njit-etd2003-081 - New Jersey Institute of Technology

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103 3.13.2.1 Open-loop Input Driven ARX Model. The Open-loop Input Driven ARX Model is shown in Figure 3.15. The inputs to the model are [54]: Respiration Vt(n). Systolic blood pressure, SBP(n) The output of the model is: Heart Rate, RR(n). Figure 3.15 Open-loop input driven ARX model. This figure shows an open-loop model-based approach that enables the dynamic effects of respiration and arterial systolic blood pressure on heart rate to be estimated in both COPD and normal subjects. This method is based on a two-compartment model of respiratory sinus arrhythmia (RSA), which includes a vagally mediated central component (h_rsa), and an arterial baroreflex (h_abr) component driven by both vagal and sympathetic systems. A parametric linear AutoRegressive representation with
104 eXogenous input (ARX) was used to estimate the impulse responses (IR) and transfer function (TF) of the two couplings in the model. A number of indexes derived from the IR's (peak magnitude, time of peak, delays) and TF's (gains and phases in the frequency bands of interest) were used for subsequence statistical analysis and the assessment of autonomic function. The two-compartment model of RSA in the Figure is described as an ARX representation by the equation. The coefficient of each polynomials (Bo A) were estimated from the data by least square minimization of the prediction error W The orders and the delays of the model were chosen by finding the model that minimized the cross-correlation of each input and the prediction error, as well as the Minimum Description Length (MDL) 3.13.2.2 Feedback Closed -loop ARX Model. In this section a more realistic physiological model involving baroreflex and feedback loop. The depolarization, and therefore contraction, of the ventricles is associated with the ejection of blood into the aorta during systole. If one made the assumption that the volume of ejected blood is roughly the same for each contraction, then the arterial pressure will only be a function of the timing of the contractions. If several contractions occur close together, arterial pressure builds, causing simultaneous stimulation and inhibition of the parasympathetic and sympathetic branches of the ANS, respectively, resulting in a decrease in the rate of depolarization of the SA nodal tissue. A cascade of contractions that occur further apart
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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
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
Page 109 and 110: 80 The final step to obtain the pow
Page 111 and 112: 82 It should be noted that if the w
Page 113 and 114: 84 The normal respiration rate can
Page 115 and 116: Figure 3.12 Power spectrum of BP II
Page 117 and 118: 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
Page 129 and 130: 100 usually attainable. The key poi
Page 131: 102 variability exists in the propa
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 and 170: 140 frequency F to the wavelet func
Page 171 and 172: 142 4.2.8 System Identification Ana
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
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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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