njit-etd2003-081 - New Jersey Institute of Technology

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LIST OF FIGURES (Continued) Figure Page 3.12 Power spectrum of BP 11131 86 3.13 Examples of typical plant 95 3.14 Optimal data acquisition measurement system 98 3.15 Open-loop input driven ARX model 103 3.16 Feedback closed-loop ARX model 105 3.17 Principal component analysis illustration 110 3.18 Horizontal hierarchical tree plot 114 3.19 Contour graph of a two-way joining clustering 121 4.1 Theoretical power spectrum of band-limited Gaussian white noise 143 5.1 HRV analysis of a COPD subject 151 5.2 BPV analysis of a COPD subject 152 5.3 HRV analysis of a normal subject 154 5.4 BPV analysis of a normal subject 155 5.4.1 Comparison of the HRV frequency spectra of COPD and normal subjects 156 5.4.2 Comparison of the BPV frequency spectra of COPD and normal subjects 157 5.5 Test signal with 3 sine waves and its power spectrum 160 5.6a STFT of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 161 5.6b SWVD of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 161 5.6c CWD of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 162 5.6d BJCD of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 162 xviii
LIST OF FIGURES (Continued) Figure Page 5.7a Morlet WT of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 163 5.7b Meyer WT of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 163 5.7c dB4 WT of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 164 5.7d Mexican Hat WT of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 164 5.7e Haar WT of a signal with 3 sine waves at 0.3, 0.1 and 0.5 Hz 165 5.8 HR RBI and power spectrum plots of a normal subject at 3 min rest paced breathing at 8 bpm 166 5.9a STFT plots of a normal subject at 3 min rest, paced breathing at 8 bpm 167 5.9b SWVD plots of a normal subject at 3 min rest, paced breathing at 8 bpm 167 5.9c CWD plots of a normal subject at 3 min rest, paced breathing at 8 bpm 168 5.9d BJCD plots of a normal subject at 3 min rest, paced breathing at 8 bpm 168 5.10 Morlet WT HRV plots of a normal subject at 3 min rest, paced breathing at 8 bpm 170 5.11 HR HBI and power spectrum of a COPD subject at rest breathing at 16 bpm 171 5.12 Morlet WT of a COPD subject at 5 min rest paced breathing at 16 bpm 172 5.13 Meyer WT of a COPD subject at 5 min rest paced breathing at 16 bpm 173 5.14 CWT (dB4) HRV plot of a COPD subject at 5 min rest paced breathing at 16 bpm 173 5.15 CWT (Mexican Hat) HRV plot of a COPD subject at 5 min rest paced breathing at 16 bpm 174 5.16 CWT (Haar) HRV plot of a COPD subject at 5 min rest paced breathing at 16 bpm 174 xix
Page 1 and 2: Copyright Warning & Restrictions Th
Page 3 and 4: ABSTRACT TIME-FREQUENCY INVESTIGATI
Page 5 and 6: TIME-FREQUENCY INVESTIGATION OF HEA
Page 7 and 8: APPROVAL PAGE TIME-FREQUENCY INVEST
Page 9 and 10: BIOGRAPHICAL SKETCH (Continued) D.A
Page 11 and 12: ACKNOWLEDGMENT The author wishes to
Page 13 and 14: TABLE OF CONTENTS Chapter Page 1 IN
Page 15 and 16: TABLE OF CONTENTS (Continued) Chapt
Page 21: LIST OF FIGURES Figure Page 2.1 The
Page 25 and 26: LIST OF FIGURES (Continued) Figure
Page 27 and 28: ABBREVIATIONS A ABP Arterial Blood
Page 29 and 30: ABBREVIATIONS (Continued) P PID Pro
Page 31 and 32: 2 mortality and sudden death. [2] B
Page 33 and 34: 4 1) Patients with severe pulmonary
Page 35 and 36: 6 examined for determining the inte
Page 37 and 38: 8 identified using principal compon
Page 39 and 40: 1 0 1. Present the application of t
Page 41 and 42: 12 1.3 Outline of the Dissertation
Page 43 and 44: CHAPTER 2 PHYSIOLOGY BACKGROUND Bio
Page 45 and 46: 16 Figure 2.2 The systemic and pulm
Page 47 and 48: 18 illustrated in Figure 2.3. The i
Page 49 and 50: 20 2.2 Blood Pressure The force tha
Page 51 and 52: 22 2.4 The Nervous System Human beh
Page 53 and 54: 24 The sympathetic nerve fibers lea
Page 55 and 56: 26 Without these sympathetic and pa
Page 57 and 58: 28 center in the medulla, which con
Page 59 and 60: 30 Figure 2.6 Autonomic innervation
Page 61 and 62: 32 average heart rate was measured
Page 63 and 64: 34 However, they do note that there
Page 65 and 66: Figure 2.9 The placement of the pos
Page 67 and 68: 38 female. While more men suffer fr
Page 69 and 70: 40 Stage II: Moderate COPD - Worsen
Page 71 and 72: 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
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90 when there is significant correl
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92 3.12 Partial Coherence Analysis
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94 after removal of the effects of
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96 The bulk of the theory and appli
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98 technique is measurement time. T
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100 usually attainable. The key poi
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102 variability exists in the propa
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104 eXogenous input (ARX) was used
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106 The baroreflex, an autonomic re
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108 the principal components are no
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110 The mathematical solution for t
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112 3.15 Cluster Analysis The term
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114 formed) one can read off the cr
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116 3.15.5 Squared Euclidian Distan
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118 Alternatively, one may use the
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120 Sneath and Sokal used the abbre
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122 may seem a bit confusing at fir
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CHAPTER 4 METHODS The purpose of th
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126 4.1.2.1 Autonomic Testing. HR V
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128 of heart rate, blood pressure,
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130 The patients who underwent LVRS
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132 panel of the Correct.vi. It was
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134 4.2.3 Power Spectrum Analysis o
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136 weighted-average value of the c
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138 For each given scale a within t
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140 frequency F to the wavelet func
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142 4.2.8 System Identification Ana
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144 In this study a simpler approac
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146 Table 4.2 Parameters That Make
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148 4.2.11 Cluster Analysis The sam
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150 viewing the time series of sequ
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Figure 5.2 BPV analysis of a COPD s
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Figure 5.3 HRV analysis of a normal
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Figure 5.4.1 Comparison of the HRV
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158 5.2 Time Frequency Analysis One
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Figure 5.5 Test signal with 3 sine
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162 Figure 5.6 (c) CWD of a signal
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164 Figure 5.7 (c) WT (dB4 wavelet)
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166 HRV more information about HRV
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168 Figure 5.9 (c) CWD plots of a n
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Figure 5.10 CWT (Morlet) HRV plot o
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172 The following figures show the
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174 Figure 5.15 CWT (Mexican Hat) H
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176 5.2.5 Best Wavelet Selection fo
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178 Table 5.1 Correlation Indices o
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180 5.2.6 Vagal Tone and Sympathova
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182 These figures basically show th
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184 Figure 5.20 Sympathetic and par
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188 5.2.7 Time-Frequency Analysis (
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190 Figure 5.29 3D and contour plot
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192 Figure 5.33 3D and contour plot
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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
Page 337:
[70] Tazebay, M.V., R.T. Saliba and
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