Quantitative analysis of EEG signals: Time-frequency methods and ...

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marked decrease in computational time. Other works also used this approach for automatic detection of spike complexes characteristic of epilepsy, thus helping in the analysis of EEG recordings from epileptic patients (Schi et al., 1994b Senhadji et al., 1995 Clark et al., 1995). Demiralp et al. (1999) used coecients in the delta frequency band for detecting P300 waves in single trials of an auditory oddball paradigm. Furthermore, they used this approach for making a selectiveaveraging of the single trials, thus obtaining a better denition of the P300. Basar et al. (1999) reported the utility ofWavelet Transform for classifying dierent type of single sweep responses to cross-modality stimulation. A digital ltering of ERPs based on the Wavelet Transform was proposed by Bertrand et al. (1994). They used the method as a noise reduction technique, reporting better results than the ones obtained with Fourier based methods, especially when applied to non-stationary signals. The main goal of this type of ltering is to extract the eventrelated responses from the single sweeps by eliminating the contribution of the ongoing EEG. This procedure would avoid the necessity of averaging the single sweeps. In this context, Bartnik et al. (1992) characterized the event-related responses from the wavelet coecients, then using selected coecients for isolating the event-related responses from the background EEG in the single trials. A similar approach has being later proposed by Zhang and Zheng (1997). Akay et al. (1994) used the Wavelet Transform for characterizing electrocortical activity of fetal lambs, reporting much better results than the ones obtained with the Gabor Transform. Thakor et al. (1993) analyzed somatosensory EPs of anesthetized cats with cerebral hypoxia by using the multiresolution decomposition. They report that selected coecients are sensitive to neurological changes, but having comparable results than the ones obtained with Fourier based methods. Ademoglu et al. (1997) used wavelet analysis for discriminating between normal and demented subjects by studying the N70-P100-N130 complex response to pattern reversal visual evoked potentials (N70 and N130 are negative peaks of the ERP with a latency of 70 and 130ms respectively). Kolev et al. (1997) used the multiresolution decomposition for studying the presence of dierent functional components in the P300 latency range in an auditory oddball paradigm. Basar et al. (1999) used the wavelet decomposition for studying the alpha responses to cross-modality stimulation, reporting similar results than the ones obtained with digital ltering. 45
Figure 15: Scalp EEG seizure recording. 4.4 Application to scalp recorded tonic-clonic seizures 4.4.1 Material and Methods An EEG time series corresponding to a tonic-clonic seizure of an epileptic patient was analyzed. Scalp electrodes were applied following the 10-20 international system. The signal was digitized at 409:6 Hz through a 12 bit A/D converter and ltered with an antialiasing eight pole lowpass Bessel lter with a cuto frequency of 50 Hz. Then, it was digitally ltered with a 1 ; 50 Hz bandwidth Butterworth lter and stored, after decimation, at 102:4 Hz in a PC hard drive. The recording included one minute of the EEG before the seizure onset and two minutes which included the ictal and post-ictal phases. All 3 minutes were analyzed at the right central (C4) electrode, choosing this electrode after visual inspection of the EEG as the one with the least amount of artifacts. Wavelet Transform was applied by using a cubic Spline function as mother wavelet. The multiresolution decomposition method (Mallat, 1989) was used for separating the signal in 7 frequency bands: B 1 = 25:8 ; 51:2Hz B 2 = 12:8 ; 25:2Hz B 3 = 6:4 ; 12:8Hz B 4 =3:2 ; 6:4Hz B 5 =1:6 ; 3:2Hz B 6 =0:8 ; 1:6Hz B 7 =0:4 ; 0:8Hz). 4.4.2 Results Figure 15 shows 90sec of the Tonic-Clonic seizure studied. The whole recording was already shown in g. 11. In this case seizure starts at second 10 and ends at second 85. Due to the fact that the aim of this work was to analyze middle and low frequencies 46
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Quantitative analysis of EEG signal
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1. Berichterstatter: Prof. Dr.-Ing.
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an okzipitalen Positionen und (c) d
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To my family: Mama, Consuelo, Hugui
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Preface In this work, I will descri
Page 12 and 13: I would certainly like to remember
Page 14 and 15: Contents Zusammenfassung Preface Ac
Page 16 and 17: 5.2.6 Calculating Lyapunov Exponent
Page 18 and 19: Summary Since the rsts recordings i
Page 20 and 21: 1 Outline of Neurophysiology: Brain
Page 22 and 23: Figure 2: Normal 20 channels (10-20
Page 24 and 25: 1.1.2 EEG in Epilepsy One important
Page 26 and 27: Figure 3: Scalp EEG of 18 channels
Page 28 and 29: Figure 4: Averaged responses upon N
Page 30 and 31: 2 Fourier Transform 2.1 Introductio
Page 32 and 33: I xx (k) =jX(k)j 2 = X(k) X (k) (
Page 34 and 35: 17 Figure 5: Topographical mapping
Page 36 and 37: 1987) is that coherence gives the c
Page 38 and 39: 3 Gabor Transform (Short Time Fouri
Page 40 and 41: where k g D k= R 1 ;1 jg D(t 0 )j 2
Page 42 and 43: and the band maximum peak frequency
Page 44 and 45: Figure 7: Intracraneal seizure reco
Page 46 and 47: Figure 10: Mean (soft line) and max
Page 48 and 49: EEG as the one with least amount of
Page 50 and 51: Figure 11: Scalp EEG of the right c
Page 52 and 53: 3.5 Conclusion In this chapter I de
Page 54 and 55: Original signal FOURIER TRANSFORM G
Page 56 and 57: 4.2.3 Multiresolution Analysis Cont
Page 58 and 59: Original signal -15 0 15 -1sec 0 1s
Page 60 and 61: 4.2.5 Wavelet Packets In the approa
Page 64 and 65: ain activity during an epileptic se
Page 66 and 67: 3 2 3 3 3 3.2 Hz 3.6 Hz 4.0 Hz 4.4
Page 68 and 69: Silva et al.,1973a,1973b Lopes da S
Page 70 and 71: sweep #1 sweep #2 sweep #3 sweep #4
Page 72 and 73: VEP non-target target F3 F4 F3 F4 F
Page 74 and 75: VEP non-target target F3 F4 F3 F4 F
Page 76 and 77: Electrode F3 F4 Cz P3 P4 O1 O2 Dela
Page 78 and 79: In conclusion, our results point to
Page 80 and 81: 63 Figure 22: Gamma responses for a
Page 82 and 83: Figure 24: T-test comparison of the
Page 84 and 85: wavelet coecients allowed an easy d
Page 86 and 87: the position vs. the linear momentu
Page 88 and 89: unknown topology it is necessary to
Page 90 and 91: the sum of the positive exponents (
Page 92 and 93: performed. On one hand, must be lar
Page 94 and 95: Duke (1992) and Elbert et al. (1994
Page 96 and 97: agation time. They applied this pro
Page 98 and 99: Figure 28: Histogram for the EEG da
Page 100 and 101: Figure 29: Attractors corresponding
Page 102 and 103: able for automatic detection proces
Page 104 and 105: ENTROPY Thermodynamics Signal analy
Page 106 and 107: 6.3 Application to visual event-rel
Page 108 and 109: VEP Non Target Target F3 -20 -10 0
Page 110 and 111: 1 VEP NON-TARGET TARGET 1 1 F3 F3 0
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1 VEP NON-TARGET TARGET 1 1 F3 F3 0
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and the Lorenz equations (a model o
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P300 deection. Due to the relation
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7 General Discussion In this chapte
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Wavelet analysis was also applied t
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aect the whole spectrum and for thi
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peaks. 7.2.3 Wavelet Transform vs.
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the ones having wide peaks (being t
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A Time-frequency resolution and the
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Z 1 ;1 tx(t) d dt x(t) dt = 1 2 Z 1
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Figure 37: Time-frequency resolutio
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References Abarbanel HDI, Brown R,
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Berger, H. Uber das Elektrenkephalo
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Gastaut H and Broughton R. Epilepti
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Lopes da Silva FH, van Lierop THMT,
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Pradhan N, Sadasivan P, Chatterji S
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Serrano E, Ph.D. Thesis, Department
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Biographical sketch 21.03.1967 born
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