njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
131 4.2 Data Analysis All signal processing techniques were performed with the LabVIEW graphical programming language and MATLAB. This section describes the use of both LabVIEW and MATLAB and the algorithms by which the different physiological signals were analyzed. All the programs for spectral analysis, the Wigner timefrequency analysis program (Sympar.vi), coherence, weighted coherence and partial coherence were written in LabVIEW. The time-frequency analysis (Cohen's class and wavelets), system identification techniques, principal component analysis and cluster analysis were performed using the MATLAB System Identification and Statistics toolboxes. 4.2.1 Wigner Time-Frequency Analysis As mentioned previously, the Wigner time-frequency analysis program (Sympar.vi) was written in LabVIEW. The time-frequency analysis was performed using raw ECG data. The ECG signal was read from a spreadsheet file acquired together with other signals such as respiration, blood pressure and Oxygen volume consumption. All of these signals were represented in spreadsheet form as columns of space delimited data. The ECG data was filtered through a high-pass filter (HP_FILT.vi) after which point the R wave locations were detected automatically (Search BP.vi). The array of R wave locations was then used to calculate the heart rate and to generate graphs of both the ECG signal with detected R waves and of the heart rate, respectively. The raw ECG signal was then displayed with markers at each corresponding detected R wave along with the newly calculated corresponding heart rate. These graphs appear on the control
132 panel of the Correct.vi. It was in the Correct.vi block that a cursor was provided allowing the user to manually detect missing peaks or undetect "bad" peaks by turning the "Modify" marker on or off. Missing peaks were results of saturation, motion, or noise artifact during acquisition of the ECG signal. Consequently, peaks must be manually interpolated in place of the missing data. "Bad" peaks consist of peaks that were not characteristically R waves, such as those generated by noise artifact. Manual detection of R waves continued until the user clicks the "Done" button on the control panel of the Correct.vi, indicating to the program that all detection of R waves was complete. The output of the Correct.vi consists of the 1131 as calculated from consecutive R waves detected in the ECG, which was then saved to a spreadsheet file to be utilized for further analysis. The IBI was interpolated and decimated (Interpolation & Decimation.vi) to generate a decimated BIM waveform which was analyzed utilizing the Wigner distribution (Symbar (Wigner).vi). After execution of the final subVl in this program, two-dimensional time-frequency graphs of both parasympathetic (HF) activity and of the combined parasympathetic and sympathetic (LF) activity were generated on the control panel of Symbar (Wigner).vi. The control panel also displayed graphs of the decimated 1113I waveform, as well as the LF/HF ratio with respect to time. 4.2.2 Power Spectrum Analysis of Heart Rate Variability The power spectrum analysis program, also written in LabVIEW, consists of several subVIs that ultimately generate graphs of the LF area and HF area of the corresponding ECG signal. As with the Wigner program, the signal was read from a spreadsheet file as a single column of space delimited data (Read From Spreadsheet File.vi). However, in
- Page 109 and 110: 80 The final step to obtain the pow
- Page 111 and 112: 82 It should be noted that if the w
- Page 113 and 114: 84 The normal respiration rate can
- Page 115 and 116: Figure 3.12 Power spectrum of BP II
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- Page 119 and 120: 90 when there is significant correl
- Page 121 and 122: 92 3.12 Partial Coherence Analysis
- Page 123 and 124: 94 after removal of the effects of
- Page 125 and 126: 96 The bulk of the theory and appli
- Page 127 and 128: 98 technique is measurement time. T
- Page 129 and 130: 100 usually attainable. The key poi
- Page 131 and 132: 102 variability exists in the propa
- Page 133 and 134: 104 eXogenous input (ARX) was used
- Page 135 and 136: 106 The baroreflex, an autonomic re
- Page 137 and 138: 108 the principal components are no
- Page 139 and 140: 110 The mathematical solution for t
- Page 141 and 142: 112 3.15 Cluster Analysis The term
- Page 143 and 144: 114 formed) one can read off the cr
- Page 145 and 146: 116 3.15.5 Squared Euclidian Distan
- Page 147 and 148: 118 Alternatively, one may use the
- Page 149 and 150: 120 Sneath and Sokal used the abbre
- Page 151 and 152: 122 may seem a bit confusing at fir
- Page 153 and 154: CHAPTER 4 METHODS The purpose of th
- Page 155 and 156: 126 4.1.2.1 Autonomic Testing. HR V
- Page 157 and 158: 128 of heart rate, blood pressure,
- Page 159: 130 The patients who underwent LVRS
- 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
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- 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
131<br />
4.2 Data Analysis<br />
All signal processing techniques were performed with the LabVIEW graphical<br />
programming language and MATLAB. This section describes the use <strong>of</strong> both<br />
LabVIEW and MATLAB and the algorithms by which the different physiological<br />
signals were analyzed. All the programs for spectral analysis, the Wigner timefrequency<br />
analysis program (Sympar.vi), coherence, weighted coherence and partial<br />
coherence were written in LabVIEW. The time-frequency analysis (Cohen's class and<br />
wavelets), system identification techniques, principal component analysis and cluster<br />
analysis were performed using the MATLAB System Identification and Statistics<br />
toolboxes.<br />
4.2.1 Wigner Time-Frequency Analysis<br />
As mentioned previously, the Wigner time-frequency analysis program (Sympar.vi) was<br />
written in LabVIEW. The time-frequency analysis was performed using raw ECG data.<br />
The ECG signal was read from a spreadsheet file acquired together with other signals<br />
such as respiration, blood pressure and Oxygen volume consumption. All <strong>of</strong> these<br />
signals were represented in spreadsheet form as columns <strong>of</strong> space delimited data. The<br />
ECG data was filtered through a high-pass filter (HP_FILT.vi) after which point the R<br />
wave locations were detected automatically (Search BP.vi). The array <strong>of</strong> R wave<br />
locations was then used to calculate the heart rate and to generate graphs <strong>of</strong> both the<br />
ECG signal with detected R waves and <strong>of</strong> the heart rate, respectively. The raw ECG<br />
signal was then displayed with markers at each corresponding detected R wave along<br />
with the newly calculated corresponding heart rate. These graphs appear on the control
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