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Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset

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The Journal of Biomedical Research 2020年第3期34卷 213-227页

作者： Shivarudhrappa Raghu Natarajan Sriraam Yasin Temel Shyam Vasudeva Rao Alangar Sathyaranjan Hegde Pieter L Kubben Department of Neurosurgery School for Mental Health and Neuroscience of the Faculty of HealthMedicine and Life SciencesMaastricht UniversityMaastricht 6200 MDthe Netherlands Center for Medical Electronics and Computing M.S.Ramaiah Institute of TechnologyBengaluruKarnataka 560054India Department of Neurosurgery Maastricht University Medical Center Department of Neurosurgery Maastricht UniversityMaastricht 6200 MDthe Netherlands Institute of Neuroscience Ramaiah Medical College and HospitalsBengaluruKarnataka 560054India

In this paper,complexity analysis and dynamic characteristics of electroencephalogram(EEG) signal based on maximal overlap discrete wavelet transform(MODWT) has been exploited for the identification of seizure *** wavelet-based studies were well suited for classification of normal and epileptic seizure EEG,we have applied MODWT which is an improved version of discrete wavelet transform(DWT).The selection of optimal wavelet sub-band and features plays a crucial role to understand the brain dynamics in epileptic ***,we have investigated MODWT using four different wavelets,namely Haar,Coif4,Dmey,and Sym4 sub-bands until seven ***,we have explored the potentials of six entropies,namely sigmoid,Shannon,wavelet,Renyi,Tsallis,and Steins unbiased risk estimator(SURE) entropies in each *** sigmoid entropy extracted from Haar wavelet in sub-band D4 showed the highest accuracy of 98.44% using support vector machine classifier for the EEG collected from Ramaiah Medical College and Hospitals(RMCH).Further,the highest accuracy of 100% and 94.51% was achieved for the University of Bonn(UBonn) and CHB-MIT databases *** findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive ***,in terms of dynamic characteristics,MODWT results revealed that the highest energy present in sub-bands D2,D3,and D4 and entropies in those respective sub-bands outperformed other entropies in terms of classification results for RMCH ***,using all the entropies,sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases *** conclusion,the comparison results of MODWT outperformed DWT.

关键词： electroencephalogram epileptic seizures entropy maximal overlap discrete wavelet transform sigmoid entropy support vector machine

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