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ABSTRACT: The use of non-invasively measured pulse transit time (PTT) to monitor the cardiovascular systems in critically ill patients, like sepsis, can be of significant clinical value. In this study, the potential of PTT and its variability in cardiovascular system monitoring in a mechanically ventilated and anesthetized rabbit model of endotoxic shock was assessed. Eight adult New Zealand white rabbits, which were treated with endotoxin bolus infusion, were studied. Measurements of PTT, pre-ejection period (PEP), and vascular transit time (VTT) were obtained in pre- and post-intervention stages (before and 90 minutes after the administration of endotoxin). The decrease in mean PTT (p <; 0.05) and PEP (p <; 0.01) in the post-intervention stage reflected sympathetic activation, whilst the increase in respiratory variation in PTT (p <; 0.01), PEP (p <; 0.01), and VTT (p <; 0.01) could be attributed to an enhancement of respiratory variation in stroke volume associated with hypovolemia in endotoxic shock. The relationship between beat-to-beat variability in PTT and all other cardiovascular time series were further investigated through linear regression analysis, which revealed that PTT was most strongly correlated with VTT (R<sup>2</sup> ≥ 0.84 with positive slope). Computation of coherence and phase shift in the ventilating frequency band (HF: 0.50 - 0.75 Hz) showed that the respiratory variation in PTT was synchronized with both PEP and VTT (coherence > 0.84 with phase shift less than one cardiac beat). These results highlighted the potential value of PTT and its respiratory variation in characterizing the pathophysioloigcal hemodynamic change in endotoxic shock.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: Renal failure patients provide a good model of fluid overload with the process of hemodialysis leading to central hypovolemia. This study aims to assess if hemodialysis induces identifiable changes in ear photoplethysmographic waveform variability (PPGV). The results are based on data collected from 10 kidney failure patients undergoing regular hemodialysis; classified as either fluid removal or non-fluid removal patients. Six minutes of continuous photoplethysmography (PPG) signals were recorded at pre-dialysis, end of dialysis and at regular intervals of 20 minutes during hemodialysis. Baseline and amplitude variabilities were derived from the PPG waveform. Frequency spectrum analysis was applied to these variability signals and spectral powers were then calculated from low frequency (LF), mid frequency (MF) and high frequency (HF) bands. The results indicate that in fluid removal patients, LF (p = 0.04), MF (p = 0.03) and HF (p = 0.0003) powers of amplitude ear PPGV (expressed in mean-scaled units) showed a significant increase at the end of dialysis compared to pre-dialysis. No significant change was observed in non-fluid removal patients. A moderate correlation was found between relative blood volume (RBV) and HF power (median R = 0.64, p <; 0.05). This study suggests that ear PPG may be a suitable monitor of the systemic circulation and can provide a non-invasive tool to detect blood volume loss.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: Low systemic vascular resistance (SVR) can be a useful indicator for early diagnosis of critical pathophysiological conditions such as sepsis, and the ability to identify low SVR from simple and noninvasive physiological signals is of immense clinical value. In this study, an SVR classification system is presented to recognize the occurrence of low SVR, among a heterogenous group of patients (N = 48), based on the use of routine cardiovascular measurements and features extracted from the finger photoplethysmogram (PPG) as inputs to a quadratic discriminant classifier. An exhaustive feature search was performed to identify a near optimum feature subset. Cohen's kappa coefficient (κ) was used as a performance measure to compare candidate feature sets. The classifier using the following combination of features performed best (κ = 0.56, sensitivity = 96.30%, positive predictivity = 92.31%): normalized low-frequency power (LF<sub>NU</sub>) derived from PPG, ratio of low-frequency power to high-frequency power (LF/HF) of the PPG variability signal, and the ratio of mean arterial pressure to heart rate (MAP/HR). Classifiers that used either LF<sub>NU</sub> (κ = 0.43), LF/HF (κ = 0.37) or MAP/HR (κ = 0.43) alone showed inferior performance. Discrimination of patients with and without low SVR can be achieved with reasonable accuracy using multiple features derived from the PPG combined with routine cardiovascular measurements.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function(RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The epsiv-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (epsiv) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE = 3D1.5) as well as testing data (AMSE = 3D1.4) compared to linear regression (AMSE = 3D1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE = 3D15.8 and 16.4) compared to linear regression (AMSE = 3D25.2 and 20.1).
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE; 10/2009
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ABSTRACT: This paper reports changes in the spectral powers of finger photoplethysmographic waveform variability (PPGV) following hemodialysis compared to pre-dialysis. The results are based on data collected from 12 hemodynamically stable patients having regular hemodialysis thrice weekly. Six minutes of continuous electrocardiogram (ECG) and finger infra-red photoplethysmographic (PPG) signals were collected at pre-dialysis and at end of dialysis. A four minute artefact free segment was selected and baseline and amplitude variabilities were derived from PPG waveform. Heart rate variability was derived from ECG R-R interval. Frequency spectrum analysis was then applied to these variability signals. The spectral powers were then calculated from low frequency (LF), mid frequency (MF) and high frequency (HF) bands. The results indicate that LF (p = 3D 0.01) and MF (p = 3D 0.02) powers of baseline PPGV (expressed in mean-scaled units) and LF (p = 3D 0.006), MF (p = 3D 0.003) and HF (p = 3D 0.017) powers of amplitude PPGV (expressed in mean-scaled units) showed a significant increase at the end of dialysis compared to pre-dialysis. HRV spectral measures did not show any significant change. The increase in LF and MF powers in PPGV may suggest the recovery and further enhancement of peripheral sympathetic vascular modulation as a result of volume unloading in initially hypervolemic dialysis patients, at the same time the increase in respiratory HF power in PPGV may indicate preload reduction.
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE; 10/2009
