njit-etd2003-081 - New Jersey Institute of Technology

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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
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Copyright Warning & Restrictions Th
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ABSTRACT TIME-FREQUENCY INVESTIGATI
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TIME-FREQUENCY INVESTIGATION OF HEA
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APPROVAL PAGE TIME-FREQUENCY INVEST
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BIOGRAPHICAL SKETCH (Continued) D.A
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ACKNOWLEDGMENT The author wishes to
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TABLE OF CONTENTS Chapter Page 1 IN
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TABLE OF CONTENTS (Continued) Chapt
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LIST OF FIGURES Figure Page 2.1 The
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LIST OF FIGURES (Continued) Figure
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LIST OF FIGURES (Continued) Figure
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ABBREVIATIONS A ABP Arterial Blood
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ABBREVIATIONS (Continued) P PID Pro
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2 mortality and sudden death. [2] B
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4 1) Patients with severe pulmonary
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6 examined for determining the inte
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8 identified using principal compon
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1 0 1. Present the application of t
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12 1.3 Outline of the Dissertation
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CHAPTER 2 PHYSIOLOGY BACKGROUND Bio
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16 Figure 2.2 The systemic and pulm
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18 illustrated in Figure 2.3. The i
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20 2.2 Blood Pressure The force tha
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22 2.4 The Nervous System Human beh
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24 The sympathetic nerve fibers lea
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26 Without these sympathetic and pa
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28 center in the medulla, which con
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30 Figure 2.6 Autonomic innervation
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32 average heart rate was measured
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34 However, they do note that there
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Figure 2.9 The placement of the pos
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38 female. While more men suffer fr
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40 Stage II: Moderate COPD - Worsen
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CHAPTER 3 ENGINEERING BACKGROUND Th
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44 Two common types of time-frequen
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46 STFT: Short-Time Fourier Transfo
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48 3.3 The Analytic Signal and Inst
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50 The advantage of using equation
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52 3.5 Covariance and Invariance Th
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where H(f), S(f) are Fourier transf
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56 Another shortcoming of the spect
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58 should take the kernel of the WD
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60 called the cross Wigner distribu
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62 3.6.3 The Choi-Williams (Exponen
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64 Figure 3.3 Performance of the Ch
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66 [-Ω,Ω ], then its STFT will be
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68 This condition forces that the w
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70 where c is a constant. Thus, the
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Figure 3.5 The time-frequency plane
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74 The measure dadb used in the tra
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76 and the wavelet transform repres
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78 Figure 3.6 Figure depicting the
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80 The final step to obtain the pow
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82 It should be noted that if the w
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84 The normal respiration rate can
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Figure 3.12 Power spectrum of BP II
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RR similar manner to give: When com
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
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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
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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
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192 Figure 5.33 3D and contour plot
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194 Figure 5.34 Sympathetic and par
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Figure 5.44 Plot of raw respiration
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Figure 5.46 The LF partial coherenc
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Figure 5.48 HF partial coherence pl
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Table 5.2 Cross-Spectral Analysis o
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Table 5.3 Cross-Spectral Analysis o
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Figure 5.50 HF coherence of COPD (1
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212 For better presentation of the
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Figure 5.53 Coherence and partial c
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216 2. Interpretation of the transf
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218 covariances of the parameters,
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220 deviations are interpreted as A
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222 Figure 5.58 Bode plot of the HR
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224 In this section a simple model
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226 The data for all 47 COPD subjec
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228 Figure 5.60 Normal and COPD cla
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230 Figure 5.61 Normal and COPD cla
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232 Figure 5.62 Normal classificati
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234 5.7 Cluster Analysis The purpos
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236 Figure 5.64 Severity classifica
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238 both the normal and COPD subjec
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240 In summary, COPD subjects had h
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APPENDIX A EXERCISE PHYSIOLOGY A.1
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244 A.3 Figure Out Your Target Hear
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APPENDIX B ANALYSIS PROGRAM LISTING
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248 4) Click on file, close to exit
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250 • TN 11
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252 B.1.2 Partial Coherence Between
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254
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256 Block Diagram !rime of record K
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258
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260 B.2.2 Time — Frequency Analys
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262 This program provides the STFT
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264 G(:j+1)=G(:,j+1)/(2*sum(G(:j+1)
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266 T=(length(Signa)/sample)/(Times
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268 subplot(3, 1,3), plot(T,E); xla
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270 4. The program creates five out
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272 B.2.3.4 Program to Generate Sym
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274 ylabel('frequency'); title('Ins
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276 The program will run and output
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278 axis([0 1 0 2]); grid on; xlabe
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280 vagal=sum(TFDs(HFC,1:k)); symto
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282 plot(J,symtopar); %plot(A,symto
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284 4. Remove the constant levels a
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286 Make sure the agreement is quit
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288 B.2.6 Principal Components Anal
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290 Columns 12 through 15 'LF_pcoh_
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292 I= 1.0000 -0.0000 -0.0000 -0.00
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294 variances = 3.4083 1.2140 1.141
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296 B.2.7 Cluster Analysis Program
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298 end [R,C]=size(Data); if length
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300 B.2.8 Cross-correlation Program
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302 C.3 Partial coherence of HR and
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304 [13] Madwed, J., and R. Cohen.
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306 [41] Mallat, S. G., "A Theory f
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[70] Tazebay, M.V., R.T. Saliba and
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