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

More documents

Recommendations

Info

7 General Discussion In this chapter I will rst discuss how the results described in this thesis are related with several questions of neurophysiology, so far still unresolved with the traditional approaches. The joining of evidence obtained with the described methods sheds lighton these topics and allows the conjecture of physiological mechanisms. In the second part of this chapter I will compare these methods, stressing their advantages and disadvantages when applied to the study of EEG signals. 7.1 Physiological considerations 7.1.1 Dynamics of Grand Mal seizures Chaos analysis of epileptic seizures leads to the general result that during seizures a transition from a complex system to a simpler one takes place. On the other hand, by using the RIR dened from the Gabor Transform, I showed and quantied a well dened frequency behavior during seizures. Grand Mal seizures were dominated by alpha and theta frequencies. Delta oscillations decreased during them and had an abrupt increase correlated with the clonic phase. With Wavelet Packets, I showed with a better resolution the temporal evolution of these frequency patterns and it was possible to establish that the low frequency activity (3 ; 4Hz) related with the rhythmic contractions of the clonic phase, was in fact originated by the \slowing" of higher frequencies (at least 8 ; 9Hz). Then, during Grand Mal seizures there is a clear frequency dynamics: some seconds after the starting of the seizure alpha and theta activity dominates, these oscillations later becoming slower and when they are in the limit of the delta band (about 3 ; 4Hz) the clonic phase of the seizure starts and delta activity has an abrupt increase dominating the EEG recording. Moreover, it is reasonable to conjecture that the violent contractions of the clonic phase are the response to brain oscillations that are generated in higher frequencies, but owing to the fact that muscles cannot react so fast, muscle activity is then limited to a tonic contraction (muscular tension) until brain oscillations become slower and muscles are capable of contracting in resonance with them. The frequency pattern described is in agreement with studies in animals and with computer simulations. Furthermore, it would be very interesting to investigate possible causes of this behavior. Neuronal fatigue is one of the most plausible explanations. The ring of a neuron is produced as a response to excitatory inputs of neighboring neurons. This connection is done mainly by means of synaptical processes generated by neurotransmitters produced in the neurons. During an epileptic seizure there is an 101
abnormal ring of the neurons, reected in high amplitude paroxysms as spikes or more generally in a marked increase of the overall amplitude of the EEG. Then, if the neurons are not capable of generating the necessary amount of neurotransmitters for sustaining this uncommon intensive ring, synaptical connections will decrease and excitatory input can not be transmitted. This process, known as neuronal fatigue, produces a change in the oscillatory behavior of cell groups, probably leading to the described \slowing" of the frequency patterns during Grand Mal seizures. It can be also conjectured that this frequency dynamics is due to a recruitment of GABA inhibitory channels, variation in the neurotransmitter balance, etc. Results in this respect should be validated and compared with analysis in intracranial recordings. I also showed with the mean and maximum band frequencies, dened from the Gabor Transform, the presence of a low amplitude but well dened peak in the delta band (1 ; 3Hz) during a seizure (referred to as a latent pacemaker). Then, based on the frequency dynamics previously described, it is possible to conjecture that oscillations in the range of the alpha or theta band characteristic of Grand Mal seizures, once they start to oscillate in the range of the delta band (after the \slowing" process), they provoke an abrupt increase of the delta activity duetoaresonance phenomenon with the latent delta pacemaker. This resonance and massive synchronization would be responsible of the starting of the clonic phase of the seizure. 7.1.2 Event-related responses Alpha responses to visual event-related potentials were studied with the Wavelet Transform. Since the sources and functions of alpha oscillations are still subject to discussion, the aim of this approach was to obtain more information about this topic by using a new and powerful method, namely wavelets. In this context, I showed that stimulation leads to alpha enhancements visualized upon all electrodes with some signicant delays between the anterior and posterior locations. Enhancements were signicantly higher in occipital locations, had short latency and were statistically independent of the stimulus type (one of them requiring a cognitive process), thus pointing towards a relation between alpha oscillations and sensory (i.e. \pre-cognitive") processing. Furthermore, the delay dierences between responses in dierent electrodes imply that event-related alpha oscillations have several sources, thus ruling out the hypothesis of a unique generator and propagation by volume conduction. It is interesting to remark that the resolution of the Wavelet Transform was crucial for achieving statistical signicance of the results. However, it is important tomention that \alpha oscillations" is a concept that include several type of dierent processes and the previous interpretation should not be taken as exclusive or general description of them. 102
Page 1 and 2:
Quantitative analysis of EEG signal
Page 3 and 4:
1. Berichterstatter: Prof. Dr.-Ing.
Page 5 and 6:
an okzipitalen Positionen und (c) d
Page 8 and 9:
To my family: Mama, Consuelo, Hugui
Page 10 and 11:
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 62 and 63:
marked decrease in computational ti
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
Page 112 and 113: 1 VEP NON-TARGET TARGET 1 1 F3 F3 0
Page 114 and 115: and the Lorenz equations (a model o
Page 116 and 117: P300 deection. Due to the relation
Page 120 and 121: Wavelet analysis was also applied t
Page 122 and 123: aect the whole spectrum and for thi
Page 124 and 125: peaks. 7.2.3 Wavelet Transform vs.
Page 126 and 127: the ones having wide peaks (being t
Page 128 and 129: A Time-frequency resolution and the
Page 130 and 131: Z 1 ;1 tx(t) d dt x(t) dt = 1 2 Z 1
Page 132 and 133: Figure 37: Time-frequency resolutio
Page 134 and 135: References Abarbanel HDI, Brown R,
Page 136 and 137: Berger, H. Uber das Elektrenkephalo
Page 138 and 139: Gastaut H and Broughton R. Epilepti
Page 140 and 141: Lopes da Silva FH, van Lierop THMT,
Page 142 and 143: Pradhan N, Sadasivan P, Chatterji S
Page 144 and 145: Serrano E, Ph.D. Thesis, Department
Page 146: Biographical sketch 21.03.1967 born
show all

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

Create successful ePaper yourself

Delete template?

Save as template?