Multi-scale Permutation Entropy and Its Applications in the Identification of Seizures_Journal of Biomedical Engineering

Authors：

 XIMin ¹ , ZHUGuohun ^2,3

1. College of Compute Science and Technology, Guilin University of Electronic Technology, Guilin 541004, China;
2. College of Automation and Control, Guilin University of Automation and Control, Guilin 541004, China;
3. Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia;

Corresponding author：

XIMin, Email: ximin0608@163.com

Keywords：

seizure; electroencephalogram; multi-scale permutation entropy; support vector machine

DOI：

10.7507/1001-5515.20150137

Video：

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The electroencephalogram (EEG) has proved to be a valuable tool in the study of comprehensive conditions whose effects are manifest in the electrical brain activity, and epilepsy is one of such conditions. In the study, multi-scale permutation entropy (MPE) was proposed to describe dynamical characteristics of EEG recordings from epilepsy and healthy subjects, then all the characteristic parameters were forwarded into a support vector machine (SVM) for classification. The classification accuracies of the MPE with SVM were evaluated by a series of experiments. It is indicated that the dynamical characteristics of EEG data with MPE could identify the differences among healthy, inter-ictal and ictal states, and there was a reduction of MPE of EEG from the healthy and inter-ictal state to the ictal state. Experimental results demonstrated that average classification accuracy was 100% by using the MPE as a feature to characterize the healthy and seizure, while 99.58% accuracy was obtained to distinguish the seizure-free and seizure EEG. In addition, the single-scale permutation entropy (PE) at scales 1-5 was put into the SVM for classification at the same time for comparative analysis. The simulation results demonstrated that the proposed method could be a very powerful algorithm for seizure prediction and could have much better performance than the methods based on single scale PE.

Citation： XIMin, ZHUGuohun. Multi-scale Permutation Entropy and Its Applications in the Identification of Seizures. Journal of Biomedical Engineering, 2015, 32(4): 751-756. doi: 10.7507/1001-5515.20150137 Copy

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2.	PRADHAN N,SADASIVAN P K,ARUNODAYA G R.Detection of seizure activity in EEG by an artificial neural network:a preliminary study[J].Comput Biomed Res,1996,29(4):303-313.
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5.	SUBASI A.Epileptic seizure detection using dynamic wavelet network[J].Expert Syst Appl,2005,29(2):343-355.
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7.	林相波,邱天爽,李小兵,等.基于小波变换并结合神经网络的癫痫发作预报[J].中国生物医学工程学报,2005,24(5):535-540.
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22.	BANDT C,POMPE B.Permutation entropy:A natural complexity measure for time series[J].Phys Rev Lett,2002,88(17):1-4.
23.	LI X L,OUYANG G,RICHARDS D A.Predictability analysis of absence seizures with permutation entropy[J].Epilepsy Res,2007,77(1):70-74.
24.	OLOFSEN E,SLEIGH J W,DAHAN A.Using permutation entropy to measure the electroencephalographic effects of sevoflurane[J].Anesthesiology,2008,109(1):448-456.
25.	ZHU G,LI Y,WEN P,et al.Epileptogenic focus detection in intracranial EEG based on delay permutation entropy[J].AIP Conf Proc,2013,1559(1):31-36.
26.	ZUNINO L,ZANIN M,TABAK B M,et al.Forbidden patterns,permutation entropy and stock market inefficiency[J].Physica A:Statistical Mechanics and its Applications,2009,388(14):2854-2864.
27.	AZIZ W,ARIF M.Multiscale permutation entropy of physiological time series[C]//IEEE INMIC 20059th International Multitopic Conference.Karachi:2005:1-6.
28.	ANDRZEJAK R G,LEHNERTZ K,MORMANN F,et al.Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity:Dependence on recording region and brain state[J].Physical Review E,2001,64(6):1-8.
29.	COSTA M,GOLDBERGER A L,PENG C K.Multiscale entropy analysis of complex physiologic time series[J].Phys Rev Lett,2002,89(6):068102.
30.	PARK J H,KIM S,KIM C H,et al.Multi-scale entropy analysis of EEG from patients under different pathological conditions[J].Fractals,2007,15(4):399-404.
31.	CORTES C,VAPNIK V.Support-Vector networks,machine learning,20[Z].1995.
32.	HUANG T M,KECMAN V,KOPRIVA I.Kernel based algorithms for mining huge data Sets,supervised,semi-supervised,and unsupervised learning[M].Berlin:Springer-Verlag,2006.
33.	CHANG C C,LIN C J.LIBSVM:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
34.	ZHU G,LI Y,WEN P P.Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm[J].Comput Methods Programs Biomed,2014,115(2):64-75.

1. MORMANN F,ANDRZEJAK R G,ELGER C E,et al.Seizure prediction:the long and winding road[J].Brain,2007,130(Pt 2):314-333.
2. PRADHAN N,SADASIVAN P K,ARUNODAYA G R.Detection of seizure activity in EEG by an artificial neural network:a preliminary study[J].Comput Biomed Res,1996,29(4):303-313.
3. KIYMIK V P,SUBASI S,OZCALIK H R.Neural networks with period gram and autoregressive spectral analysis methods in detection of epileptic seizures[J].J Med Syst,2004,28(6):511-522.
4. SRINIVASAN V,ESWARAN C,SRIRAAM N.Approximate entropy-based epileptic EEG detection using artificial neural networks[J].IEEE Trans Inf Technol Biomed,2007,11(3):288-295.
5. SUBASI A.Epileptic seizure detection using dynamic wavelet network[J].Expert Syst Appl,2005,29(2):343-355.
6. 陈颖萍,林家瑞.基于小波变换和独立分量分析相结合的癫痫脑电分析[J].生物医学工程研究,2006,25(1):9-13.
7. 林相波,邱天爽,李小兵,等.基于小波变换并结合神经网络的癫痫发作预报[J].中国生物医学工程学报,2005,24(5):535-540.
8. ZHU T Q,HUANG L Y,TIAN X Z.Epileptic seizure prediction by using empirical mode decomposition and complexity analysis of single-channel scalp electroencephalogram[C]//Proceedings of The 20092nd International Conference on Biomedical Engineering and Informatics.Tianjin:2009:606-609.
9. 白冬梅,邱天爽,鲍海平.基于经验模式分解与样本熵的癫痫预测方法[J].中国生物医学工程学报,2006,25(5):527-531.
10. ZHU G H,YAN L,PENG W.Analysing epileptic EEGs with a visibility graph algorithm[C]//20125th International Conference on Biomedical Engineering and Informatics (BMEI).Chongqing:2012:432-436.
11. 欧阳高翔,崔素媛,李小俚.基于脑电非线性动力学的癫痫发作分析[C]//中国生物医学工程进展——2007中国生物医学工程联合学术年会论文集:下册.2007.
12. WILSON K,SCHEUER M L,PLUMMER C,et al.Seizure detection:correlation of human experts[J].Clinl Neurophysiol,2003,114(11):2156-2164.
13. MURRO A M,KING D W,SMITH J R,et al.Computerized seizure detection of complex partial seizures[J].Electroencephalogr Clin Neurophysiol,1991,79(4):330-333.
14. QU H,GOTMAN J.A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring:Possible use as a warning device[J].IEEE Trans Biomed Eng,1997,44(2):115-122.
15. GABOR A J,LEACH R R,DOWLA F U.Automated seizure detection using a self-organizing neural network[J].Electroencephalogr Clin Neurophysiol,1996,99(3):257-266.
16. WEBBER W S,LESSER R P,RICHARDSON R T,et al.An approach to seizure detection using an artificial neural network[J].Electroencephalogr Clin Neurophysiol,1996,98(4):250-272.
17. 蔡冬梅,周卫东,刘凯,等.基于Hurst指数和SVM的癫痫脑电检测方法[J].中国生物医学工程学报,2010,29(6):836-840.
18. NICOLAOU N,GEORGIOU J.Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines[J].Expert Syst Appl,2012,39(1):202-209.
19. XU M G,XIA P,JIANG Y,et al.Changes in electroencephalogram in rat epilepsy model via nonlinear dynamical approach[J].中国临床康复,2005,9(21):216-218.
20. OUYANG G,LI J,LIU X Z,et al.Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis[J].Epilepsy Res,2013,104(3):246-252.
21. YUAN Q,ZHOU W,LI S,et al.Epileptic EEG classification based on extreme learning machine and nonlinear features[J].Epilepsy Res,2011,96(1-2):29-38.
22. BANDT C,POMPE B.Permutation entropy:A natural complexity measure for time series[J].Phys Rev Lett,2002,88(17):1-4.
23. LI X L,OUYANG G,RICHARDS D A.Predictability analysis of absence seizures with permutation entropy[J].Epilepsy Res,2007,77(1):70-74.
24. OLOFSEN E,SLEIGH J W,DAHAN A.Using permutation entropy to measure the electroencephalographic effects of sevoflurane[J].Anesthesiology,2008,109(1):448-456.
25. ZHU G,LI Y,WEN P,et al.Epileptogenic focus detection in intracranial EEG based on delay permutation entropy[J].AIP Conf Proc,2013,1559(1):31-36.
26. ZUNINO L,ZANIN M,TABAK B M,et al.Forbidden patterns,permutation entropy and stock market inefficiency[J].Physica A:Statistical Mechanics and its Applications,2009,388(14):2854-2864.
27. AZIZ W,ARIF M.Multiscale permutation entropy of physiological time series[C]//IEEE INMIC 20059th International Multitopic Conference.Karachi:2005:1-6.
28. ANDRZEJAK R G,LEHNERTZ K,MORMANN F,et al.Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity:Dependence on recording region and brain state[J].Physical Review E,2001,64(6):1-8.
29. COSTA M,GOLDBERGER A L,PENG C K.Multiscale entropy analysis of complex physiologic time series[J].Phys Rev Lett,2002,89(6):068102.
30. PARK J H,KIM S,KIM C H,et al.Multi-scale entropy analysis of EEG from patients under different pathological conditions[J].Fractals,2007,15(4):399-404.
31. CORTES C,VAPNIK V.Support-Vector networks,machine learning,20[Z].1995.
32. HUANG T M,KECMAN V,KOPRIVA I.Kernel based algorithms for mining huge data Sets,supervised,semi-supervised,and unsupervised learning[M].Berlin:Springer-Verlag,2006.
33. CHANG C C,LIN C J.LIBSVM:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
34. ZHU G,LI Y,WEN P P.Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm[J].Comput Methods Programs Biomed,2014,115(2):64-75.

Journal of Biomedical Engineering

Multi-scale Permutation Entropy and Its Applications in the Identification of Seizures

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