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Articles

Schizophrenia Detection Using Biomarkers from Electroencephalogram Signals

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References

  • O. D. Howes, and R. M. Murray, “Schizophrenia an integrated socio developmental- cognitive model,” Lancet, Vol. 383, no. 9929, pp. 1677–87, 2014.
  • K. R. Patel, J. Cherian, K. Gohil, and D. Atkinson, “Schizophrenia: overview and treatment options,” J. Manag Care Hospital Formulary Manage, Vol. 39, no. 9, pp. 638–45, 2014.
  • E. Basar, and B. Guntekin, “A review of brain oscillations in cognitive disorders and the role of neurotransmitters,” Brain Research, Vol. 1235, pp. 172–93, 2008.
  • A. Sharma, J. K. Rai, and R. P. Tewari, “Prior forecasting of epileptic seizure and localization of epileptogenic region,” J. Biomed Eng: Appl Basis Commun, Vol. 29, no. 2, pp. 1–16, 2017.
  • A. Sharma, J. K. Rai, and R. P. Tewari. “Epileptic seizure prediction and identification of epileptogenic region using EEG signal,” Proceeding of IEEE International Conference on Green Computing and Internet of Things (ICGcIOT), Galgotia College, Greater Noida, Oct 8-10, 2015, pp. 1188-1192.
  • A. Kaplan, Y. Fingelkurts, A. A. Borisov, and S. V. Darkhovasty, “Non stationary nature of brain activity as revealed by EEG/ MEG, methodological, practical and conceptual challenges,” Signal Process, Vol. 85, pp. 2190–212, 2005.
  • S. F. Timashev, O. Y. Panishev, Y. S. Polyakov, and A. Y. Kaplan, “Analysis of cross correlation in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia,” Physica A, Vol. 391, no. 4, pp. 1179–94, 2012.
  • O. Y. Panischev, S. A. Demin, A. Y. Kaplan, and N. Y. Varaksina, “Use of cross correlation analysis of EEG signals for detecting risk level of schizophrenia,” Biomed Eng, Vol. 47, no. 3, pp. 153–6, 2013.
  • Z. D. Aharon, N. Fogelson, A. Peled, and N. Intrator, “Schizophrenia detection and classification by advanced analysis of EEG recording using single electrode approach,” PLOS ONE, Vol. 10, no. 4, pp. 1–12, 2015.
  • B. S. Raghavendra, D. N. Dutt, H. N. Hallahali, and J. P. John, “Complexity analysis of EEG in patients with schizophrenia using fractal dimension,” Physiol Meas, Vol. 30, pp. 795–808, 2009.
  • G. Arasil, V. Agah, and E. O. Kaplan. “Investigation of schizophrenic EEG signals using wavelet transform functions,” IEEE Conference on Signal Processing and Communication, Malatya, Turkey, pp. 1–5, 2015.
  • B. Thilakvathi, S. Shenbaga, K. Bhanu, and M. Malaippan, “EEG signal complexity analysis for schizophrenia during rest and mental activity,” Biomed. Res., Vol. 28, no. 1, pp. 1–9, 2017.
  • R. Boostani, K. Sadatnezhad, and M. Sabeti, “An efficient classifier to diagnose of schizophrenia based on the EEG signals,” Expert. Syst. Appl., Vol. 36, no. 3, pp. 6492–9, 2009.
  • J. W. Kim, Y. S. Lee, D. H. Han, and J. Lee, “Diagnostic utility of quantitative EEG in un-medicated schizophrenia,” Neurosci. Lett., Vol. 589, pp. 126–31, 2015.
  • M. P. Van, C. W. Mandl, C. J. Stam, R. S. Khan, and E. H. Pol, “Aberrant frontal and temporal complex network structure in schizophrenia: A graph theoretical analysis,” J. Neurosci., Vol. 30, no. 47, pp. 15915–26, 2015.
  • R. Hornero, D. Abasolo, N. Jimeno, C. I. Sanchez, J. Poza, and M. Abhoy, “Variability, regularity, and complexity of time series generated by schizophrenic patients and control subjects,” IEEE Trans. Biomed. Eng., Vol. 53, no. 2, pp. 210–8, 2006.
  • http://brain.bio.msu.ru/eeg_schizophrenia.htm
  • V. Jurack, D. Tsuzuki, and I. Dau, “10/20, 10/10 and 10/5 system revisited: their validity as relative head surface based positioning system,” J. Neuroimage, Vol. 34, no. 1, pp. 1600–11, 2007.
  • M. B. Hamaneh, N. Chitravas, K. Kaiboriboon, and S. D. Lohatoo, “Automated removal of EKG artefacts from EEG data using independent component analysis and continuous wavelet transfrom,” IEEE Trans. Biomed. Eng., Vol. 61, no. 6, pp. 1634–41, 2014.
  • S. Wang, D. Li, Y. Wei, and H. Li, “A feature selection method based on Fisher Discriminant ratio for text sentiment classification,” Expert. Syst. Appl., Vol. 38, no. 7, pp. 8696–702, 2011.
  • D. M. Hawkins, “The problem of overfitting,” J. Chem. Inf. Model., Vol. 44, no. 1, pp. 1–12, 2014.
  • C. Gomez, A. Mediavilla, R. Hornero, D. Abasolo, and A. Fernandaz, “Use of Higuchi fractal dimension for the analysis of MEG recording from Alzheimer’s disease patients,” Med. Eng. Phys., Vol. 31, no. 3, pp. 306–13, 2009.
  • A. Delmore, T. Sejnowski, and S. Makeig, “Enhaced detection of artefacts in EEG data using higher order statstics and independent component analysis,” J. Neuroimage, Vol. 34, no. 7, pp. 1443–9, 2007.
  • H. Liu, T. Zhang, Y. Ye, C. Pan, G. Yang, J. Wang, and R. C. Qiu, “A data driven approach for resting-state EEG signal classification of schizophrenia with control participants using random matrix theory,” arXiv preprint arXiv:1712.05289v2, 2018.

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