Research on epilepsy detection and recognition based on the combination of time frequency transform and deep learning model
Canhui Wang, Yan Li, Haoran Tang, Tianqi Xu, Zongfang Ren, Ibrahim Sadek, Ibrahim Sadek, Ibrahim Sadek, Ibrahim Sadek
TL;DR
This paper improves epilepsy detection in EEG signals by combining wavelet transforms with optimized deep learning models.
Contribution
The novel contribution is optimizing CWT with tailored neural networks and training techniques for better epilepsy detection.
Findings
CWT-based feature extraction outperforms STFT in EEG signal analysis.
CWT combined with Shallow ConvNet achieves the best overall performance.
CWT+EEGNet shows excellent precision after integrating attention modules and dynamic adaptations.
Abstract
To improve the detection performance of epileptic electroencephalogram (EEG) signals and address their non-stationary characteristics,this paper compares the combined effects of continuous wavelet transform (CWT) and short-time Fourier transform (STFT) with three neural network models—EEGNet,AlexNet,and Shallow ConvNet—and incorporates targeted optimization designs. Specifically,Focal Loss,dynamic data augmentation,and an early stopping mechanism are introduced in the training phase to enhance model robustness. For EEGNet,optimizations are implemented by integrating a Squeeze-and-Excitation (SE) attention module,improving depthwise separable convolution,and dynamically adapting dimensions to reduce classification errors. For Shallow ConvNet,improvements include layered convolution for extracting “time-frequency” features and average pooling to adapt to long-duration data blocks.…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEEG and Brain-Computer Interfaces · Epilepsy research and treatment · Functional Brain Connectivity Studies