Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs.

Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in CPR for accurate analysis heart rhythms. In this study, we propose a novel Convolutional Neural Network-based Encoder-Decoder (CNNED) structure with a shock advisory algorithm to improve the accuracy and reliability of shock versus non-shock decision-making without CPR pause in OHCA scenarios. Our approach employs a cascade of CNNEDs in conjunction with an AED shock advisory algorithm to process the ECG data for shock decisions. Initially, a CNNED trained on an equal number of shockable and non-shockable rhythms is used to filter the CPR-contaminated data. The resulting filtered signal is then fed into a second CNNED, which is trained on imbalanced data more tilted toward the specific rhythm being analyzed. A reliable shock versus non-shock decision is made when both classifiers from the cascade structure agree, while segments with conflicting classifications are labeled as indeterminate, indicating the need for additional segments to analyze. To evaluate our approach, we generated CPR-contaminated ECG data by combining clean ECG data with 52 CPR samples. We used clean ECG data from the CUDB, AFDB, SDDB, and VFDB databases, to which 52 CPR artifact cases were added, while a separate test set provided by the AED manufacturer Defibtech LLC was used for performance evaluation. The test set comprised 20,384 non-shockable CPR-contaminated segments from 392 subjects, as well as 3744 shockable CPR-contaminated samples from 41 subjects with coarse ventricular fibrillation (VF) and 31 subjects with rapid ventricular tachycardia (rapid VT). We observed improvements in rhythm analysis using our proposed cascading CNNED structure when compared to using a single CNNED structure. Specifically, the specificity of the proposed cascade of CNNED structure increased from 99.14% to 99.35% for normal sinus rhythm and from 96.45% to 97.22% for other non-shockable rhythms. Moreover, the sensitivity for shockable rhythm detection increased from 90.90% to 95.41% for ventricular fibrillation and from 82.26% to 87.66% for rapid ventricular tachycardia. These results meet the performance thresholds set by the American Heart Association and demonstrate the reliable and accurate analysis of heart rhythms during CPR using only ECG data without the need for CPR interruptions or a reference signal.

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimizing interruptions in CPR increases the chance of survival. Methods and Results The objective of this study was to apply a deep-learning algorithm using convolutional layers, residual networks, and bidirectional long short-term memory method to classify shockable versus nonshockable rhythms in the presence and absence of CPR artifact. Forty subjects' data from Physionet with 1131 shockable and 2741 nonshockable samples contaminated with 43 different CPR artifacts that were acquired from a commercial automated external defibrillator during asystole were used. We had separate data as train and test sets. Using our deep neural network model, the sensitivity and specificity of the shock versus no-shock decision for the entire data set over the 4-fold cross-validation sets were 95.21% and 86.03%, respectively. This result was based on the training and testing of the model using ECG data in both the presence and the absence of CPR artifact. For ECG without CPR artifact, the sensitivity was 99.04% and the specificity was 95.2%. A sensitivity of 94.21% and a specificity of 86.14% were obtained for ECG with CPR artifact. In addition to 4-fold cross-validation sets, we also examined leave-one-subject-out validation. The sensitivity and specificity for the case of leave-one-subject-out validation were 92.71% and 97.6%, respectively. Conclusions The proposed trained model can make shock versus nonshock decision in automated external defibrillators, regardless of CPR status. The results meet the American Heart Association's sensitivity requirement (>90%).

OxiMA: A Frequency-Domain Approach to Address Motion Artifacts in Photoplethysmograms for Improved Estimation of Arterial Oxygen Saturation and Pulse Rate.

OBJECTIVE: The purpose of this paper is to demonstrate that a new algorithm for estimating arterial oxygen saturation can be effective even with data corrupted by motion artifacts (MAs). METHODS: OxiMA, an algorithm based on the time-frequency components of a photoplethysmogram (PPG), was evaluated using 22-min datasets recorded from 10 subjects during voluntarily-induced hypoxia, with and without subject-induced MAs. A Nellcor OxiMax transmission sensor was used to collect an analog PPG while reference oxygen saturation and pulse rate (PR) were collected simultaneously from an FDA-approved Masimo SET Radical RDS-1 pulse oximeter. RESULTS: The performance of our approach was determined by computing the mean relative error between the PR/oxygen saturation estimated by OxiMA and the reference Masimo oximeter. The average estimation error using OxiMA was 3 beats/min for PR and 3.24% for oxygen saturation, respectively. CONCLUSION: The results show that OxiMA has great potential for improving the accuracy of PR and oxygen saturation estimation during MAs. SIGNIFICANCE: This is the first study to demonstrate the feasibility of a reconstruction algorithm to improve oxygen saturation estimates on a dataset with MAs and concomitant hypoxia.

A novel approach using time-frequency analysis of pulse-oximeter data to detect progressive hypovolemia in spontaneously breathing healthy subjects.

Accurate and early detection of blood volume loss would greatly improve intraoperative and trauma care. This study has attempted to determine early diagnostic and quantitative markers for blood volume loss by analyzing photoplethysmogram (PPG) data from ear, finger and forehead sites with our high-resolution time-frequency spectral (TFS) technique in spontaneously breathing healthy subjects (n = 11) subjected to lower body negative pressure (LBNP). The instantaneous amplitude modulations present in heart rate (AM HR) and breathing rate (AMBR) band frequencies of PPG signals were calculated from the high-resolution TFS. Results suggested that the changes (P < 0.05) in AMBR and especially in AMHR values can be used to detect the blood volume loss at an early stage of 20% LBNP tolerance when compared to the baseline values. The mean percent decrease in AMHR values at 100% LBNP tolerance was 78.3%, 72.5%, and 33.9% for ear, finger, and forehead PPG signals, respectively. The mean percent increase in AMBR values at 100% LBNP tolerance was 99.4% and 19.6% for ear and finger sites, respectively; AMBR values were not attainable for forehead PPG signal. Even without baseline AMHR values, our results suggest that hypovolemia detection is possible with specificity and sensitivity greater than 90% for the ear and forehead locations when LBNP tolerance is 100%. Therefore, the TFS analysis of noninvasive PPG waveforms is promising for early diagnosis and quantification of hypovolemia at levels not identified by vital signs in spontaneously breathing subjects.

Atrial Fibrillation detection using time-varying coherence function and Shannon Entropy.

We introduce a novel method for automatic detection of Atrial Fibrillation (AF) using time-varying coherence functions (TVCF) and Shannon Entropy (SE). The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs). Two TVTFs are obtained using two adjacent data segments with one data segment as the input signal and the other data segment as the output to produce the first TVTF; the second TVTF is produced by reversing the input and output signals. The detection algorithm was tested on RR interval time series derived from two databases: the MIT-BIH Atrial Fibrillation (AF) and the MIT-BIH normal sinus rhythm (NSR). The MIT-BIH database contains a variety of short and long AF beats from 25 subjects and the MIT-BIH NSR database consists of only normal sinus rhythms from 18 subjects. Using the receiver operating characteristic curves from the combination of TVCF and SE, we obtained the accuracy of 97.49%, sensitivity of 97.41% and specificity of 97.54% for the MIT-BIH AF database. Furthermore, the specificity of the MIT-BIH NSR database was 100%.

Time-varying methods for characterizing nonstationary dynamics of physiological systems.

BACKGROUND: Accurate and early diagnosis of various diseases and pathological conditions require analysis techniques that can capture time-varying (TV) dynamics. In the pursuit of promising TV signal processing methods applicable to real-time clinical monitoring applications, nonstationary spectral techniques are of great significance. OBJECTIVES: We present two potential practical applications of such techniques in quantifying TV physiological dynamics concealed in photoplethysmography (PPG) signals: early detection of blood-volume loss using a nonparametric approach known as variable frequency complex demodulation (VFCDM), and accurate detection of abrupt changes in respiratory rates using a parametric approach known as combined optimal parameter search and multiple mode particle filtering (COPS-MPF). METHODS: The VFCDM technique has been tested using ear-PPG signals in two study models: mechanically ventilated patients undergoing surgery in operating room settings and spontaneously breathing conscious healthy subjects subjected to lower body negative pressure (LBNP) in laboratory settings. Extraction of respiratory rates has been tested using COPS-MPF technique in finger-PPG signals collected from healthy volunteers with abrupt changes in respiratory rate ranging from 0.1 to 0.4 Hz. RESULTS: VFCDM method showed promise to detect the blood loss noninvasively in mechanical ventilated patients well before blood losses become apparent to the physician. In spontaneously breathing subjects during LBNP experiments, the early detection and quantification of blood loss was possible at 40% of LBNP tolerance. COPS-MPF showed high accuracy in detecting the constant as well as sudden changes in respiratory rates as compared to other time-invariant methods. CONCLUSION: Integration of such robust algorithms into pulse oximeter device may have significant impact in real-time clinical monitoring and point-of-care healthcare settings.

Respiratory rate extraction via an autoregressive model using the optimal parameter search criterion.

We present an autoregressive model-based method which enables accurate respiratory rate extraction from pulse oximeter recordings over a wide range: 12-48 breaths/min. The method uses the optimal parameter search (OPS) technique to estimate accurate AR parameters which are then factorized into multiple pole terms. The pole with the highest magnitude is shown to correspond to the respiratory rate. The performance of the proposed method to extract respiratory rate is compared to the widely used Burg algorithm using both simulation examples and pulse oximeter recordings. In a previous study, we demonstrated several nonparametric time-frequency approaches that were more accurate than Burg's algorithm when the data length was 1 min [Chon, K. H., S. Dash, and K. Ju. IEEE Trans. Biomed. Eng. 56(8):2054-2063, 2009]. One of the key advantages of the AR method is that a shorter data length can be used. Thus, in this study, we reduced the data length to 30 s and applied our OPS algorithm to examine if accurate respiratory rates can be extracted directly from pulse oximeter recordings. It was found that our proposed method's accuracy was consistently better with smaller variance than Burg's method. In particular, our proposed method's accuracy was significantly greater when respiratory rates were lower than 24 breaths/min.

Automatic real time detection of atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with significant morbidity and mortality. Timely diagnosis of the arrhythmia, particularly transient episodes, can be difficult since patients may be asymptomatic. In this study, we describe a robust algorithm for automatic detection of AF based on the randomness, variability and complexity of the heart beat interval (RR) time series. Specifically, we employ a new statistic, the Turning Points Ratio, in combination with the Root Mean Square of Successive RR Differences and Shannon Entropy to characterize this arrhythmia. The detection algorithm was tested on two databases, namely the MIT-BIH Atrial Fibrillation Database and the MIT-BIH Arrhythmia Database. These databases contain several long RR interval series from a multitude of patients with and without AF and some of the data contain various forms of ectopic beats. Using thresholds and data segment lengths determined by Receiver Operating Characteristic (ROC) curves we achieved a high sensitivity and specificity (94.4% and 95.1%, respectively, for the MIT-BIH Atrial Fibrillation Database). The algorithm performed well even when tested against AF mixed with several other potentially confounding arrhythmias in the MIT-BIH Arrhythmia Database (Sensitivity = 90.2%, Specificity = 91.2%).

Detection of low-frequency oscillations in renal blood flow.

Detection of the low-frequency (LF; approximately 0.01 Hz) component of renal blood flow, which is theorized to reflect the action of a third renal autoregulatory mechanism, has been difficult due to its slow dynamics. In this work, we used three different experimental approaches to detect the presence of the LF component of renal autoregulation using normotensive and spontaneously hypertensive rats (SHR), both anesthetized and unanesthetized. The first experimental approach utilized a blood pressure forcing in the form of a chirp, an oscillating perturbation with linearly increasing frequency, to elicit responses from the LF autoregulatory component in anesthetized normotensive rats. The second experimental approach involved collection and analysis of spontaneous blood flow fluctuation data from anesthetized normotensive rats and SHR to search for evidence of the LF component in the form of either amplitude or frequency modulation of the myogenic and tubuloglomerular feedback mechanisms. The third experiment used telemetric recordings of arterial pressure and renal blood flow from normotensive rats and SHR for the same purpose. Our transfer function analysis of chirp signal data yielded a resonant peak centered at 0.01 Hz that is greater than 0 dB, with the transfer function gain attenuated to lower than 0 dB at lower frequencies, which is a hallmark of autoregulation. Analysis of the data from the second experiments detected the presence of approximately 0.01-Hz oscillations only with isoflurane, albeit at a weaker strength compared with telemetric recordings. With the third experimental approach, the strength of the LF component was significantly weaker in the SHR than in the normotensive rats. In summary, our detection via the amplitude modulation approach of interactions between the LF component and both tubuloglomerular feedback and the myogenic mechanism, with the LF component having an identical frequency to that of the resonant gain peak, provides evidence that 0.01-Hz oscillations may represent the third autoregulatory mechanism.

Electrohydraulic pump-driven closed-loop blood pressure-regulatory system.

In this paper, we describe our design for a new electrohydraulic (EH) pump-driven renal perfusion pressure (RPP)-regulatory system capable of implementing precise and rapid RPP regulation in experimental animals. Without this automated system, RPP is manually controlled via a blood pressure clamp, and the imprecision in this method leads to compromised RPP data. This motivated us to develop an EH pump-driven closed-loop blood pressure regulatory system based on flow-mediated occlusion using the vascular occlusive cuff technique. A closed-loop servo-controller system based on a proportional plus integral (PI) controller was designed using the dynamic feedback RPP signal from animals. In vivo performance was evaluated via flow-mediated RPP occlusion, maintenance, and release responses during baseline and ANG II-infused conditions. A step change of -30 mmHg, referenced to normal RPP, was applied to Sprague-Dawley rats with the proposed system to assess the performance of the PI controller. The PI's performance was compared against manual control of blood pressure clamp to regulate RPP. Rapid RPP occlusion (within 3 s) and a release time of approximately 0.3 s were obtained for the PI controller for both baseline and ANG II infusion conditions, in which the former condition was significantly better than manual control. We concluded that the proposed EH RPP-regulatory system could fulfill in vivo needs to study various pressure-flow relationships in diverse fields of physiology, in particular, studying the dynamics of the renal autoregulatory mechanisms.

Time-varying causal coherence function and its application to renal blood pressure and blood flow data.

This paper describes the development of a model-based approach to estimating both feedforward and feedback paths of causal time-varying coherence functions (TVCF). Theoretical derivations of the coherence bounds of the causal TVCF using the proposed approach are also provided. Both theoretical derivations and simulation results revealed interesting observations, and they were corroborated using experimental renal blood pressure and flow data. Specifically, both theoretical derivations and experimental data showed that in certain cases, the calculation of the traditional TVCF was inappropriate when the system under investigation was a causal system. Moreover, the use of the causal TVCF not only provides quantitative assessment of the coupling between the two signals, but it also provides valuable insights into the composition of the physical structure of the renal autoregulatory system.

An approach to estimate time-varying casual coherence function.

OBJECTIVES: This paper describes the development of a model-based approach to estimating both open-loop and causal time-varying coherence functions (TVCF). Theoretical derivations of the coherence bounds using the proposed approach are also provided. METHODS: A time-varying vector autoregressive (VAR) model was used to estimate both open-loop and causal TVCF. The time-varying optimal parameter search method was employed to identify the time-varying model coefficients as well as the model order of the VAR model. RESULTS: Simulation results revealed interesting observations, and they were corroborated using experimental renal blood pressure and flow data. Specifically, experimental data showed that in certain cases, the calculation of the open-loop TVCF might provide incorrect interpretation of the results when the system under investigation was a closed-loop system, which is consistent with theoretical derivations. CONCLUSIONS: The use of the closed-loop TVCF not only provides quantitative assessment of the coupling between the two signals, but it also provides valuable insights into the composition of the physical structure of the system.

Changes in expression of p53, proliferating cell nuclear antigen and bcl-2 in recurrent laryngeal cancer after radiotherapy.

The biological changes in recurrent laryngeal cancer following radiotherapy are not fully understood. The authors investigated differences in the expression of p53, proliferating cell nuclear antigen (PCNA) and bcl-2 in laryngeal cancer specimens before radiotherapy and in recurrent laryngeal cancer specimens following radiotherapy in the same patients. The authors investigated the expression of p53, PCNA and bcl-2 by immunohistochemical stain in 30 specimens from 15 patients with primary laryngeal cancer and recurrent laryngeal cancer after radiotherapy. The expression of p53 protein was significantly different in laryngeal cancer before radiotherapy (4/15, 26.7 per cent) compared with recurrent laryngeal cancer after radiotherapy (8/15, 53.3 per cent) (p<0.05). The PCNA index was also significantly different in laryngeal cancer specimens before radiotherapy (mean, 11.9 per cent) compared with recurrent laryngeal cancer after radiotherapy (mean, 18.0 per cent) (p<0.05). However, there was no statistically significant alteration of bcl-2 expression in primary compared with recurrent laryngeal cancer. The expression of p53 and PCNA increased in recurrent laryngeal cancers after radiotherapy, compared with that in laryngeal cancers before radiotherapy. Recurrent laryngeal cancers arising following radiotherapy became biologically aggressive.

Separation of heart rate variability components of the autonomic nervous system by utilizing principal dynamic modes.

This work introduces a modified Principal Dynamic Modes (PDM) methodology using eigenvalue/eigenvector analysis to separate individual components of the sympathetic and parasympathetic nervous contributions to heart rate variability. We have modified the PDM technique to be used with even a single output signal of heart rate variability data, whereas the original PDMs required both input and output data. This method specifically accounts for the inherent nonlinear dynamics of heart rate control, which the current method of power spectrum density (PSD) is unable to do. Propranolol and atropine were administered to normal human volunteers intravenously to inhibit the sympathetic and parasympathetic activities, respectively. With separate applications of the respective drugs, we found a significant decrease in the amplitude of the waveforms that correspond to each nervous activity. Furthermore, we observed near complete elimination of these dynamics when both drugs were given to the subjects. Comparison of our method to the conventional low/high frequency ratio of PSD shows that PDM methodology provides much more accurate assessment of the autonomic nervous balance by separation of individual components of the autonomic nervous activities. The PDM methodology is expected to have an added benefit that diagnosis and prognostication of a patient's health can be determined simply via a non-invasive electrocardiogram.

A portable, low-cost, battery-powered wireless monitoring system for obtaining varying physiologic parameters from multiple subjects.

A portable, low-cost, and battery-powered wireless monitoring system that is capable of measuring multiple physiologic parameters simultaneously from many subjects was developed. The wireless communication of data is based on a commercially-available mote known as Tmote Sky. The star network topology (SNT), is used to collect data from many patients via multiple motes. Application protocol software was developed to facilitate the communication link between the monitor terminal and multiple motes. Based on the standard specifications of the mote, the SNT strategy, and the application protocol software design, a single mote can support up to 5 electrocardiogram signals with a sampling rate of 200 Hz. This capability facilitates affordable wireless monitoring of multiple physiologic signals from many subjects; its application is especially attractive for monitoring subjects in nursing homes, battlefields, and disaster scenarios.

Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics.

Liver cirrhosis is a worldwide health problem. Reliable, noninvasive methods for early detection of liver cirrhosis are not available. Using a three-step approach, we classified sera from rats with liver cirrhosis following different treatment insults. The approach consisted of: (i) protein profiling using surface-enhanced laser desorption/ionization (SELDI) technology; (ii) selection of a statistically significant serum biomarker set using machine learning algorithms; and (iii) identification of selected serum biomarkers by peptide sequencing. We generated serum protein profiles from three groups of rats: (i) normal (n=8), (ii) thioacetamide-induced liver cirrhosis (n=22), and (iii) bile duct ligation-induced liver fibrosis (n=5) using a weak cation exchanger surface. Profiling data were further analyzed by a recursive support vector machine algorithm to select a panel of statistically significant biomarkers for class prediction. Sensitivity and specificity of classification using the selected protein marker set were higher than 92%. A consistently down-regulated 3495 Da protein in cirrhosis samples was one of the selected significant biomarkers. This 3495 Da protein was purified on-chip and trypsin digested. Further structural characterization of this biomarkers candidate was done by using cross-platform matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) peptide mass fingerprinting (PMF) and matrix-assisted laser desorption/ionization time of flight/time of flight (MALDI-TOF/TOF) tandem mass spectrometry (MS/MS). Combined data from PMF and MS/MS spectra of two tryptic peptides suggested that this 3495 Da protein shared homology to a histidine-rich glycoprotein. These results demonstrated a novel approach to discovery of new biomarkers for early detection of liver cirrhosis and classification of liver diseases.

Common crus aplasia: diagnosis by 3D volume rendering imaging using 3DFT-CISS sequence.

AIM: The purpose of this study was to evaluate the findings of three-dimensional (3D) volume rendering (VR) imaging in common crus aplasia (CCA) of the inner ear. MATERIALS AND METHODS: Using 3D VR imaging of temporal bone constructive interference in steady state (CISS) magnetic resonance (MR) images, we retrospectively reviewed seven inner ears of six children who were candidates for cochlear implants and who had been diagnosed with CCA. As controls, we used the same method to examine 402 inner ears of 201 patients who had no clinical symptoms or signs of sensorineural hearing loss. Temporal bone MR imaging (MRI) was performed with a 1.5 T MR machine using a CISS sequence, and VR of the inner ear was performed on a work station. Morphological image analysis was performed on rotation views of 3D VR images. RESULTS: In all seven cases, CCA was diagnosed by the absence of the common crus. The remaining superior semicircular canal (SCC) was normal in five and hypoplastic in two inner ears, while the posterior SCC was normal in all seven. One patient showed bilateral symmetrical CCA. Complicated combined anomalies were seen in the cochlea, vestibule and lateral SCC. CONCLUSION: 3D VR imaging findings with MR CISS sequence can directly diagnose CCA. This technique may be useful in delineating detailed anomalies of SCCs.

Respiratory sinus arrhythmia: opposite effects on systolic and mean arterial pressure in supine humans.

1. Are arterial blood pressure fluctuations buffered or reinforced by respiratory sinus arrhythmia (RSA)? There is still considerable debate about this simple question. Different results have been obtained, triggering a discussion as to whether or not the baroreflexes are responsible for RSA. We suspected that the measurements of different aspects of arterial pressure (mean arterial pressure (MAP) and systolic pressure (SP)) can explain the conflicting results. 2. Simultaneous recordings of beat-to-beat MAP, SP, left cardiac stroke volume (SV, pulsed ultrasound Doppler), heart rate (HR) and respiration (RE) were obtained in 10 healthy young adults during spontaneous respiration. In order to eliminate HR variations at respiratory frequency we used propranolol and atropine administration in the supine and tilted positions. Respiration-synchronous variation in the recorded variables was quantified by spectral analysis of the recordings of each of these variables, and the phase relations between them were determined by cross-spectral analysis. 3. MAP fluctuations increased after removing heart rate variations in both supine and tilted position, whereas SP fluctuations decreased in the supine position and increased in the head-up tilted position. 4. RSA buffers respiration-synchronous fluctuations in MAP in both positions. However, fluctuations in SP were reinforced by RSA in the supine and buffered in the tilted position.

A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry.

A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algorithm is developed for modeling time series data. The new algorithm is based on the concepts of affine geometry in which the salient feature of the algorithm is to remove the linearly dependent ARMA vectors from the pool of candidate ARMA vectors. For noiseless time series data with a priori incorrect model-order selection, computer simulations show that accurate linear and nonlinear ARMA model parameters can be obtained with the new algorithm. Many algorithms, including the fast orthogonal search (FOS) algorithm, are not able to obtain correct parameter estimates in every case, even with noiseless time series data, because their model-order search criteria are suboptimal. For data contaminated with noise, computer simulations show that the new algorithm performs better than the FOS algorithm for MA processes, and similarly to the FOS algorithm for ARMA processes. However, the computational time to obtain the parameter estimates with the new algorithm is faster than with FOS. Application of the new algorithm to experimentally obtained renal blood flow and pressure data show that the new algorithm is reliable in obtaining physiologically understandable transfer function relations between blood pressure and flow signals.