Literature DB >> 20703641

Analysis of repetitive flash stimulation frequencies and record periods to detect migraine using artificial neural network.

Selahaddin Batuhan Akben1, Abdulhamit Subasi, Deniz Tuncel.   

Abstract

Different kind of methods has been applied to detect the migraine by using flash stimulation. Especially frequency analysis of EEG signal is the most preferred method to detect the migraine by using flash stimulation. Different flash stimulation frequencies at wide frequency range have been used in migraine detection. But the effects of these flash stimulation frequencies and the most effective frequency can be determined by analyzing these frequencies separately. Since each stimulation frequency has been implemented in different time periods, it is necessary to determine the time period to detect magnitude increase in migraine patients. The aim of this study is to determine the most effective flash stimulation frequency and time duration to detect the migraine. In this study, we analyzed the flash stimulation frequencies and time duration separately for detecting migraine. Performance of each flash stimulation frequency has been determined to detect the migraine by analyzing the power spectrums obtained under 2 Hz, 4 Hz and 6 Hz and artificial neural network has been used to determine the which data has a superior performance. Afterwards we analyzed the 2 s, 4 s, 6 s, 8 s and 10 s of flash stimulation periods separately by observing the power spectrums and the results are verified by using artificial neural network. As a result of this study we proposed the 4 Hz of flash stimulation frequency is the most effective frequency and 8 s time period is necessary to detect the migraine at the beta band of EEG's T5-T3 channel.

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Year:  2010        PMID: 20703641     DOI: 10.1007/s10916-010-9556-2

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


  9 in total

1.  Steady-state visual evoked potentials in the low frequency range in migraine: a study of habituation and variability phenomena.

Authors:  Marina de Tommaso; Sebastiano Stramaglia; Jan Mathijs Schoffelen; Marco Guido; Giuseppe Libro; Luciana Losito; Vittorio Sciruicchio; Michele Sardaro; Mario Pellicoro; Franco Michele Puca
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3.  Analysis of EEG signals under flash stimulation for migraine and epileptic patients.

Authors:  Selahaddin Batuhan Akben; Abdülhamit Subasi; Deniz Tuncel
Journal:  J Med Syst       Date:  2009-10-06       Impact factor: 4.460

4.  Analysis of EEG records in an epileptic patient using wavelet transform.

Authors:  Hojjat Adeli; Ziqin Zhou; Nahid Dadmehr
Journal:  J Neurosci Methods       Date:  2003-02-15       Impact factor: 2.390

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Authors:  W E Waters; P J O'Connor
Journal:  J Neurol Neurosurg Psychiatry       Date:  1975-06       Impact factor: 10.154

6.  Visually evoked phase synchronization changes of alpha rhythm in migraine: correlations with clinical features.

Authors:  Marina de Tommaso; Daniele Marinazzo; Marco Guido; Giuseppe Libro; Sebastiano Stramaglia; Luigi Nitti; Gianluca Lattanzi; Leonardo Angelini; Mario Pellicoro
Journal:  Int J Psychophysiol       Date:  2005-04-14       Impact factor: 2.997

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Authors:  C Lia; L Carenini; C Degioz; E Bottachi
Journal:  Ital J Neurol Sci       Date:  1995-05

8.  EEG features in juvenile migraine: topographic analysis of spontaneous and visual evoked brain electrical activity: a comparison with adult migraine.

Authors:  S Genco; M de Tommaso; A M Prudenzano; M Savarese; F M Puca
Journal:  Cephalalgia       Date:  1994-02       Impact factor: 6.292

9.  Visual evoked potentials in migraine.

Authors:  C Spreafico; R Frigerio; P Santoro; C Ferrarese; E Agostoni
Journal:  Neurol Sci       Date:  2004-10       Impact factor: 3.307

  9 in total
  3 in total

1.  Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders.

Authors:  Ercan Gokgoz; Abdulhamit Subasi
Journal:  J Med Syst       Date:  2014-04-03       Impact factor: 4.460

2.  Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques.

Authors:  Zülfikar Aslan
Journal:  Phys Eng Sci Med       Date:  2021-09-10

3.  Automatic migraine classification using artificial neural networks.

Authors:  Paola A Sanchez-Sanchez; José Rafael García-González; Juan Manuel Rúa Ascar
Journal:  F1000Res       Date:  2020-06-16
  3 in total

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