PD-GATv2: positive difference second generation graph attention network based on multi-granularity in information systems to classification

Fu, Yu; Liu, Xindi; Yu, Bin

doi:10.1007/s10489-024-05432-y

Appl Intell

2024

DOI: 10.1007/s10489-024-05432-y

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PD-GATv2: positive difference second generation graph attention network based on multi-granularity in information systems to classification

Yu Fu,

Xindi Liu,

Bin Yu

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Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy

Sun,

Xiang,

Dong

et al. 2024

Entropy

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Drug-resistant epilepsy is frequent, persistent, and brings a heavy economic burden to patients and their families. Traditional epilepsy detection methods ignore the causal relationship of seizures and focus on a single time or spatial dimension, and the effect varies greatly in different patients. Therefore, it is necessary to research accurate automatic detection technology of epilepsy in different patients. We propose a causal-spatio-temporal graph attention network (CSTGAT), which uses transfer entropy (TE) to construct a causal graph between multiple channels, combining graph attention network (GAT) and bi-directional long short-term memory (BiLSTM) to capture temporal dynamic correlation and spatial topological structure information. The accuracy, specificity, and sensitivity of the SWEZ dataset were 97.24%, 97.92%, and 98.11%. The accuracy of the private dataset reached 98.55%. The effectiveness of each module was proven through ablation experiments and the impact of different network construction methods was compared. The experimental results indicate that the causal relationship network constructed by TE could accurately capture the information flow of epileptic seizures, and GAT and BiLSTM could capture spatiotemporal dynamic correlations. This model accurately captures causal relationships and spatiotemporal correlations on two datasets, and it overcomes the variability of epileptic seizures in different patients, which may contribute to clinical surgical planning.

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Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy

Sun,

Xiang,

Dong

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

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PD-GATv2: positive difference second generation graph attention network based on multi-granularity in information systems to classification

Cited by 1 publication

References 28 publications

Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy

Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy

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