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Comparison Of FFT And AR Techniques For Scalp EEG Analysis

  • Conference paper

Part of the book series: IFMBE Proceedings ((IFMBE,volume 21))

Abstract

Scalp electroencephalogram (EEG) with bipolar montage is used in most infirmaries for monitoring epilepsy. However, scalp EEG is unpopular as compared to IEEG (intra-cranial EEG) in the research field. Most researchers used IEEG and scalp EEG with unipolar montage. Bipolar montage is also rarely used in the research in contrast to unipolar montage. The main aim of this paper is to investigate and determine a suitable method for processing EEG data using bipolar montage directly from the hospital archive. Two well-known methods namely, the Fast Fourier Transform (FFT) and the Autoregressive (AR) will be analyzed and compared based on their power spectrums. Results obtained based on monitored frequencies showed that the AR method is better than FFT in delineating the epilepsy region which can be visually observed and recognizable.

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References

  1. Maiwald T, Winterhalder M, Aschenbrenner-Scheibe R, Voss HU et al (2004) Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristics. Physica D 194: 357–368

    Article  MATH  Google Scholar 

  2. Mormann F, Andrzejak RG, Elger C E & Lehnertz K. (2006) Seizure prediction: the long and winding road. Brain. DOI:10.1093/brain/aw1241

    Google Scholar 

  3. Ubeyli E D, Cvetkovic D, Cosic I (2008) AR Spectral Analysis Technique for Human PPG, ECG and EEG Signals. J Med Syst. DOI 10.1007/s10916-007-9123-7

    Google Scholar 

  4. Griffiths M J, Grainger P, Cox M V et al (2005) Recent Advances in EEG Monitoring For General Anesthesia, Altered States of Consciousness and Sports Performance Science. The 3rd IEE International Seminar on Medical Applications of Signal Processing, London, UK, 2005, pp: 1–5

    Google Scholar 

  5. Jiruska, P., Proks, J., Drbal, O., Sovka, P., Maruscic, P., & Mares. P. (2005) Comparison of Different Method of Time Shift Measurement in EEG. Physiol. Res. 54:459–456

    Google Scholar 

  6. Hu M, Shoa H (2007) Autoregressive spectral analysis on statistical autocorrelation. Physica A 376:139–146

    Article  Google Scholar 

  7. Akin A & Kiymik, M K (2000) Application of Periodogram and AR Spectral Analysis to EEG Signals. Journals of Medical Systems. vol. 24(4): 247–256

    Article  Google Scholar 

  8. Krusienski D J, McFarland D J, Wolpaw J R. (2006) An Evaluation of Autoregressive Spectral Estimation Model Order for Brain-Computer Interface Applications. Proc. of the 28th IEEE EMBS Annual International Conference. New York, USA. 1323–1326

    Google Scholar 

  9. Freeman W J, Viana di Prisco G (1986) Relation of olfactory EEG to behavior: Time series analysis. Behavioural Neuroscience. vol 100(5):753–763.

    Article  Google Scholar 

  10. Takola R, Hytti H, Ihalainen H (2005) Tutorial on Univariate Autoregressive Spectral Analysis. J Clinical Monitoring and Computing. 19:401–410. DOI 10.1007/s10877-005-7089-x

    Article  Google Scholar 

  11. Subasi A (2007) Selection of optimal AR spectral estimation method for EEG signals using Cramer-Roa bound. Computer in Biology and Medicine 37: 183–194.

    Article  Google Scholar 

  12. Empson J (1986) Human Brainwaves. The Psychological Significance of the Electroencephalogram, Macmillan Press Ltd, London

    Google Scholar 

  13. Vanhatalo S, Voipo J & Kaila M (2005) Full-band EEG (FbEEG): an emerging standart in electroencephalography. Clinical Neuroplysiology, 116: 1–8

    Article  Google Scholar 

  14. Ghafar R, Md Tahir N, Hussain A & Abd. Samad S (2007) Characterizing Seizure from EEG Data using UMACE Filter. Proc of International Conference on Robotics, Vision, Information and Signal Processing, Penang, Malaysia, 2007, pp: 555–558

    Google Scholar 

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Correspondence to Rosniwati Ghafar .

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© 2008 Springer-Verlag Berlin Heidelberg

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Ghafar, R., Hussain, A., Samad, S.A., Tahir, N.M. (2008). Comparison Of FFT And AR Techniques For Scalp EEG Analysis. In: Abu Osman, N.A., Ibrahim, F., Wan Abas, W.A.B., Abdul Rahman, H.S., Ting, HN. (eds) 4th Kuala Lumpur International Conference on Biomedical Engineering 2008. IFMBE Proceedings, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69139-6_43

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  • DOI: https://doi.org/10.1007/978-3-540-69139-6_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69138-9

  • Online ISBN: 978-3-540-69139-6

  • eBook Packages: EngineeringEngineering (R0)

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