njit-etd2003-081 - New Jersey Institute of Technology

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3 pulmonary disease (COPD) is an excellent candidate to now be studied. Patients with pulmonary disease also have significant alterations of their autonomic regulation. In COPD patients breathing patterns are altered, causing abnormalities in the entrainment of the normal central oscillators. A recent study by Pagani, et al [3] demonstrated that there is a decrease in the high frequency (parasympathetic) activity detected in patients with COPD compared to age matched controls, and a decrease in low frequency (sympathetic) activity. This work also demonstrated that the RSA vagal index, which provides an assessment of the gain of respiratory modulation of RR variability, is altered in patients with COPD. Unfortunately, there has not been much focused work studying the relationship of the pulmonary functions and alterations in autonomic regulation as monitored by HRV, BPV, or BRS and linking these parameters to morbidity and mortality. Since there are several physiological correlations between the populations of patients with COPD and congestive heart failure (CHF), there is a good reason to believe that some of the findings in the CHF population may also extend to patients with COPD. With the correlations that are possible to see from the previous work that has been done in cardiac disease and heart failure, it should be possible to demonstrate that these autonomic tests are clear predictors of morbidity and mortality, and that improvements in the parameters that one measures, correlate with improved outcomes. The first part of this study has been designed to answer questions about interactions between the nervous system, the cardiac and the pulmonary system; and issues concerning heart rate variability activity. The study consists of several sections that were performed on different days on the population of:
4 1) Patients with severe pulmonary disease that are being enrolled into the National Emphysema Treatment Trial (NETT) to study lung volume reduction surgery (LVRS) as well as to the elective population undergoing lung volume reduction surgery. 2) Normal individuals with no known cardiac or pulmonary disease. Our hypotheses are as follows: In COPD patients: 1) The most severely diseased patients will have the lowest HRV, BPV and decreased BRS, corresponding to decreased survival and higher morbidity. 2) The results of the autonomic testing of the patients will improve after pulmonary rehabilitation. 3) Regardless of the treatment arm of the study, the patients that improve the results of their autonomic testing will have better survival and decreased morbidity. In normal individuals: 1) HRV, BPV and BRS are higher in normal individuals than in COPD patients. 2) The results of the autonomic testing are better for normal individuals than COPD patients. The application of the data analysis in the time and frequency domain is continued in studying a regulatory feedback loop, called the baroreflex mechanism, which exits in the cardiovascular system in which autonomic nervous system activity is altered in response to changes in blood pressure. The results from this part of the research will allow one to discuss the influence of respiration on heart rate variability in response to different means of paced-breathing maneuvers (by COPD patients) such as
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 and 22: LIST OF FIGURES Figure Page 2.1 The
Page 23 and 24: LIST OF FIGURES (Continued) Figure
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: 2 mortality and sudden death. [2] B
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
Page 73 and 74: 44 Two common types of time-frequen
Page 75 and 76: 46 STFT: Short-Time Fourier Transfo
Page 77 and 78: 48 3.3 The Analytic Signal and Inst
Page 79 and 80: 50 The advantage of using equation
Page 81 and 82: 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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