Automated epileptic seizure detection in EEGs using increment entropy

This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure EEGs from non-seizure ones. The maximum accuracy achieves 97.32%. The maximum sensitivity and the maximum specificity are 95.34% and 99.30%,respectively. The results indicate our approach using the IncrEn and SVMs is an effective tool to detect EEG seizure.

[1]  Shekhar Saxena,et al.  Epilepsy Care in the World: Results of an ILAE/IBE/WHO Global Campaign Against Epilepsy Survey , 2006, Epilepsia.

[2]  Nurettin Acir,et al.  Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks , 2005, IEEE Transactions on Biomedical Engineering.

[3]  K. Lehnertz.  Nonlinear EEG analysis in epilepsy , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  U. Rajendra Acharya,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies , 2022 .

[5]  Hasan Ocak,et al.  Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy , 2009, Expert Syst. Appl..

[6]  Julius Georgiou,et al.  Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines , 2012, Expert Syst. Appl..

[7]  Hamid Reza Mohseni,et al.  Epileptic Seizure Detection Using Neural Fuzzy Networks , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[8]  Jianru Xue,et al.  Increment Entropy as a Measure of Complexity for Time Series , 2015, Entropy.

[9]  G. Ouyang,et al.  Predictability analysis of absence seizures with permutation entropy , 2007, Epilepsy Research.

[10]  Joel E. W. Koh,et al.  A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals , 2015, European Neurology.

[11]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[12]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  V. Srinivasan,et al.  Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks , 2007, IEEE Transactions on Information Technology in Biomedicine.

[14]  J. Crowcroft,et al.  Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine , 2012, Journal of Neuroscience Methods.