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

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

doi:10.7555/jbr.33.20190021

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.

Highlights

Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) are the most abundant phospholipids in mammalian cell membranes, with PC accounting for 45%–50% and PE for 15%–25% of the total lipid content[1]
Whether skeletal muscle PC:PE ratio plays a direct role in insulin sensitively is unclear; these findings suggest possible associations between PC and PE content and insulin resistance that warrant further investigation
Given that reduced muscle mitochondrial function is associated with insulin resistance in skeletal muscle[64, 65,91] it is likely that imbalances in the mitochondrial PC:PE ratio can play a modulatory role in muscular insulin sensitivity

Summary

Introduction

Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) are the most abundant phospholipids in mammalian cell membranes, with PC accounting for 45%–50% and PE for 15%–25% of the total lipid content[1]. This finding is corroborated in choline/ethanolamine phosphotransferase 1 (CEPT1) knockdown in C2C12 myoblasts and muscle-specific knockout mice fed a high-fat diet where CEPT1 deficiency increases the SR PC:PE ratio and decreases SERCA activity to preserve insulin sensitivity[14]. Given that mitochondrial phospholipids influence mitochondrial biogenesis[63,64], bilayer-protein interactions[61], the activity of the electron transport system and mitochondrial function[62,65], skeletal muscle mitochondrial PC:PE ratio may be an important determinant of whole-body insulin sensitivity.

Results

Conclusion