About
Contact & Profiles
A5001432751
Research Areas
- Gait Recognition and Analysis
- EEG and Brain-Computer Interfaces
- Video Surveillance and Tracking Methods
- Epilepsy research and treatment
- Human Pose and Action Recognition
- ECG Monitoring and Analysis
NetEase (China)
2024
Zhejiang University of Technology
2022
To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) 166 094 non-IED 4-second video-EEG segments. The data is processed by the proposed patient detection with frame difference Simple Keypoints (SKPS) capturing patients' movements. EEG EfficientNetV2. features are fused via multilayer perceptron. We...
10.1016/j.neunet.2024.106319
article
EN
cc-by-nc
Neural Networks
2024-04-14
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