njit-etd2003-081 - New Jersey Institute of Technology

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77 3.8 Power Spectral Analysis of Heart Rate Variability Power spectral analysis of heart rate variability is a potentially powerful tool for evaluating the activity of the autonomic nervous system noninvasively. Power spectral analysis is a technique which transforms a signal from the time domain to the frequency domain. The time domain signal used for computing the heart rate variability power spectrum is known as the interbeat interval (IBI). The following is a brief description of how to obtain the MI signal. To begin the analysis, the ECG is converted from analog to digital form and stored in ASCII format on the data acquisition computer running in LabVIEW. The data is then transferred over to a signal-processing computer. Another LabVIEW program is used to obtain the HRV power spectrum. The following is a description of the necessary steps to obtain the power spectrum of HRV. The first step to obtain the power spectrum of HRV is to detect every R-wave in the ECG. Because the R-wave complex is pronounced in the ECG, the LabVIEW detection program can easily detect these R-waves. Before trying to detect the R-waves, the ECG signals are detrended using a locally weighted robust regression algorithm built in LabVIEW. To help detect each R- wave, a vertical threshold (which the R-wave must exceed) and a horizontal threshold (to prevent detecting an R-wave for a period of time after one was detected) can be varied. If an error occurs during the R-wave detection, the analyst can use existing software to manually detect or undetect the incorrectly detected R-waves.
78 Figure 3.6 Figure depicting the construction of the MI signal. (From S.J. Shin, W.N. Tapp, S.S. Reisman, and B.H. Natelson, "Assessment of autonomic regulation of heart rate variability by the method of complex demodulation," 1989.) Once the R-waves are properly detected, an interbeat interval (IBI) signal can be constructed. To obtain the IBI signal, the distance in time between a specific beat ( T„) and the beat previous in time (Tm-1) is calculated. This value of time difference then becomes the amplitude of the MI signal at that specific beat. Mathematically, the IBI signal is computed by the formula IBIm = Tm — Tm-1. See Figure 3.6 for a graphical representation. Although the MI represents the heart period at discrete points, the IBI signal is not suitable for FFT analysis because the discrete points, located at each R-wave, are not evenly spaced. In order to produce equidistant IBI samples suitable for analysis, the IBI signal must be interpolated. The interpolation method used was that of a backward step function. This method assumes no new information about the direction of the time
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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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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
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: 76 and the wavelet transform repres
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 and 132: 102 variability exists in the propa
Page 133 and 134: 104 eXogenous input (ARX) was used
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
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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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