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

Shivarudhrappa Raghu, Natarajan Sriraam*, Yasin Temel, Shyam Vasudeva Rao, Alangar Sathyaranjan Hegde, Pieter L. Kubben

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Web of Science)

Abstract

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 onset. Since 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 patients. Therefore, we have investigated MODWT using four different wavelets, namely Haar, Coif4, Dmey, and Sym4 sub-bands until seven levels. Further, we have explored the potentials of six entropies, namely sigmoid, Shannon, wavelet, Renyi, Tsallis, and Steins unbiased risk estimator (SURE) entropies in each sub-band. The 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 respectively. The findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive respectively. Besides, 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 database. Similarly, using all the entropies, sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases respectively. In conclusion, the comparison results of MODWT outperformed DWT.

Original languageEnglish
Pages (from-to)213-227
Number of pages15
JournalJournal of biomedical research
Volume34
Issue number3
DOIs
Publication statusPublished - May 2020

Keywords

  • electroencephalogram
  • epileptic seizures
  • entropy
  • maximal overlap discrete wavelet transform
  • sigmoid entropy
  • support vector machine
  • EPILEPTIC SEIZURES
  • SIGNAL CLASSIFICATION
  • AUTOMATED DETECTION
  • COMPONENT ANALYSIS
  • WAVELET TRANSFORM
  • DIAGNOSIS
  • MODEL

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