njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
101 The quantity C(ω) has a range of 0.0 to 1.0, where 1.0 indicates that all of the measured output power is due to the input excitation. This is the most desirable situation and will only be true at frequencies where the spectral energy of the noise ny (t) is negligible. The coherence can therefore be viewed as a measurement quality indicator. When a significant portion of the measured output is not related to the excitation, a low coherence results. This indicates that for a given amount of averaging, the variance of the transfer function at these frequencies will be higher than the variance where there is good coherence (closer to 1.0). It is important to recognize that since this estimator is unbiased, given sufficient averaging, the transfer function estimate will converge to the system's actual transfer function in spite of possibly low coherence. The above transfer function and coherence estimation calculations are calculated in Lab VIEW and/or MatLab System Identification Toolbox. 3.13.2 The ARX Models The timing of electrical and mechanical events within the heart is vital to its function. In "normal" healthy humans, a bundle of spontaneously depolarizing cells located on the right atrium of the heart, called the sino-atrial (SA) node, acts as the pacemaker for the heart. Through an upward drift in electrical potential, the cells spontaneously reach a threshold potential, at which point the cells rapidly depolarize, or "fire", as a group. This is followed by a reset which marks the start of a new cycle. The firing initiates the spread of electrical activity through the heart, and therefore initiates the contraction that is necessary for blood to be delivered to the rest of the body. Although temporal
102 variability exists in the propagation of the electrical activity across the tissue of the heart, the primary interest is in the temporal variability from "beat-to-beat", which can be captured through observations of a distinct electrical event contained within each cycle. The spontaneous depolarization of SA nodal cells has an intrinsic rate that is modulated by direct input from the two branches of the autonomic nervous system (ANS). The basal activity of each branch has an effect on the mean rate of depolarization, but also the vast majority of variability in the timing of the electrical events of the heart is produced via this autonomic innervation by varying how quickly the SA nodal cells reach the threshold and "fire". Activity of the respiratory rhythm generator has been shown to modulate the rate of depolarization of SA nodal cells at the respiratory frequency via the parasympathetic branch, and baroreflex feedback mechanisms have been shown to modulate the rate through both branches at sub respiratory frequencies. The goal of the modeling, therefore, will be to capture the dynamics of these mechanisms in a relatively simple ARX-modeling scheme, which has direct physiological interpretation. Here two models are used for this study. The first model is an open loop input driven ARX model which can capture the characteristics of the "heart rate" variability. One input is simply a filtered Gaussian white process, for which several nice results will be developed. Other inputs correspond directly to autonomic mediation specifically related to respiration and blood pressure related modulation. The other model includes a feedback that allows one to investigate the feedback mechanisms in the ARX model, which will lay the groundwork for capturing the effect of baroreflex activity on heart rate variability.
- Page 79 and 80: 50 The advantage of using equation
- Page 81 and 82: 52 3.5 Covariance and Invariance Th
- Page 83 and 84: where H(f), S(f) are Fourier transf
- Page 85 and 86: 56 Another shortcoming of the spect
- Page 87 and 88: 58 should take the kernel of the WD
- Page 89 and 90: 60 called the cross Wigner distribu
- Page 91 and 92: 62 3.6.3 The Choi-Williams (Exponen
- Page 93 and 94: 64 Figure 3.3 Performance of the Ch
- Page 95 and 96: 66 [-Ω,Ω ], then its STFT will be
- Page 97 and 98: 68 This condition forces that the w
- Page 99 and 100: 70 where c is a constant. Thus, the
- Page 101 and 102: Figure 3.5 The time-frequency plane
- Page 103 and 104: 74 The measure dadb used in the tra
- Page 105 and 106: 76 and the wavelet transform repres
- Page 107 and 108: 78 Figure 3.6 Figure depicting the
- 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 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: 100 usually attainable. The key poi
- 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
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- Page 147 and 148: 118 Alternatively, one may use the
- Page 149 and 150: 120 Sneath and Sokal used the abbre
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- 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 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
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- 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
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101<br />
The quantity C(ω) has a range <strong>of</strong> 0.0 to 1.0, where 1.0 indicates that all <strong>of</strong> the<br />
measured output power is due to the input excitation. This is the most desirable<br />
situation and will only be true at frequencies where the spectral energy <strong>of</strong> the noise<br />
ny (t) is negligible. The coherence can therefore be viewed as a measurement quality<br />
indicator. When a significant portion <strong>of</strong> the measured output is not related to the<br />
excitation, a low coherence results. This indicates that for a given amount <strong>of</strong> averaging,<br />
the variance <strong>of</strong> the transfer function at these frequencies will be higher than the variance<br />
where there is good coherence (closer to 1.0).<br />
It is important to recognize that since this estimator is unbiased, given sufficient<br />
averaging, the transfer function estimate will converge to the system's actual transfer<br />
function in spite <strong>of</strong> possibly low coherence.<br />
The above transfer function and coherence estimation calculations are calculated<br />
in Lab VIEW and/or MatLab System Identification Toolbox.<br />
3.13.2 The ARX Models<br />
The timing <strong>of</strong> electrical and mechanical events within the heart is vital to its function. In<br />
"normal" healthy humans, a bundle <strong>of</strong> spontaneously depolarizing cells located on the<br />
right atrium <strong>of</strong> the heart, called the sino-atrial (SA) node, acts as the pacemaker for the<br />
heart. Through an upward drift in electrical potential, the cells spontaneously reach a<br />
threshold potential, at which point the cells rapidly depolarize, or "fire", as a group. This<br />
is followed by a reset which marks the start <strong>of</strong> a new cycle. The firing initiates the<br />
spread <strong>of</strong> electrical activity through the heart, and therefore initiates the contraction that<br />
is necessary for blood to be delivered to the rest <strong>of</strong> the body. Although temporal