Various epileptic seizure detection techniques using biomedical signals: a review

被引:60
|
作者
Paul Y. [1 ]
机构
[1] School of Informatics, Eötvös Loránd University, Budapest
关键词
Electroencephalogram (EEG); Empirical mode decomposition; Epilepsy; Fourier transform; Hilbert transform; Particle swarm optimization (PSO); Rational function; Wavelet;
D O I
10.1186/s40708-018-0084-z
中图分类号
学科分类号
摘要
Epilepsy is a chronic chaos of the central nervous system that influences individual’s daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain’s electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Approximately one out of every three patients have frequent seizures, despite treatment with multiple anti-epileptic drugs. According to a survey, population aged 65 or above in European Union is predicted to rise from 16.4% (2004) to 29.9% (2050) and also this tremendous increase in aged population is also predicted for other countries by 2050. In this paper, seizure detection techniques are classified as time, frequency, wavelet (time–frequency), empirical mode decomposition and rational function techniques. The aim of this review paper is to present state-of-the-art methods and ideas that will lead to valid future research direction in the field of seizure detection. © 2018, The Author(s).
引用
收藏
相关论文
共 50 条
  • [21] A Fuzzy Classifier based Detection for Epileptic Seizure Signals
    AL-Bokhity, B.
    Nashat, Dalia
    Nazmy, T. M.
    2017 13TH INTERNATIONAL COMPUTER ENGINEERING CONFERENCE (ICENCO), 2017, : 100 - 105
  • [22] The detection of epileptic seizure signals based on fuzzy entropy
    Xiang, Jie
    Li, Conggai
    Li, Haifang
    Cao, Rui
    Wang, Bin
    Han, Xiaohong
    Chen, Junjie
    JOURNAL OF NEUROSCIENCE METHODS, 2015, 243 : 18 - 25
  • [23] Detection of epileptic seizure in EEG signals using linear least squares preprocessing
    Zamir, Z. Roshan
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2016, 133 : 95 - 109
  • [24] EPILEPTIC SEIZURE DETECTION IN EEG SIGNALS USING MULTIFRACTAL ANALYSIS AND WAVELET TRANSFORM
    Uthayakumar, R.
    Easwaramoorthy, D.
    FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2013, 21 (02)
  • [25] Detection of Epileptic Seizure from EEG Signals by Using Recurrence Quantification Analysis
    Kutlu, Funda
    Kose, Cemal
    2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 1387 - 1390
  • [26] Analysis of EEG Signals for Detection of Epileptic Seizure Using Hybrid Feature Set
    Gill, Ammama Furrukh
    Fatima, Syeda Alishbah
    Akram, M. Usman
    Khawaja, Sajid Gul
    Awan, Saqib Ejaz
    THEORY AND APPLICATIONS OF APPLIED ELECTROMAGNETICS, 2015, 344 : 49 - 57
  • [27] An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development
    Lasefr, Zakareya
    Elleithy, Khaled
    Reddy, Ramasani Rakesh
    Abdelfattah, Eman
    Faezipour, Miad
    APPLIED SCIENCES-BASEL, 2023, 13 (17):
  • [28] Epileptic Seizure Detection using Singular Values and Classical Features of EEG Signals
    Elmahdy, Ahmed E.
    Yahya, Norashikin
    Kamel, Nidal S.
    Shahid, Arslan
    2015 INTERNATIONAL CONFERENCE ON BIOSIGNAL ANALYSIS, PROCESSING AND SYSTEMS (ICBAPS), 2015,
  • [29] Epileptic seizure detection from EEG signals using logistic model trees
    Kabir E.
    Siuly
    Zhang Y.
    Brain Informatics, 2016, 3 (2) : 93 - 100
  • [30] Epileptic Seizure Detection from EEG Signals by Using Wavelet and Hilbert Transform
    Polat, Hasan
    Ozerdem, Mehmet Sirac
    2016 XII INTERNATIONAL CONFERENCE ON PERSPECTIVE TECHNOLOGIES AND METHODS IN MEMS DESIGN (MEMSTECH), 2016, : 66 - 69