Examining EEG Signals with Spectral Analyses Methods in Migrain Patients during Pregnancy

Abstract

Automatic music classification is very useful to music indexing, content-based retrieval and on-line music distribution, but it is a challenge to extract the most common and salient themes from unstructured raw music data. In this paper,   a novel approach is proposed to automatically classify the Radif of Mirza Abdollah a canonic repertoire of Persian music. The Radif is made up essentially of non-measured pieces or free rhythm which provide a generative model or pattern for the creation of new composition. Music Segments are decomposed according to time segments obtained from the beginning parts of the original music signal into segments of 3 sec. In order to better classify pure and vocal music, a number of features including inharmonicity, mel-frequency cepstral coefficient, pitch, mean and standard deviation of spectral centroid are extracted to characterize the music content which are mainly related to frequency domain. Experimental results are carried out on a novel database, which contains 250 gushe of the repertoire played by the four most famous Iranian masters and performed on two stringed instruments the Tar & The Setar. Classical machine learning algorithms such as MLP neural networks, KNN and SVM are employed. Finally, SVM shows a better performance in music classification than the others.

Keywords: Repertoire, inharmonicity,Mel-Frequency Cepstral Coefficient, pitch, gushe, K- nearest neighbors, support Vector Machines

Keywords

• EEG, Migraine, Spectral Analysis, Welch, Burg, Covariance, Modified Covariance

References

1. [1] Wong TW, Wong KS, Yu TS, Kay R. Prevalence of migraine and other headaches in Hong Kong. Neuroepidemiology, 14:82-91(1995).
2. [2] Silberstein, Stephen D. Headache in Clinical Practice (2nd Ed.). London: Taylor & Francis Group. ISBN 1-901865-88-6(2002).
3. [3] Dahlöf, C., Linde M., One-year prevalence of migraine in Sweden: a population- based study in adult,Cephelalgia, 664-671(2001).
4. [4] Fisch&Spehlmann’s EEG Primer: Basic Principles of Digital and Analog EEG, Elsevier, ISBN: 978- 975- 9057-34-4.
5. [5] Güler, I., Kıymık, M.K., Akin, M., and Alkan, A., “AR Spectral Analysis of EEG Signals by using maximum likelihood estimation”,Comput. Biol. Med., 31:441-450, (2001).
6. [6] Zoubir, M., and Boashash, B., “Seizure detection of newborn EEG using a model approach”, IEEE Trans. Biomed. Eng., 45(6):(1998).
7. [7] Subha, D.P., Joseph, P.K., Rajendra, A.U., Lim, C.M., “ EEG Signal Analysis: A Survey”, J Med. Syst, 34:195-212, (2010).
8. [8] Proakis, J.G., Manolakis, D.G., “Digital Signal Processing Principles, Algorithms, and Applications”,Prentice-Hall, New Jersey, (1996).

9. [9] Sand, T.,“EEG in migraine: a review of the literature”, Funct Neurol., 6(1):7-22 (1991).
10. [10] Welch,P.D.,“The use of fast Fourier transform for the estimation of power spec-tra: a method based on timeaveraging over short, modified periodograms”, IEEE Transactions on Audio and Electroacoustics,15 (2):70–73 (1967).
11. [11] Orfanidis, S. J.,“Introduction to Signal Processing”, Englewood Cliffs, NJ: Prentice-Hall, (1995).
12. [12] Akaike, H.,“A new look at statistical model identification”, IEEE Transactions on Automatic Control, 19: 716–723(1974).