njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
181 This section looks at some applications of the wavelet time frequency distribution techniques. At the same time, comparisons of different wavelets are done to find the best wavelet for the sympathovagal case. The wavelet distributions were calculated using our wavelet time frequency distribution software. The program then analyzed the representative heart rate and blood pressure RBI signals of a COPD subject. The 3D, contour plots and the activity plots were obtained for the entire file by calculating the areas under the LF, HF ranges for each instant of time. The sympathovagal balance (the ratio of the LF to the HF range, LF:HF) was also obtained. As mentioned earlier drug studies indicate that the LF range is a mixture of sympathetic and parasympathetic division activity. The ratio is an indication of both reciprocity and non-reciprocity [50]. Figures 5.18 — 5.27 display the 5-minute activity plots of a COP subject at rest breathing at 16 breaths per minute using the Morlet, Meyer, Daubechies 4, Mexican Hat and Haar wavelet. The even figures (5.18, 5.20, 5.22, 5.24 and 5.26) present the low frequency, the high frequency and the ratio of low frequency to high frequency as obtained by summing the areas under the curves in the LF and HF bands at each instant of time throughout the whole experiment period. The odd figures (5.19, 5.21, 5.23, 5.25 and 5.27) present the normalized low frequency, the normalized high frequency and the normalized ratio of the low frequency to high frequency. The normalized LF and HF activity plots are obtained by dividing at each instant of time the LF and HF power by the sum of both as follows:
182 These figures basically show that at rest, the trend of the relative power is higher in the high frequency range than in the low frequency range. In addition, the oscillation in the HF range is more than double the oscillation in the LF range. Fast oscillation in the HF range indicates that the parasympathetic activity of the COPD subject had to make faster and more regular adjustments to sustain the resting state and at the same time to compensate for the higher than normal breathing rate that contributes to more power in the HF range. The ratio of the LF to HF emphasizes the dominance of the LF activity during rest even though there is a regular compensation in the HF activity due to higher respiration rate. Figures 5.18 — 5.27 replicate the same findings using the Morlet, Meyer, dB4, Mexican Hat and Haar wavelet. The differences between these wavelets relate back to the properties of the mother wavelet. The Mexican Hat and the Haar wavelets still present the cross-term interference problem. The dB4 is the higher order Haar wavelet that improves the cross-term a little. However, the clarity of presentation and the smoothest display of Morlet wavelet have demonstrated its use as a suitable HRV timefrequency analysis method.
- Page 159 and 160: 130 The patients who underwent LVRS
- Page 161 and 162: 132 panel of the Correct.vi. It was
- Page 163 and 164: 134 4.2.3 Power Spectrum Analysis o
- Page 165 and 166: 136 weighted-average value of the c
- 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: 180 5.2.6 Vagal Tone and Sympathova
- 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
181<br />
This section looks at some applications <strong>of</strong> the wavelet time frequency distribution<br />
techniques. At the same time, comparisons <strong>of</strong> different wavelets are done to find the best<br />
wavelet for the sympathovagal case.<br />
The wavelet distributions were calculated using our wavelet time frequency<br />
distribution s<strong>of</strong>tware. The program then analyzed the representative heart rate and blood<br />
pressure RBI signals <strong>of</strong> a COPD subject. The 3D, contour plots and the activity plots<br />
were obtained for the entire file by calculating the areas under the LF, HF ranges for each<br />
instant <strong>of</strong> time. The sympathovagal balance (the ratio <strong>of</strong> the LF to the HF range, LF:HF)<br />
was also obtained. As mentioned earlier drug studies indicate that the LF range is a<br />
mixture <strong>of</strong> sympathetic and parasympathetic division activity. The ratio is an indication<br />
<strong>of</strong> both reciprocity and non-reciprocity [50].<br />
Figures 5.18 — 5.27 display the 5-minute activity plots <strong>of</strong> a COP subject at rest<br />
breathing at 16 breaths per minute using the Morlet, Meyer, Daubechies 4, Mexican Hat<br />
and Haar wavelet. The even figures (5.18, 5.20, 5.22, 5.24 and 5.26) present the low<br />
frequency, the high frequency and the ratio <strong>of</strong> low frequency to high frequency as<br />
obtained by summing the areas under the curves in the LF and HF bands at each instant <strong>of</strong><br />
time throughout the whole experiment period. The odd figures (5.19, 5.21, 5.23, 5.25<br />
and 5.27) present the normalized low frequency, the normalized high frequency and the<br />
normalized ratio <strong>of</strong> the low frequency to high frequency. The normalized LF and HF<br />
activity plots are obtained by dividing at each instant <strong>of</strong> time the LF and HF power by the<br />
sum <strong>of</strong> both as follows: