njit-etd2003-081 - New Jersey Institute of Technology

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29 2.5 Heart Rate and Heart Rate Variability 2.5.1 Physiology of Changes in Heart Rate Change in heart rate is sensitive to changes in body temperature, plasma electrolyte concentrations and hormone concentrations [5]. However, the most important influence of beat-to-beat variations of heart rate comes from the autonomic nervous system. More specifically, sympathetic activity increases heart rate, whereas activity in the parasympathetic (vagus) nerves causes the heart rate to decrease. Due to considerably more parasympathetic activity to the heart than sympathetic activity in the resting state, the normal resting heart rate is below the inherent rate of 100 beats/minute. The autonomic nervous system innervates the heart in a number of places. The sympathetic nervous system terminates at the SA node, the conduction system, atrial and ventricular myocardium, and coronary vessels. The parasympathetic fibers terminate in the SA and AV nodes, atrial and ventricular musculature, and coronary vessels. Interplay between the two systems will cause the heart to speed up or slow down, depending on which system is more active. Figure 2.6 illustrates the autonomic innervation of the heart [5]. Perhaps the most important site of innervation of the autonomic nervous system on the heart occurs at the SA node. As matter of fact, the SA node possesses an inherent discharge rate, often referred to as the pacemaker potential. The pacemaker potential is a slow depolarization of the cells of the SA node. The innervation of the sympathetic and parasympathetic nervous system on the SA node changes the characteristics of depolarization within the SA node cells, thus changing heart rate. Figure 2.7 illustrates these changes due to autonomic innervation.
30 Figure 2.6 Autonomic innervation of the heart. (From G. J Tortora and N. P Anagnostakos, "Principles of Anatomy and Physiology," Harper and Row, Publishers, New York, 1990.) For comparative purposes, the pacemaker potential labeled "Control" is the normal. From the figure, one can observe that sympathetic stimulation increases the slope of the pacemaker potential. As a result, the SA node cells reach the threshold more rapidly, thus increasing the heart rate. Conversely, parasympathetic stimulation decreases the slope of the pacemaker potential. Consequently, the SA node cells reach the threshold more slowly, and heart rate decreases. In addition to decreasing the slope of the pacemaker potential, parasympathetic stimulation also hyperpolarizes the plasma membrane of the SA node cells so that the pacemaker potential starts from a more negative membrane potential. As a result, the time it takes the SA node cells to reach the threshold increases, which decreases heart rate.
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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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Page 11 and 12: ACKNOWLEDGMENT The author wishes to
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Page 21 and 22: LIST OF FIGURES Figure Page 2.1 The
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Page 27 and 28: ABBREVIATIONS A ABP Arterial Blood
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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
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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
Page 83 and 84: where H(f), S(f) are Fourier transf
Page 85 and 86: 56 Another shortcoming of the spect
Page 87 and 88: 58 should take the kernel of the WD
Page 89 and 90: 60 called the cross Wigner distribu
Page 91 and 92: 62 3.6.3 The Choi-Williams (Exponen
Page 93 and 94: 64 Figure 3.3 Performance of the Ch
Page 95 and 96: 66 [-Ω,Ω ], then its STFT will be
Page 97 and 98: 68 This condition forces that the w
Page 99 and 100: 70 where c is a constant. Thus, the
Page 101 and 102: Figure 3.5 The time-frequency plane
Page 103 and 104: 74 The measure dadb used in the tra
Page 105 and 106: 76 and the wavelet transform repres
Page 107 and 108: 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
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[70] Tazebay, M.V., R.T. Saliba and
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