njit-etd2003-081 - New Jersey Institute of Technology

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307 [55] Haar, A., "Zur theorie der orthoganalen funktionensysteme," Math. Annal., 69, 331- 371, 1910 [56] Hotelling, H., "Analysis of a Complex of Statistical variables into Principal Components," Journal of Educational Psychiatry, 24:417-441, 1933. [57] http://fonsg3.let.uva.nl/praat/manual/Principal component analysis.html, 11/2001 [58] Taylor, A., "Recognizing Biological Sounds Using Machine Learning," URL. http://www.cse.unsw.edu.au/—andrewt/papers/warbler ai95/wabler ai95.html, 11/2001. [59] www.statsoft.com/textbook/stmulsca.html, 11/2001 [60] www.statsoft.com/textbook/stcoran.html, 11/2001 [61] Brillinger, DR, "Identification of point process systems". Ann Probability 3: 909- 929, 1975. [62] Rosenberg, JR; Amjad, AM; Breeze, P; Brillinger, DR; Halliday, DM, "The Fourier approach to the identification of functional coupling between neuronal spike trains". Prog Biophys molec Biol, 53: 1-31, 1989. [63] Wiener, N., "Generalized harmonic analysis". Acta Math, 55: 117-258, 1930. [64] Halliday DM; Rosenberg JR; Amjad AM; Breeze P; Conway BA; Farmer SF; "A framework for the analysis of mixed time series/point process data — Theory and application to the study of physiological tremor, single motor unit discharges and electromyograms". Prog Biophys molec Biol, 64: 237-278, 1995. [65] Bloomfield, P; "Fourier Analysis of Time Series: An Introduction". Wiley, New York, 1976. [66] Brillinger, DR; "Time Series — Data Analysis and Theory", 2nd ed., Holden Day, San Francisco, 1981. [67] Jenkins, GM; Watts, DG; "Spectral analysis and its applications". Holden-Day, 1968. [68] Hlawatsch, F., and G.F. Boudreaux-Bartels, "Linear and Quadratic Time-Frequency Signal Representations," IEEE Signal Processing Magazine, pp. 21-67, April 1992. [69] Saliba, R., "Bilinear Time-Frequency Representations of Heart Rate Variability and Respiration During Stress," NJIT, PhD Dissertation, January 1997.
[70] Tazebay, M.V., R.T. Saliba and S.S. Reisman, "Adaptive Time-frequency Analysis of Autonomic Nervous System," Proceedings of the 17th IEEE Annual International Conference of the Engineering in Medicine and Biology Society, Montreal, Canada, 1995. 308
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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
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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
Page 285 and 286: 256 Block Diagram !rime of record K
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Page 289 and 290: 260 B.2.2 Time — Frequency Analys
Page 291 and 292: 262 This program provides the STFT
Page 293 and 294: 264 G(:j+1)=G(:,j+1)/(2*sum(G(:j+1)
Page 295 and 296: 266 T=(length(Signa)/sample)/(Times
Page 297 and 298: 268 subplot(3, 1,3), plot(T,E); xla
Page 299 and 300: 270 4. The program creates five out
Page 301 and 302: 272 B.2.3.4 Program to Generate Sym
Page 303 and 304: 274 ylabel('frequency'); title('Ins
Page 305 and 306: 276 The program will run and output
Page 307 and 308: 278 axis([0 1 0 2]); grid on; xlabe
Page 309 and 310: 280 vagal=sum(TFDs(HFC,1:k)); symto
Page 311 and 312: 282 plot(J,symtopar); %plot(A,symto
Page 313 and 314: 284 4. Remove the constant levels a
Page 315 and 316: 286 Make sure the agreement is quit
Page 317 and 318: 288 B.2.6 Principal Components Anal
Page 319 and 320: 290 Columns 12 through 15 'LF_pcoh_
Page 321 and 322: 292 I= 1.0000 -0.0000 -0.0000 -0.00
Page 323 and 324: 294 variances = 3.4083 1.2140 1.141
Page 325 and 326: 296 B.2.7 Cluster Analysis Program
Page 327 and 328: 298 end [R,C]=size(Data); if length
Page 329 and 330: 300 B.2.8 Cross-correlation Program
Page 331 and 332: 302 C.3 Partial coherence of HR and
Page 333 and 334: 304 [13] Madwed, J., and R. Cohen.
Page 335: 306 [41] Mallat, S. G., "A Theory f
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