njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
239 providing higher resolution in time and frequency while suppressing interferences between the signal components. The respiration analysis validated the physiological states the COPD subjects were in. With shortness of breath, the breathing rate has to increase to supply the body with more oxygen and remove more carbon dioxide, thus shifting the frequency of respiration into higher ranges that could reach as high as 0.55 Hz (33 bpm). The HRV analysis of normal subjects showed that with rest the average heart rate slows down while the variability in the heart rate increases. On the other hand, with exercise, the average heart rate speeds up while the variability in the heart rate decreases. Both the time-frequency and statistical analysis of the HRV signals showed that a stressful exercise level drastically changed the HRV signal and that there was a significant difference between the three states of rest, exercise, and recovery. In the second phase of this research, the cross spectral analyses (i.e. coherence, weighted coherence and partial coherence) were used to investigate the increase or decrease of the HRV, BPV or any of the combinations of HRV, BPV and respiration that changed the behavior in the autonomic nervous system. Examination of the results for the 47 COPD and 8 normal subjects presented some real challenge because the peaks of the ECG R-waves, blood pressure peaks and even respiration signals of COPD patients were difficult to detect and analyze. However, the interrelationships between heart rate and respiration, heart rate and blood pressure, and blood pressure and respiration could be quantified and helped in understanding the various coupling effects between various systems inside the body.
240 In summary, COPD subjects had higher respiratory rate, heart rate and blood pressure while HRV and BPV were always lower than those of normal subjects. From examining the transfer function and frequency response of the cardiovascular system ARX models from both normal and COPD subjects, COPD models showed a similar DC gain response (slightly less). However, there was a significant lag in phase (at —180 °) for COPD models as compare to those of normal subjects. In the last phase, a new methodology was proposed that used principal component analysis and cluster analysis to identify diseased subjects from a normal population. This method combines the techniques of principal component analysis (PCA) and cluster analysis (CA) and has been shown to separate the COPD from the normal population with 100% accuracy. It can also classify the COPD population into "at risk", "mild", "moderate" and "severe" stages with 100%, 90%, 88% and 100% accuracy respectively. As a result, cluster and principal component analysis can be used to separate COPD and normal subjects and can be used successfully in COPD severity classification. In conclusion, wavelets as time-frequency representation provide great visual benefit in analyzing HRV, BPV and other biological signals. Using cross-spectral techniques such as coherence, partial coherence, and modeling transfer function analysis also provided important insights to understanding the behavior of complex physiological systems. Statistical techniques such as PCA and CA could be used as accurate tools of severity classification and could be a great help in diagnosing different disease states noninvasively.
- 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 and 248: 218 covariances of the parameters,
- Page 249 and 250: 220 deviations are interpreted as A
- 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: 238 both the normal and COPD subjec
- 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
- 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
240<br />
In summary, COPD subjects had higher respiratory rate, heart rate and blood<br />
pressure while HRV and BPV were always lower than those <strong>of</strong> normal subjects.<br />
From examining the transfer function and frequency response <strong>of</strong> the cardiovascular<br />
system ARX models from both normal and COPD subjects, COPD models showed a<br />
similar DC gain response (slightly less). However, there was a significant lag in phase (at<br />
—180 °) for COPD models as compare to those <strong>of</strong> normal subjects.<br />
In the last phase, a new methodology was proposed that used principal component<br />
analysis and cluster analysis to identify diseased subjects from a normal population. This<br />
method combines the techniques <strong>of</strong> principal component analysis (PCA) and cluster<br />
analysis (CA) and has been shown to separate the COPD from the normal population with<br />
100% accuracy. It can also classify the COPD population into "at risk", "mild",<br />
"moderate" and "severe" stages with 100%, 90%, 88% and 100% accuracy respectively.<br />
As a result, cluster and principal component analysis can be used to separate COPD and<br />
normal subjects and can be used successfully in COPD severity classification.<br />
In conclusion, wavelets as time-frequency representation provide great visual<br />
benefit in analyzing HRV, BPV and other biological signals. Using cross-spectral<br />
techniques such as coherence, partial coherence, and modeling transfer function analysis<br />
also provided important insights to understanding the behavior <strong>of</strong> complex physiological<br />
systems. Statistical techniques such as PCA and CA could be used as accurate tools <strong>of</strong><br />
severity classification and could be a great help in diagnosing different disease states noninvasively.
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