njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
189 The purpose of this is to show that, although the output for different wavelets varies, the fundamental characteristics of the wavelet transform are the same. Figure 5.29 — 5.33 show the results of these transforms. A band of cones of influence corresponding to each minima and maxima on the BP IIBI signal at the frequency of respiration (16 bpm, 0.266667 Hz) appears in each wavelet transform representation. In addition, the power intensity of the band is as high as the similar band shown in the normal subject case investigated in section 5.2.2. Another band of cones of influence in the very low frequency range (0.01 — 0.15 Hz) also exists with high energy and in longer time (from 10 seconds to 120 seconds, to 180 seconds, to 250 seconds). This matches the trend of the blood pressure IIBI signal that can be seen in figure 5.28 (top panel, signal in green). Among the wavelets, the Morlet wavelet again provides the best representation in terms of time, frequency resolution and meaningful details. The band of the cones of influence at 0.266667 Hz modulated slightly indicating that the BPV is also dominated by the respiration signal and is also not a stationary signal. There are also many interesting observations unveiled from the data and the experimental setup of the normal and COPD subjects. The first was the interplay of the two opposing physiological factors that were simultaneously revealed in the timefrequency analysis of the heart rate variability signal during stressful exercise conditions for normal subjects and during rest for COPD patients. Another was the compensation of the HR in COPD by having a cyclic modulation at about 4 to 5 minutes while normal subjects took longer to have these HR modulations and in a more random nature.
190 Figure 5.29 3D and contour plot of blood pressure spectrum using Morlet wavelet. Figure 5.30 3D and contour plot of blood pressure spectrum using Meyer wavelet.
- Page 167 and 168: 138 For each given scale a within t
- Page 169 and 170: 140 frequency F to the wavelet func
- Page 171 and 172: 142 4.2.8 System Identification Ana
- Page 173 and 174: 144 In this study a simpler approac
- Page 175 and 176: 146 Table 4.2 Parameters That Make
- Page 177 and 178: 148 4.2.11 Cluster Analysis The sam
- Page 179 and 180: 150 viewing the time series of sequ
- Page 181 and 182: Figure 5.2 BPV analysis of a COPD s
- Page 183 and 184: Figure 5.3 HRV analysis of a normal
- Page 185 and 186: Figure 5.4.1 Comparison of the HRV
- Page 187 and 188: 158 5.2 Time Frequency Analysis One
- Page 189 and 190: Figure 5.5 Test signal with 3 sine
- Page 191 and 192: 162 Figure 5.6 (c) CWD of a signal
- Page 193 and 194: 164 Figure 5.7 (c) WT (dB4 wavelet)
- Page 195 and 196: 166 HRV more information about HRV
- 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: 188 5.2.7 Time-Frequency Analysis (
- 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 and 268: 238 both the normal and COPD subjec
190<br />
Figure 5.29 3D and contour plot <strong>of</strong> blood pressure spectrum using Morlet wavelet.<br />
Figure 5.30 3D and contour plot <strong>of</strong> blood pressure spectrum using Meyer wavelet.