Double Discrete Variational Autoencoder for Epileptic EEG Signals Classification DOI Creative Commons

Shufeng Liang,

Xin Zhang, Hulin Zhao

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 106567 - 106578

Published: Jan. 1, 2024

Electroencephalography (EEG) plays a key role in the clinical evaluation of epilepsy and provides strong support for treatment decisions. However, analyzing decoding EEG recordings is burdensome task neurologists experts. Existing automated detection techniques make considerable efforts feature engineering, but often fall short when it comes to representing complex patterns signals. Deep learning methods allow higher-order representations intricate pattern learning, experiencing explosive success performance diagnostics. In this paper, we propose novel Double Discrete Variational AutoEncoder (D2-VAE) network learn latent signals perform deep discretization. Specifically, method builds learnable codebook based on Vector Quantized (VQ-VAE) obtain generic representation signal. The discretization local signal obtained by queries, whereas global information characterized building histogram quantization patterns. Such local-global portrayal more attuned single-mode repetition multi-mode mixing that characterizes epileptic Multiple diagnostic tasks multiple metrics are used validate effectiveness proposed classification experimental results show D2-VAE possesses low-dimensional yet powerful quantitative representation, with significant improvement over existing methods.

Language: Английский

Optimal Sub-Segment Extraction of Finger Motor Imagery Electroencephalogram Signal DOI

Qingwei Ye,

Peng Zhou

Published: Jan. 1, 2025

Language: Английский

Citations

0
Double Discrete Variational Autoencoder for Epileptic EEG Signals Classification DOI Creative Commons

Shufeng Liang,

Xin Zhang, Hulin Zhao

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 106567 - 106578

Published: Jan. 1, 2024

Electroencephalography (EEG) plays a key role in the clinical evaluation of epilepsy and provides strong support for treatment decisions. However, analyzing decoding EEG recordings is burdensome task neurologists experts. Existing automated detection techniques make considerable efforts feature engineering, but often fall short when it comes to representing complex patterns signals. Deep learning methods allow higher-order representations intricate pattern learning, experiencing explosive success performance diagnostics. In this paper, we propose novel Double Discrete Variational AutoEncoder (D2-VAE) network learn latent signals perform deep discretization. Specifically, method builds learnable codebook based on Vector Quantized (VQ-VAE) obtain generic representation signal. The discretization local signal obtained by queries, whereas global information characterized building histogram quantization patterns. Such local-global portrayal more attuned single-mode repetition multi-mode mixing that characterizes epileptic Multiple diagnostic tasks multiple metrics are used validate effectiveness proposed classification experimental results show D2-VAE possesses low-dimensional yet powerful quantitative representation, with significant improvement over existing methods.

Language: Английский

Citations

0