Application of entropies for automated diagnosis of epilepsy using EEG signals: A review

被引:359
作者
Acharya, U. Rajendra [1 ,2 ,3 ]
Fujita, H. [4 ]
Sudarshan, Vidya K. [1 ]
Bhat, Shreya [5 ]
Koh, Joel E. W. [1 ]
机构
[1] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore 599489, Singapore
[2] SIM Univ, Sch Sci & Technol, Dept Biomed Engn, Singapore, Singapore
[3] Univ Malaya, Dept Biomed Engn, Fac Engn, Kuala Lumpur, Malaysia
[4] Iwate Prefectural Univ, Fac Software & Informat Sci, Morioka, Iwate, Japan
[5] Manipal Inst Technol, Dept Biomed Engn, Manipal 576104, Karnataka, India
关键词
Epilepsy; Interictal; EEG; Entropy; Fuzzy; HOS; APPROXIMATE ENTROPY; SEIZURE DETECTION; PERMUTATION ENTROPY; NEURAL-NETWORK; TIME-SERIES; SCALP EEG; CLASSIFICATION; IDENTIFICATION; SYSTEM; QUANTIFICATION;
D O I
10.1016/j.knosys.2015.08.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Epilepsy is the neurological disorder of the brain which is difficult to diagnose visually using Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using EEG signals will be a useful tool in medical field. The automation of epilepsy detection using signal processing techniques such as wavelet transform and entropies may optimise the performance of the system. Many algorithms have been developed to diagnose the presence of seizure in the EEG signals. The entropy is a nonlinear parameter that reflects the complexity of the EEG signal. Many entropies have been used to differentiate normal, interictal and ictal EEG signals. This paper discusses various entropies used for an automated diagnosis of epilepsy using EEG signals. We have presented unique ranges for various entropies used to differentiate normal, interictal, and ictal EEG signals and also ranked them depending on the ability to discrimination ability of three classes. These entropies can be used to classify the different stages of epilepsy and can also be used for other biomedical applications. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:85 / 96
页数:12
相关论文
共 107 条
[81]   Practical considerations of permutation entropy [J].
Riedl, M. ;
Mueller, A. ;
Wessel, N. .
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2013, 222 (02) :249-262
[82]   Computing the q-index for Tsallis Nonextensive Image Segmentation [J].
Rodrigues, Paulo S. ;
Giraldi, Gilson. A. .
2009 XXII BRAZILIAN SYMPOSIUM ON COMPUTER GRAPHICS AND IMAGE PROCESSING (SIBGRAPI 2009), 2009, :232-+
[83]   Wavelet entropy:: a new tool for analysis of short duration brain electrical signals [J].
Rosso, OA ;
Blanco, S ;
Yordanova, J ;
Kolev, V ;
Figliola, A ;
Schürmann, M ;
Basar, E .
JOURNAL OF NEUROSCIENCE METHODS, 2001, 105 (01) :65-75
[84]   Complex systems and the technology of variability analysis [J].
Seely, AJE ;
Macklem, PT .
CRITICAL CARE, 2004, 8 (06) :R367-R384
[85]  
SHANNON CE, 1948, BELL SYST TECH J, V27, P379, DOI [DOI 10.1002/J.1538-7305.1948.TB00917.X, DOI 10.1002/J.1538-7305.1948.TB01338.X]
[86]   Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals [J].
Sharma, Rajeev ;
Pachori, Ram Bilas ;
Acharya, U. Rajendra .
ENTROPY, 2015, 17 (02) :669-691
[87]  
Shen P. C., 2013, PLOS ONE, V8
[88]   Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine [J].
Song, Yuedong ;
Crowcroft, Jon ;
Zhang, Jiaxiang .
JOURNAL OF NEUROSCIENCE METHODS, 2012, 210 (02) :132-146
[89]  
Sonnino G., 2013, PHYS AUC, V23, P10
[90]   Approximate entropy-based epileptic EEG detection using artificial neural network's [J].
Srinivasan, Vairavan ;
Eswaran, Chikkannan ;
Sriraam, Natarajan .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2007, 11 (03) :288-295