Literature DB >> 25472728

Analysis of spike waves in epilepsy using Hilbert-Huang transform.

Jin-De Zhu1, Chin-Feng Lin, Shun-Hsyung Chang, Jung-Hua Wang, Tsung-Ii Peng, Yu-Yi Chien.   

Abstract

In this paper, we used the Hilbert-Huang transform (HHT) analysis method to examine the time-frequency characteristics of spike waves for detecting epilepsy symptoms. We obtained a sample of spike waves and nonspike waves for HHT decomposition by using numerous intrinsic mode functions (IMFs) of the Hilbert transform (HT) to determine the instantaneous, marginal, and Hilbert energy spectra. The Pearson correlation coefficients of the IMFs, and energy-IMF distributions for the electroencephalogram (EEG) signal without spike waves, Spike I, Spike II and Spike III sample waves were determined. The analysis results showed that the ratios of the referred wave and Spike III wave to the referred total energy for IMF1, IMF2, and the residual function exceeded 10%. Furthermore, the energy ratios for IMF1, IMF2, IMF3 and the residual function of Spike I, Spike II to their total energy exceeded 10%. The Pearson correlation coefficients of the IMF3 of the EEG signal without spike waves and Spike I wave, EEG signal without spike waves and Spike II wave, EEG signal without spike waves and Spike III wave, Spike I and II waves, Spike I and III waves, and Spike II and III waves were 0.002, 0.06, 0.01, 0.17, 0.03, and 0.3, respectively. The energy ratios of IMF3 in the δ band to its referred total energy for the EEG signal without spike waves, and of the Spike I, II, and III waves were 4.72, 6.75, 5.41, and 5.55%, respectively. The weighted average frequency of the IMF1, IMF2, and IMF3 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of the spike waves, respectively. The weighted average magnitude of the IMF3, IMF4, and IMF5 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of spike waves, respectively.

Entities:  

Mesh:

Year:  2014        PMID: 25472728     DOI: 10.1007/s10916-014-0170-6

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  18 in total

1.  Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

Authors:  R Quian Quiroga; Z Nadasdy; Y Ben-Shaul
Journal:  Neural Comput       Date:  2004-08       Impact factor: 2.026

2.  EEG signal analysis: a survey.

Authors:  D Puthankattil Subha; Paul K Joseph; Rajendra Acharya U; Choo Min Lim
Journal:  J Med Syst       Date:  2010-04       Impact factor: 4.460

3.  EEG classification of imagined syllable rhythm using Hilbert spectrum methods.

Authors:  Siyi Deng; Ramesh Srinivasan; Tom Lappas; Michael D'Zmura
Journal:  J Neural Eng       Date:  2010-06-16       Impact factor: 5.379

Review 4.  Spike timing-dependent plasticity: a Hebbian learning rule.

Authors:  Natalia Caporale; Yang Dan
Journal:  Annu Rev Neurosci       Date:  2008       Impact factor: 12.449

5.  Hilbert-Huang transformation-based time-frequency analysis methods in biomedical signal applications.

Authors:  Chin-Feng Lin; Jin-De Zhu
Journal:  Proc Inst Mech Eng H       Date:  2012-03       Impact factor: 1.617

6.  A wavelet transform based feature extraction and classification of cardiac disorder.

Authors:  S Sumathi; H Lilly Beaulah; R Vanithamani
Journal:  J Med Syst       Date:  2014-07-15       Impact factor: 4.460

7.  Time-frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform.

Authors:  Andrea Pigorini; Adenauer G Casali; Silvia Casarotto; Fabio Ferrarelli; Giuseppe Baselli; Maurizio Mariotti; Marcello Massimini; Mario Rosanova
Journal:  J Neurosci Methods       Date:  2011-04-15       Impact factor: 2.390

8.  The effect of multiscale PCA de-noising in epileptic seizure detection.

Authors:  Jasmin Kevric; Abdulhamit Subasi
Journal:  J Med Syst       Date:  2014-08-30       Impact factor: 4.460

9.  Quantification of motion artifact rejection due to active electrodes and driven-right-leg circuit in spike detection algorithms.

Authors:  Antoine Nonclercq; Pierre Mathys
Journal:  IEEE Trans Biomed Eng       Date:  2010-07-08       Impact factor: 4.538

10.  Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination.

Authors:  Bo Yu; Terrence Mak; Xiangyu Li; Leslie Smith; Yihe Sun; Chi-Sang Poon
Journal:  Biomed Eng Online       Date:  2012-04-10       Impact factor: 2.819

View more
  3 in total

1.  A combined TMS-EEG study of short-latency afferent inhibition in the motor and dorsolateral prefrontal cortex.

Authors:  Yoshihiro Noda; Robin F H Cash; Reza Zomorrodi; Luis Garcia Dominguez; Faranak Farzan; Tarek K Rajji; Mera S Barr; Robert Chen; Zafiris J Daskalakis; Daniel M Blumberger
Journal:  J Neurophysiol       Date:  2016-05-25       Impact factor: 2.714

2.  Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism.

Authors:  Chin-Feng Lin; Jiun-Yi Su; Hao-Min Wang
Journal:  J Med Syst       Date:  2015-07-21       Impact factor: 4.460

3.  Chaotic Visual Cryptosystem Using Empirical Mode Decomposition Algorithm for Clinical EEG Signals.

Authors:  Chin-Feng Lin
Journal:  J Med Syst       Date:  2015-12-08       Impact factor: 4.460

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.