Probing epileptic disorders with lightweight neural network and EEG's intrinsic geometry

The nonlinear dynamical systems can be stabilized on attractors in chaotic states, where the attractors depicted by dynamical trajectories may take on specific geometries. Electroencephalogram (EEG) signals are typically chaotic signals that have various nonlinear dynamic characteristics. Intrinsic...

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Veröffentlicht in:	Nonlinear dynamics Jg. 111; H. 6; S. 5817 - 5832
Hauptverfasser:	Song, Zhenxi, Deng, Bin, Zhu, Yulin, Cai, Lihui, Wang, Jiang, Yi, Guosheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.03.2023 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Attractors (mathematics) Automotive Engineering Classical Mechanics Control Convulsions & seizures Deep learning Disorders Dynamic characteristics Dynamical Systems Electroencephalography Engineering Epilepsy Geometry Machine learning Mechanical Engineering Neural networks Nonlinear dynamics Nonlinear systems Original Paper Segments Seizures Vibration EEG geometry Epileptic disorders Electroencephalogram (EEG) Seizure detection Chaotic system Fully convolutional neural network
ISSN:	0924-090X, 1573-269X
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Abstract	The nonlinear dynamical systems can be stabilized on attractors in chaotic states, where the attractors depicted by dynamical trajectories may take on specific geometries. Electroencephalogram (EEG) signals are typically chaotic signals that have various nonlinear dynamic characteristics. Intrinsic geometry of EEG signals could contribute to tracking the recurrence of seizures and probing epileptic disorders, but it is ignored in most deep network-based seizure detection algorithms. Therefore, this paper presents an automatic detection framework called Recursive State-Space Neural Network (RSSNN) to infer the EEG geometry from single-channel signals and identify different epileptic patterns with a fast computational speed. RSSNN consists of a mathematical mapping module and a deep learning model. The former reconstructs EEG geometry in a high-dimensional state-space and maps it to a two-dimensional graph. The latter is a newly designed lightweight (0.68 MB) fully convolutional network that decodes geometry into brain states. We validated RSSNN on a public EEG dataset collected from epileptic patients with seizure and seizure-free conditions and healthy volunteers. A sliding window with a one-second length is utilized to verify the performance of RSSNN at the segment level. Moreover, the voting strategy is adopted to obtain the final prediction at the subject level. In the testing phase, RSSNN obtains an overall 99.79% accuracy at the EEG segment level and reaches 100% accuracy at the subject level. Notably, it takes less than 25 ms to predict one subject. This study proves the potential of EEG's intrinsic geometry as a seizure indicator for real-time monitoring by combining it with a lightweight neural network. It enriches the deep learning-based seizure prediction methodology in nonlinear dynamics.
AbstractList	The nonlinear dynamical systems can be stabilized on attractors in chaotic states, where the attractors depicted by dynamical trajectories may take on specific geometries. Electroencephalogram (EEG) signals are typically chaotic signals that have various nonlinear dynamic characteristics. Intrinsic geometry of EEG signals could contribute to tracking the recurrence of seizures and probing epileptic disorders, but it is ignored in most deep network-based seizure detection algorithms. Therefore, this paper presents an automatic detection framework called Recursive State-Space Neural Network (RSSNN) to infer the EEG geometry from single-channel signals and identify different epileptic patterns with a fast computational speed. RSSNN consists of a mathematical mapping module and a deep learning model. The former reconstructs EEG geometry in a high-dimensional state-space and maps it to a two-dimensional graph. The latter is a newly designed lightweight (0.68 MB) fully convolutional network that decodes geometry into brain states. We validated RSSNN on a public EEG dataset collected from epileptic patients with seizure and seizure-free conditions and healthy volunteers. A sliding window with a one-second length is utilized to verify the performance of RSSNN at the segment level. Moreover, the voting strategy is adopted to obtain the final prediction at the subject level. In the testing phase, RSSNN obtains an overall 99.79% accuracy at the EEG segment level and reaches 100% accuracy at the subject level. Notably, it takes less than 25 ms to predict one subject. This study proves the potential of EEG's intrinsic geometry as a seizure indicator for real-time monitoring by combining it with a lightweight neural network. It enriches the deep learning-based seizure prediction methodology in nonlinear dynamics. The nonlinear dynamical systems can be stabilized on attractors in chaotic states, where the attractors depicted by dynamical trajectories may take on specific geometries. Electroencephalogram (EEG) signals are typically chaotic signals that have various nonlinear dynamic characteristics. Intrinsic geometry of EEG signals could contribute to tracking the recurrence of seizures and probing epileptic disorders, but it is ignored in most deep network-based seizure detection algorithms. Therefore, this paper presents an automatic detection framework called Recursive State-Space Neural Network (RSSNN) to infer the EEG geometry from single-channel signals and identify different epileptic patterns with a fast computational speed. RSSNN consists of a mathematical mapping module and a deep learning model. The former reconstructs EEG geometry in a high-dimensional state-space and maps it to a two-dimensional graph. The latter is a newly designed lightweight (0.68 MB) fully convolutional network that decodes geometry into brain states. We validated RSSNN on a public EEG dataset collected from epileptic patients with seizure and seizure-free conditions and healthy volunteers. A sliding window with a one-second length is utilized to verify the performance of RSSNN at the segment level. Moreover, the voting strategy is adopted to obtain the final prediction at the subject level. In the testing phase, RSSNN obtains an overall 99.79% accuracy at the EEG segment level and reaches 100% accuracy at the subject level. Notably, it takes less than 25 ms to predict one subject. This study proves the potential of EEG's intrinsic geometry as a seizure indicator for real-time monitoring by combining it with a lightweight neural network. It enriches the deep learning-based seizure prediction methodology in nonlinear dynamics.
Author	Song, Zhenxi Zhu, Yulin Deng, Bin Wang, Jiang Yi, Guosheng Cai, Lihui
Author_xml	– sequence: 1 givenname: Zhenxi surname: Song fullname: Song, Zhenxi organization: School of Electrical and Information Engineering, Tianjin University – sequence: 2 givenname: Bin surname: Deng fullname: Deng, Bin organization: School of Electrical and Information Engineering, Tianjin University – sequence: 3 givenname: Yulin surname: Zhu fullname: Zhu, Yulin organization: School of Electrical and Information Engineering, Tianjin University – sequence: 4 givenname: Lihui surname: Cai fullname: Cai, Lihui organization: School of Electrical and Information Engineering, Tianjin University – sequence: 5 givenname: Jiang surname: Wang fullname: Wang, Jiang organization: School of Electrical and Information Engineering, Tianjin University – sequence: 6 givenname: Guosheng orcidid: 0000-0002-9362-6605 surname: Yi fullname: Yi, Guosheng email: guoshengyi@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University
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Keywords	EEG geometry Epileptic disorders Electroencephalogram (EEG) Seizure detection Chaotic system Fully convolutional neural network
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SubjectTerms	Accuracy Algorithms Attractors (mathematics) Automotive Engineering Classical Mechanics Control Convulsions & seizures Deep learning Disorders Dynamic characteristics Dynamical Systems Electroencephalography Engineering Epilepsy Geometry Machine learning Mechanical Engineering Neural networks Nonlinear dynamics Nonlinear systems Original Paper Segments Seizures Vibration
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Title	Probing epileptic disorders with lightweight neural network and EEG's intrinsic geometry
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