An EEG-Based Seizure Recognition Method Using Dynamic Routing

The diagnosis and treatment of brain diseases represent the forefront of brain science research, with EEG-related research occupying a uniquely significant position. In recent years, deep learning technology has been widely applied to the study of EEG signals, yet the integration of information from...

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Veröffentlicht in:IEEE access Jg. 12; S. 74054 - 74068
Hauptverfasser: Xiong, Zhiwen, Liu, Yang, Jiang, Peng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Brain
brain connectivity
Brain modeling
capsule neural network
Channels
Convulsions & seizures
dynamic routing algorithm
Electroencephalography
Epilepsy
Feature extraction
Heuristic algorithms
Machine learning
Recognition
Routing
seizure detection
Seizures
Vectors
ISSN:2169-3536, 2169-3536
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Zusammenfassung:The diagnosis and treatment of brain diseases represent the forefront of brain science research, with EEG-related research occupying a uniquely significant position. In recent years, deep learning technology has been widely applied to the study of EEG signals, yet the integration of information from multiple EEG channels remains a challenging task. Based on the dynamic routing algorithm, this study established a deep neural network. Subsequently, leveraging this network, an epileptic seizure recognition method, VarChanNet, was proposed. Seizure recognition experiments were conducted using the Bonn and CHB-MIT databases. The experimental results demonstrate that the proposed VarChanNet method maintains high recognition accuracy even when the number of channels involved in the recognition process changes. It reliably functions on both the Bonn and CHB-MIT databases, indicating its potential for generalization. Furthermore, the method provides recommendations for channel selection during the recognition process. For instance, in the case of CHB-MIT, Channel 21 can be selected for single-channel recognition, Channels 2 and 3 for dual-channel, and Channels 1, 2, and 3 for triple-channel epileptic seizure recognition. In a word, the proposed VarChanNet method enables the fusion of information from different EEG channels, supporting recognition tasks even when the number of channels varies. It offers a new perspective for EEG analysis and holds the potential for generalization.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3402075