njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
235 attempt by adjusting the threshold of the correlation coefficients would provide a better and more defined group of clusters. However, this would defeat the purpose of blind separation and classification. The COPD assignment of the 47 COPD and 8 normal subjects are presented in Table 5.10. When compared to the clinical classification using FEV1 over FVC data the PCA-CA classification technique produces very high accuracy results. It 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. Figure 5.63 Separation of normal (Red) from COPD subjects (Blue/Green).
236 Figure 5.64 Severity classification of normal and COPD subjects. (Normal: blue circle, At Risk: green star, Mild: cyan pentagon, Moderate: magenta square and Severe: red diamond). Table 5.10 Summary of Normal-COPD Severity Classification Stage Clinical PCA-CA % Accuracy Severe 4 4 100.00 Moderate 17 15 88.23 Mild 18 20 90.00 At Risk 8 8 100.00 Normal 8 8 100.00
- 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 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: 234 5.7 Cluster Analysis The purpos
- 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
- 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
235<br />
attempt by adjusting the threshold <strong>of</strong> the correlation coefficients would provide a better<br />
and more defined group <strong>of</strong> clusters. However, this would defeat the purpose <strong>of</strong> blind<br />
separation and classification.<br />
The COPD assignment <strong>of</strong> the 47 COPD and 8 normal subjects are<br />
presented in Table 5.10. When compared to the clinical classification using FEV1 over<br />
FVC data the PCA-CA classification technique produces very high accuracy results. It<br />
has been shown to separate the COPD from the normal population with 100% accuracy.<br />
It can also classify the COPD population into "at risk", "mild", "moderate" and "severe"<br />
stages with 100%, 90%, 88% and 100% accuracy respectively. As a result, cluster and<br />
principal component analysis can be used to separate COPD and normal subjects and can<br />
be used successfully in COPD severity classification.<br />
Figure 5.63 Separation <strong>of</strong> normal (Red) from COPD subjects (Blue/Green).
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