeXt50 Model
Bằng: Dongming Li, et al.
Được phát hành: (2024-11-01)