njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
219 Columns 8 through 13 -0.0114 0.0063 0.0139 0.0169 0.0216 0.0175 0.0182 0.0182 0.0181 0.0180 0.0177 0.0174 A= Columns 1 through 7 1.0000 -0.9960 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0 0.0041 0.0058 0.0058 0.0058 0.0058 0.0058 Columns 8 through 11 >> -0.0000 0.0000 0.0000 -0.0019 0.0058 0.0058 0.0058 0.0041 The information in the theta format is displayed by the present command present(th) and the results are shown above. This spells out the information in the calculated model th. Depending on the character of the underlying model structure, the information is given Estimated standard deviations for the parameters are always supplied. For multivariable ARX models and for state-space matrices, the standard deviations are given as imaginary numbers added to the parameters. For the polynomials they are given as a second row. For the case shown above, the polynomial coefficients and their standard
220 deviations are interpreted as A(q) =1 — 0.9960q -1 — 0.0019e 0 and the standard deviation of "1" is zero (naturally enough, since it is not estimated). The standard deviation of al is 0.0041, a 2 through a9 is 0.0058 and a10 is 0.0041. Note that leading zeros in the B(q) polynomial indicate the delay. In this case, Figure 5.56 shows the actual iibi output (derived HRV signal) (blue) and the estimated model simulated output (green). From this figure, it is cleared that the simple ARX model does not estimate the amplitude of the HRV output well. However, the period of the simulated output mimics the heart rate variability period very closely. The period of the iibi signal is the desired information needed to be obtained from the model and the amplitude needs not be matched exactly since the iibi of the ECG gives twice the HRV information. The estimated model provides the period variation information and this is all one can ask for out of the simple ARX model provided by the System Identification.
- Page 197 and 198: 168 Figure 5.9 (c) CWD plots of a n
- Page 199 and 200: Figure 5.10 CWT (Morlet) HRV plot o
- Page 201 and 202: 172 The following figures show the
- Page 203 and 204: 174 Figure 5.15 CWT (Mexican Hat) H
- Page 205 and 206: 176 5.2.5 Best Wavelet Selection fo
- Page 207 and 208: 178 Table 5.1 Correlation Indices o
- Page 209 and 210: 180 5.2.6 Vagal Tone and Sympathova
- Page 211 and 212: 182 These figures basically show th
- Page 213 and 214: 184 Figure 5.20 Sympathetic and par
- Page 215 and 216: 186 Figure 5.24 Sympathetic and par
- Page 217 and 218: 188 5.2.7 Time-Frequency Analysis (
- Page 219 and 220: 190 Figure 5.29 3D and contour plot
- Page 221 and 222: 192 Figure 5.33 3D and contour plot
- Page 223 and 224: 194 Figure 5.34 Sympathetic and par
- Page 225 and 226: 196 Figure 5.38 Sympathetic and par
- Page 227 and 228: 198 Figure 5.42 Sympathetic and par
- Page 229 and 230: Figure 5.44 Plot of raw respiration
- Page 231 and 232: Figure 5.46 The LF partial coherenc
- Page 233 and 234: Figure 5.48 HF partial coherence pl
- Page 235 and 236: Table 5.2 Cross-Spectral Analysis o
- Page 237 and 238: Table 5.3 Cross-Spectral Analysis o
- Page 239 and 240: Figure 5.50 HF coherence of COPD (1
- Page 241 and 242: 212 For better presentation of the
- Page 243 and 244: Figure 5.53 Coherence and partial c
- Page 245 and 246: 216 2. Interpretation of the transf
- Page 247: 218 covariances of the parameters,
- Page 251 and 252: 222 Figure 5.58 Bode plot of the HR
- Page 253 and 254: 224 In this section a simple model
- Page 255 and 256: 226 The data for all 47 COPD subjec
- Page 257 and 258: 228 Figure 5.60 Normal and COPD cla
- Page 259 and 260: 230 Figure 5.61 Normal and COPD cla
- Page 261 and 262: 232 Figure 5.62 Normal classificati
- Page 263 and 264: 234 5.7 Cluster Analysis The purpos
- Page 265 and 266: 236 Figure 5.64 Severity classifica
- Page 267 and 268: 238 both the normal and COPD subjec
- Page 269 and 270: 240 In summary, COPD subjects had h
- Page 271 and 272: APPENDIX A EXERCISE PHYSIOLOGY A.1
- Page 273 and 274: 244 A.3 Figure Out Your Target Hear
- Page 275 and 276: APPENDIX B ANALYSIS PROGRAM LISTING
- Page 277 and 278: 248 4) Click on file, close to exit
- Page 279 and 280: 250 • TN 11
- Page 281 and 282: 252 B.1.2 Partial Coherence Between
- Page 283 and 284: 254
- Page 285 and 286: 256 Block Diagram !rime of record K
- Page 287 and 288: 258
- 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
219<br />
Columns 8 through 13<br />
-0.0114 0.0063 0.0139 0.0169 0.0216 0.0175<br />
0.0182 0.0182 0.0181 0.0180 0.0177 0.0174<br />
A=<br />
Columns 1 through 7<br />
1.0000 -0.9960 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000<br />
0 0.0041 0.0058 0.0058 0.0058 0.0058 0.0058<br />
Columns 8 through 11<br />
>><br />
-0.0000 0.0000 0.0000 -0.0019<br />
0.0058 0.0058 0.0058 0.0041<br />
The information in the theta format is displayed by the present command<br />
present(th) and the results are shown above. This spells out the information in the<br />
calculated model th. Depending on the character <strong>of</strong> the underlying model structure, the<br />
information is given<br />
Estimated standard deviations for the parameters are always supplied. For<br />
multivariable ARX models and for state-space matrices, the standard deviations are given<br />
as imaginary numbers added to the parameters. For the polynomials they are given as a<br />
second row. For the case shown above, the polynomial coefficients and their standard
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