a la physique de l'information - Lisa - Université d'Angers

Recommendations

Info

Multifractality in central and peripheral cardiovascular data of healthy subjects 625 Figure 7. Average multifractal spectra of LDF signals for five healthy subjects. Forearm: the LDF probe is positioned on the ventral face of the forearm; finger: the LDF probe is positioned on the distal phalanx of the second finger (palmar side of the index) (see the text). For LDF signals, the estimation of the slope for the scaling region on the partition functions was done using boxes of size between 256 and 8192 samples. (see figure 6). The average multifractal spectrum of LDF signals recorded on the finger is also larger than the average multifractal spectrum of LDF signals recorded on the forearm (as found for the supine position). The mid-width is 0.23 for the average multifractal spectrum of LDF signals recorded on the finger, whereas it is 0.15 for the one of LDF signals recorded on the forearm. Moreover, as in the supine position, the average multifractal spectrum of LDF signals recorded on the finger is more asymmetric than the one of LDF signals recorded on the forearm. We also observe (see figure 7) that the average multifractal spectra of LDF signals have different patterns between upright and supine positions (for figure 7, 8192 samples of LDF signals (5.46 min of data) and 512 samples of HRV signals are considered for both upright and supine positions). 4. Discussion From figures 4(a) and (b) we observe that the partition functions exhibit a power-law behavior, or a linear behavior in log–log coordinates, over a significant range of scales and of exponents. This type of scaling behavior, or regular evolution across scales, is an important property to identify fractal data. From these observations, we can infer that LDF and HRV signals present multifractal properties. Moreover, our results show that the average numerically estimated multifractal spectrum of HRV data has a mid-width of 0.04 for the supine position (average value computed with 14 subjects). The recent nonlinear studies dealing with HRV mostly analyzed data from long series of R–R intervals based on ambulatory 24 h ECG recordings. Our study was designed to analyze and compare the multifractal spectra of short series (series of a few minutes; long periods of LDF recordings are not possible due to the high sensibility of the LDF technique to movements). However, we note that the average multifractal spectra we obtain with HRV short series are close to the ones presented by Munoz-Diosdado et al (2005a, 2005b) and by Guzman-Vargas et al (2005) who analyzed longer periods of data (2 h of ECG for Munoz-Diosdado et al 2005a, 2005b, and around 8 h of ECG for Guzman-Vargas et al 2005). 193/197
626 A Humeau et al Therefore, the multifractal spectra of HRV data may not vary much between short (laboratory conditions) and longer recordings (Holter ECG). From our results we also observe that, whatever the LDF probe position (finger or forearm), and whatever the subject position (supine or upright), the average multifractal spectra of LDF time series are larger than the ones of HRV data. The width of a multifractal spectrum measures the length for the range of fractal exponents in the signal. Therefore, the wider the range, the ‘richer’ the signal in structure. From our results, we may deduce that LDF signals are ‘richer’ in structure than HRV data. When LDF signals are reduced to samples acquired during the R peaks of the ECG data, the results are the same: we find larger multifractal spectra for these reduced data than for HRV. Our results also show that the width for the average multifractal spectrum of LDF signals is larger when the data are recorded on the palmar side of the finger than when they are recorded on the ventral face of the forearm; the average multifractal spectrum is also more asymmetric in the finger than in the forearm. Moreover, from our results we observe that LDF signals recorded in the upright position do not have the same multifractal spectra as the ones recorded in the supine position (see figure 7). Several hypotheses can be proposed to explain all these differences. (1) The differences observed between HRV and LDF multifractal spectra may come from the nature and origins of the signals: ECG signals (from which HRV data are extracted) come from the recording of the potential changes at the skin surface resulting from depolarization and repolarization of heart muscle. The ECG signal, being a straightforward recording of the electrical activity of the heart, is simpler (it has been produced by a simpler system) than that recorded by LDF. LDF signals come from light beating spectroscopy. In the LDF technique, a heterodyne detection process is used: shifted and non-shifted laser light is mixed together forming a dynamic self-beating speckle pattern at the detector. The coherence of the laser light and the Doppler effect lead to temporal intensity variations of the speckle areas (for the influence of the coherence laser light in the speckle variations, see for example Federico and Kaufmann (2006) and Funamizu and Uozumi (2007)). Therefore, many signal-processing steps are present between the recording of the backscattered photons and the LDF visualization device. This may lead to LDF signals becoming more noisy than HRV data. In order to analyze the possible role played by the instrumentation noise in the width of the multifractal spectra, we computed the multifractal spectrum of a noisy HRV signal (see figure 8). The latter figure shows that a very large amount of noise (3.5 times the standard deviation of the original HRV signal) has to be added to a HRV signal to cause its multifractal spectrum to become similar to that of a LDF signal. The study of the noise influence in the width of the multifractal spectra could be continued; a multifractal analysis of LDF speckles could also be conducted. (2) The larger multifractal spectra of LDF signals compared to the ones of HRV data may also come from the microcirculation flow. LDF measurements from the skin reflect perfusion in capillaries, arterioles, venules and dermal vascular plexa. The diameters of some vessels probed may therefore be in the same range as the ones of the red blood cells. The complexity of the flow in such conditions could contribute to the width of the multifractal spectra for LDF signals. This hypothesis could be analyzed by computing and processing models describing microvascular flow. (3) The differences observed between multifractal spectra of LDF signals recorded on the ventral face of the forearm and on the palmar side of the finger could be due to physiological aspects: on the palmar side of the finger, the sympathetic activity is higher than on the forearm (see for example Azman-Juvan et al (2008)). Moreover, the index microvasculature includes venoarteriolar anastomoses that largely influences the blood 194/197
Page 1 and 2:
Université d’Angers Laboratoire
Page 3 and 4:
Ce mémoire est dédié à mon épo
Page 5 and 6:
4 Physique de l’information 39 4.
Page 7 and 8:
1.2 Organisation du document L’es
Page 9 and 10:
4/197
Page 11 and 12:
de Rennes 1. Licence et Maîtrise
Page 13 and 14:
2.5 Activités de recherche 2.5.1 B
Page 15 and 16:
2.5.2 Encadrement Thèses Thèse so
Page 17 and 18:
2.5.3 Responsabilités Management d
Page 19 and 20:
[A20] S. BLANCHARD, D. ROUSSEAU, D.
Page 21 and 22:
In 8th Euro-American Workshop on In
Page 23 and 24:
études visaient alors à analyser
Page 25 and 26:
Fig. 3.1 réside dans le fait qu’
Page 27 and 28:
également permis de réaliser qu
Page 29 and 30:
caracteristique effective g eff (u)
Page 31 and 32:
des architectures neuronales contr
Page 33 and 34:
Dans l’ Éq.(3.10), ∆t représe
Page 35 and 36:
quantifieur, le niveau de saturatio
Page 37 and 38:
exemples présentés dans [39], l
Page 39 and 40:
• le type de validation des résu
Page 41 and 42:
présenté de façon condensée dan
Page 43 and 44:
38/197
Page 45 and 46:
pour l’étude de la turbulence pa
Page 47 and 48:
A B C Figure 4.1 : Images de coupe
Page 49 and 50:
atteint la capacité informationnel
Page 51 and 52:
* Figure 4.5 : Échelle optimale d
Page 53 and 54:
• L’“intégrale de corrélati
Page 55 and 56:
4.2.3 Analyse multifractale en imag
Page 57 and 58:
fonction de partition Z 10 30 10 25
Page 59 and 60:
exposant τ(q) 15 10 5 0 −5 −10
Page 61 and 62:
complexité colorimétrique des ima
Page 63 and 64:
dimension fractale généralisée D
Page 65 and 66:
est la moyenne du signal calculée
Page 67 and 68:
A B C Figure 4.17 : Observations in
Page 69 and 70:
A B C Figure 4.20 : Nombre moyen de
Page 71 and 72:
[14] S. Blanchard, D. Rousseau et F
Page 73 and 74:
[43] J. Chauveau, D. Rousseau et F.
Page 75 and 76:
[76] A. Humeau, B. Buard, F. Chapea
Page 77 and 78:
[100] M. D. McDonnell, N. G. Stocks
Page 79 and 80:
[131] D. Rousseau, F. Duan, J. Roja
Page 81 and 82:
76/197
Page 83 and 84:
D. ROUSSEAU and F. CHAPEAU-BLONDEAU
Page 85 and 86:
ROUSSEAU AND CHAPEAU-BLONDEAU: BAYE
Page 87 and 88:
ROUSSEAU AND CHAPEAU-BLONDEAU: BAYE
Page 89 and 90:
D. ROUSSEAU, G. V. ANAND, and F. CH
Page 91 and 92:
quantizers. Classically, the design
Page 93 and 94:
Fig. 2. Nonlinear array used as pre
Page 95 and 96:
probability of error P er 10 0 10 -
Page 97 and 98:
probability of error (expected as s
Page 99 and 100:
[3] N.H. Lu, B.A. Einstein, Detecti
Page 101 and 102:
Abstract Neurocomputing 71 (2007) 3
Page 103 and 104:
3. Assessing nonlinear transmission
Page 105 and 106:
output SNR 250 200 150 100 50 0 N=3
Page 107 and 108:
precisely the scope of the present
Page 109 and 110:
[4] F. Chapeau-Blondeau, G. Chauvet
Page 111 and 112:
Nonlinear SNR amplification of harm
Page 113 and 114:
P.R. BHAT and D. ROUSSEAU and G. V.
Page 115 and 116:
It is assumed that the signal s is
Page 117 and 118:
Let us de£ne the improvement in pe
Page 119 and 120:
F. CHAPEAU-BLONDEAU and D. ROUSSEAU
Page 121 and 122:
Contents Raising the noise to impro
Page 123 and 124:
Raising the noise to improve perfor
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
S. BLANCHARD, D. ROUSSEAU, D. GINDR
Page 137 and 138:
1984 OPTICS LETTERS / Vol. 32, No.
Page 139 and 140:
F. CHAPEAU-BLONDEAU, D. ROUSSEAU, S
Page 141 and 142:
1288 J. Opt. Soc. Am. A/Vol. 25, No
Page 143 and 144:
Page 145 and 146:
Page 147 and 148: 36 IEEE SIGNAL PROCESSING LETTERS,
Page 149 and 150: 38 IEEE SIGNAL PROCESSING LETTERS,
Page 151 and 152: A. HISTACE and D. ROUSSEAU. Constru
Page 153 and 154: noises Z i in (3) are chosen Gaussi
Page 155 and 156: Author's personal copy Physica A 38
Page 157 and 158: Author's personal copy F. Chapeau-B
Page 161 and 162: edundancy Author's personal copy F.
Page 163 and 164: model description length Author's p
Page 165 and 166: total description length x 10 4 1.6
Page 171 and 172: J. CHAUVEAU and D. ROUSSEAU and F.
Page 173 and 174: 2 for natural images, and carry rel
Page 175 and 176: 4 Fig. 2 Random RGB color image I2(
Page 177 and 178: 6 log2[ number of boxes N(a) ] 24 2
Page 179 and 180: 8 Fig. 7 Color histogram in the RGB
Page 181 and 182: 10 log2[ number of boxes N(a) ] 8 7
Page 183 and 184: 12 17. Landgrebe, D.: Hyperspectral
Page 185 and 186: Numerical simulation of laser Doppl
Page 187 and 188: esults are meaningful to better app
Page 189 and 190: A. HUMEAU, B. BUARD, F. CHAPEAU-BLO
Page 191 and 192: 618 A Humeau et al 1. Introduction
Page 193 and 194: 620 A Humeau et al (a) (b) (c) Figu
Page 195 and 196: 622 A Humeau et al (a) (b) (c) Figu
Page 197: 624 A Humeau et al Figure 5. Averag
Page 201 and 202: 628 A Humeau et al peripheral cardi
show all

a la physique de l'information - Lisa - Université d'Angers

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?