njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
Table 4.1 Wavelet Names Available for Analysis (94+)(From Wavelet Toolbox User's Guide, Version 2, The MathWorks, Inc., 9/2000) 141
142 4.2.8 System Identification Analysis All the system identification tasks were performed using the commercially available MATLAB System Identification Toolbox (The MathWorks, Inc.). The physiological data acquired from COPD and normal subjects were used for building simplified models of complex systems from respiration signals as noisy time-series data. The mathematical ARX models of dynamic cardiovascular systems based on observed input/output data were obtained. The time and frequency response plots as well as the pole-zero plots were also acquired. The control parameters of the estimated models such as ARX coefficients, the model order, and the transfer function parameters such as the overall gain, DC gain, 3 dB points and phase responses were tabulated for each subject. 4.2.9 Analysis of generation of Broadband respiration signal Part of the method of investigation for noninvasively characterizing the ANS in humans was to determine the system response at all frequencies of interest simultaneously through the use of broadband input waveforms. Berger, Saul, et al. [44] described a simple method for eliciting a broadband respiration signal without significantly altering the normal ventilatory mechanics. In their study the broadband respiratory signal was obtained by instructing the subject to start inhale/exhale as cued by a sequence of beeps (100 ms long), which is generated by a PDP-11/23 computer. The computer programs then beep evenly at a preset rate 2 0 for a few minutes so that the subject can find a comfortable depth of inspiration. The computer then switches to a mode in which the beeps were spaced at irregular intervals chosen randomly from a given distribution but
- Page 119 and 120: 90 when there is significant correl
- Page 121 and 122: 92 3.12 Partial Coherence Analysis
- Page 123 and 124: 94 after removal of the effects of
- Page 125 and 126: 96 The bulk of the theory and appli
- Page 127 and 128: 98 technique is measurement time. T
- Page 129 and 130: 100 usually attainable. The key poi
- Page 131 and 132: 102 variability exists in the propa
- Page 133 and 134: 104 eXogenous input (ARX) was used
- Page 135 and 136: 106 The baroreflex, an autonomic re
- Page 137 and 138: 108 the principal components are no
- Page 139 and 140: 110 The mathematical solution for t
- Page 141 and 142: 112 3.15 Cluster Analysis The term
- Page 143 and 144: 114 formed) one can read off the cr
- Page 145 and 146: 116 3.15.5 Squared Euclidian Distan
- Page 147 and 148: 118 Alternatively, one may use the
- Page 149 and 150: 120 Sneath and Sokal used the abbre
- Page 151 and 152: 122 may seem a bit confusing at fir
- Page 153 and 154: CHAPTER 4 METHODS The purpose of th
- Page 155 and 156: 126 4.1.2.1 Autonomic Testing. HR V
- Page 157 and 158: 128 of heart rate, blood pressure,
- Page 159 and 160: 130 The patients who underwent LVRS
- Page 161 and 162: 132 panel of the Correct.vi. It was
- Page 163 and 164: 134 4.2.3 Power Spectrum Analysis o
- Page 165 and 166: 136 weighted-average value of the c
- Page 167 and 168: 138 For each given scale a within t
- Page 169: 140 frequency F to the wavelet func
- Page 173 and 174: 144 In this study a simpler approac
- Page 175 and 176: 146 Table 4.2 Parameters That Make
- Page 177 and 178: 148 4.2.11 Cluster Analysis The sam
- Page 179 and 180: 150 viewing the time series of sequ
- Page 181 and 182: Figure 5.2 BPV analysis of a COPD s
- Page 183 and 184: Figure 5.3 HRV analysis of a normal
- Page 185 and 186: Figure 5.4.1 Comparison of the HRV
- Page 187 and 188: 158 5.2 Time Frequency Analysis One
- Page 189 and 190: Figure 5.5 Test signal with 3 sine
- Page 191 and 192: 162 Figure 5.6 (c) CWD of a signal
- Page 193 and 194: 164 Figure 5.7 (c) WT (dB4 wavelet)
- Page 195 and 196: 166 HRV more information about HRV
- Page 197 and 198: 168 Figure 5.9 (c) CWD plots of a n
- Page 199 and 200: Figure 5.10 CWT (Morlet) HRV plot o
- Page 201 and 202: 172 The following figures show the
- Page 203 and 204: 174 Figure 5.15 CWT (Mexican Hat) H
- Page 205 and 206: 176 5.2.5 Best Wavelet Selection fo
- Page 207 and 208: 178 Table 5.1 Correlation Indices o
- Page 209 and 210: 180 5.2.6 Vagal Tone and Sympathova
- Page 211 and 212: 182 These figures basically show th
- Page 213 and 214: 184 Figure 5.20 Sympathetic and par
- Page 215 and 216: 186 Figure 5.24 Sympathetic and par
- Page 217 and 218: 188 5.2.7 Time-Frequency Analysis (
- Page 219 and 220: 190 Figure 5.29 3D and contour plot
Table 4.1 Wavelet Names Available for Analysis (94+)(From Wavelet Toolbox User's<br />
Guide, Version 2, The MathWorks, Inc., 9/2000)<br />
141
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