Automatic Epileptic Electroencephalogram Detection during Normal, Interictal and Ictal Periods Combining Feature Extraction Based on Sample Entropy and Wavelet Packet Energy with Real AdaBoost Algorithm_Journal of Biomedical Engineering

Authors：

ZHANGJianzhao ,  JIANGWei , BENXianye

School of Information Science and Engineering, Shandong University, Jinan 250100, China;

Corresponding author：

JIANGWei, Email: jwimage@sdu.edu.cn

Keywords：

electroencephalogram; epilepsy; Real AdaBoost algorithm; sample entropy; error-correcting output codes

DOI：

10.7507/1001-5515.20160166

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Electroencephalogram (EEG) analysis has been widely used in disease diagnosis. The EEG detection of the patients with epilepsy can be used to make judgments about patients' conditions in time, which is of great practical value. Therefore, the techniques of automatic detection, diagnosis and classification of epileptic EEG signals are urgently needed. In order to realize fast and accurate automatic detection and classification of the EEG signals during the normal, interictal and ictal periods of epilepsy, we propose an automatic classification and recognition method which combines the Real Adaboost algorithm based on error-correcting output codes (ECOC) with a feature extraction method based on sample entropy (SampEn) and wavelet packet energy in this paper. In the present study, we used the sample entropy of input signals and the energy of some parts of frequency bands as features, and then we classified the extracted features with the method combining ECOC with Real AdaBoost algorithm. In order to test the validity, we used the epilepsy database from the University of Bonn. The database has 5 groups of EEG signals, which contains the data of normal people with their eyes open or closed, the data collected inside and outside of the epileptic foci from patients during their interictal period and the data from patients during their ictal period. The results showed that the method had strong abilities of classification and recognition of the EEG signals, and especially the recognition rate had been improved significantly. The average recognition rate of the EEG signals with different features during the three periods of the five groups mentioned above can reach 96.78%, which is superior to those with algorithms recorded in many other literatures. The method has better stability, processing speed and potential of real-time application, and it plays a supporting role in the prediction and detection of epilepsy in clinical practice.

Citation： ZHANGJianzhao, JIANGWei, BENXianye. Automatic Epileptic Electroencephalogram Detection during Normal, Interictal and Ictal Periods Combining Feature Extraction Based on Sample Entropy and Wavelet Packet Energy with Real AdaBoost Algorithm. Journal of Biomedical Engineering, 2016, 33(6): 1031-1038. doi: 10.7507/1001-5515.20160166 Copy

1.	ÜBEYLI E D. Wavelet/mixture of experts network structure for EEG signals classification[J]. Expert Syst Appl, 2008, 34(3): 1954-1962.
2.	MARTIS R J, ACHARYA U R, TAN J H, et al. Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals[J]. Int J Neural Syst, 2012, 22(6): 1250027.
3.	席敏, 朱国魂.多尺度排列熵及其在癫痫发作识别中的应用[J].生物医学工程学杂志,2015,32(4):751-756.
4.	KUMAR Y, DEWAL M L, ANAND R S. Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network[J]. Signal Image and Video Processing, 2014, 8(7): 1323-1334.
5.	ACHARYA U R, YANTI R, ZHENG Jia-wei, et al. Automated diagnosis of epilepsy using CWT, HOS and texture parameters[J]. Int J Neural Syst, 2013, 23(3): 1350009.
6.	CHEN Shuangshuang, ZHOU Weidong, GENG Shujuan, et al. Approach for epileptic EEG detection based on gradient boosting[J]. Journal of Measurement Science and Instrumentation, 2015, 6(1):96-102.
7.	SONG Yuedong, CROWCROFT J, ZHANG Jiaxiang. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine[J]. J Neurosci Methods, 2012, 210(2): 132-146.
8.	SONG Jiangling, HU Wenfeng, ZHANG Rui. Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine[J]. Neurocomputing, 2016, 175(A): 383-391.
9.	陈爽爽, 周卫东,袁琦,等.基于多特征的颅内脑电癫痫检测方法[J].中国生物医学工程学报,2013,32(3):279-283.
10.	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): 061907.
11.	RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
12.	洪波, 唐庆玉,杨福生,等.近似熵,互近似熵的性质,快速算法及其在脑电与认知研究中的初步应用[J].信号处理,1999,15(2):100-108.
13.	FREUND Y, SCHAPIRE R E. Experiments with a New Boosting Algorithm[C]//Thirteenth International Conference on Machine Learning. San Francisco, 1996: 148-156.
14.	SCHAPIRE R E, SINGER Y. Improved boosting algorithms using confidence-rated predictions[J]. Mach Learn, 1999, 37(3): 297-336.
15.	DIETTERICH T G, BAKIRI G. Solving multiclass learning problems via error-correcting output codes[J]. Journal of Artificial Intelligence Research, 1994, 2(1): 263-286.

1. ÜBEYLI E D. Wavelet/mixture of experts network structure for EEG signals classification[J]. Expert Syst Appl, 2008, 34(3): 1954-1962.
2. MARTIS R J, ACHARYA U R, TAN J H, et al. Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals[J]. Int J Neural Syst, 2012, 22(6): 1250027.
3. 席敏, 朱国魂.多尺度排列熵及其在癫痫发作识别中的应用[J].生物医学工程学杂志,2015,32(4):751-756.
4. KUMAR Y, DEWAL M L, ANAND R S. Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network[J]. Signal Image and Video Processing, 2014, 8(7): 1323-1334.
5. ACHARYA U R, YANTI R, ZHENG Jia-wei, et al. Automated diagnosis of epilepsy using CWT, HOS and texture parameters[J]. Int J Neural Syst, 2013, 23(3): 1350009.
6. CHEN Shuangshuang, ZHOU Weidong, GENG Shujuan, et al. Approach for epileptic EEG detection based on gradient boosting[J]. Journal of Measurement Science and Instrumentation, 2015, 6(1):96-102.
7. SONG Yuedong, CROWCROFT J, ZHANG Jiaxiang. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine[J]. J Neurosci Methods, 2012, 210(2): 132-146.
8. SONG Jiangling, HU Wenfeng, ZHANG Rui. Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine[J]. Neurocomputing, 2016, 175(A): 383-391.
9. 陈爽爽, 周卫东,袁琦,等.基于多特征的颅内脑电癫痫检测方法[J].中国生物医学工程学报,2013,32(3):279-283.
10. 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): 061907.
11. RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
12. 洪波, 唐庆玉,杨福生,等.近似熵,互近似熵的性质,快速算法及其在脑电与认知研究中的初步应用[J].信号处理,1999,15(2):100-108.
13. FREUND Y, SCHAPIRE R E. Experiments with a New Boosting Algorithm[C]//Thirteenth International Conference on Machine Learning. San Francisco, 1996: 148-156.
14. SCHAPIRE R E, SINGER Y. Improved boosting algorithms using confidence-rated predictions[J]. Mach Learn, 1999, 37(3): 297-336.
15. DIETTERICH T G, BAKIRI G. Solving multiclass learning problems via error-correcting output codes[J]. Journal of Artificial Intelligence Research, 1994, 2(1): 263-286.

Journal of Biomedical Engineering

Automatic Epileptic Electroencephalogram Detection during Normal, Interictal and Ictal Periods Combining Feature Extraction Based on Sample Entropy and Wavelet Packet Energy with Real AdaBoost Algorithm

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content