Nonlinear Dynamical Systems with Chaos and Big Data: A Case Study of Epileptic Seizure Prediction and Control

Shafique, Ashfaque Bin; Sayeed, Mohamed; Tsakalis, Konstantinos

doi:10.1007/978-3-319-53817-4_13

Cited by 10 publications

(6 citation statements)

References 50 publications

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“…Among the most popular layout conventions is the 10-20 International System [29]. Other electrode layouts exist for humans, primates, and other laboratory animals, including rats [30]. The acquisition system utilized an OpenBCI Ganglion board, which permitted the recording of up to four EEG channels at 200 samples per second.…”

Section: B Eeg Measurement Setupmentioning

confidence: 99%

Electroencephalographic Response of Brain Stimulation by Shock Waves From Laser Generated Carbon Nanotube Transducer

Lee

LaRocco

Paeng

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: B Eeg Measurement Setupmentioning

confidence: 99%

Electroencephalographic Response of Brain Stimulation by Shock Waves From Laser Generated Carbon Nanotube Transducer

Lee

LaRocco

Paeng

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…First,researchers try to nd some mathematical measures, which can be used as the thresh old for alarm [19].For example, aarabi, a and he, B of the University of Minnesota [20] set multi-scale reference rules to trigger warning marks when probability statistics reach the threshold range. Another way is that we can regard it as a classi cation problem between preictal and interictal categories [21].…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure prediction from multivariate sequential signals using multidimensional convolution network

Wei

Cao

Zhang

et al. 2022

Preprint

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Background: The ability to predict coming seizures will improve the quality of life of patients with epilepsy. Analysis of brain electrical activity using multivariate sequential signals can be used to predict seizures. Method:Seizure prediction can be regarded as a classification problem between interictal and preictal EEG signals. In this work, hospital multivariate sequential EEG signals were transformed into multidimensional input,multidimensional convolutional neural network models were constructed to predict seizures. several channels segments were extracted from the interictal and preictal time duration and fed them to the proposed deep learning models. Result:The average accuracy of multidimensional deep network model for multi-channel EEG data is about 94%, the average sensitivity is 88.47%, and the average specificity is 89.75%. Conclusion:This study combines the advantages of multivariate sequential signals and multidimensional convolution network for EEG data analysis to predict epileptic seizures, thereby enabling early warning before epileptic seizures in clinical applications.

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“…In the modeling stage, The solution of epilepsy prediction can be described in two ways [18]. First,researchers try to find some mathematical measures, which can be used as the thresh old for alarm [19].For example, aarabi, a and he, B of the University of Minnesota [20] set multi-scale reference rules to trigger warning marks when probability statistics reach the threshold range. Another way is that we can regard it as a classification problem between preictal and interictal categories [21].…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure detection from multivariate sequential signals using Multidimensional convolution network

Wei

Zhang

Zhou

2022

Preprint

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Background The ability to predict coming seizures will improve the quality of life of patients with epilepsy. Analysis of brain electrical activity using multivariate sequential signals can be used to predict seizures. Method Seizure prediction can be regarded as a classification problem between interictal and preictal EEG signals. In this work, hospital multivariate sequential EEG signals were transformed into multidimensional input,multidimensional convolutional neural network models were constructed to predict seizures. several channels segments were extracted from the interictal and preictal time duration and fed them to the proposed deep learning models. Result The average accuracy of multidimensional deep network model for multi-channel EEG data is about 94%, the average sensitivity is 88.47%, and the average specificity is 89.75%. Conclusion This study combines the advantages of multivariate sequential signals and multidimensional convolution network for EEG data analysis to predict epileptic seizures, thereby enabling early warning before epileptic seizures in clinical applications.

show abstract

Nonlinear Dynamical Systems with Chaos and Big Data: A Case Study of Epileptic Seizure Prediction and Control

Cited by 10 publications

References 50 publications

Electroencephalographic Response of Brain Stimulation by Shock Waves From Laser Generated Carbon Nanotube Transducer

Electroencephalographic Response of Brain Stimulation by Shock Waves From Laser Generated Carbon Nanotube Transducer

Epileptic seizure prediction from multivariate sequential signals using multidimensional convolution network

Epileptic seizure detection from multivariate sequential signals using Multidimensional convolution network

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