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%).

0055 Physiological Based Predictive Models of Vigilance

Introduction

The Naval Submarine Medical Research Laboratory (NSMRL) is developing predictive models to examine how non-invasive, non-disruptive physiological monitoring can be used to track performance decrements due to sleep deficiency. Utilizing biometrics extracted from physiological measures to track performance changes would allow for automated tracking of fatigue and alleviate the overhead necessary to monitor individual schedules and sleep patterns.

Methods

NSMRL collaborated with the University of Connecticut to run a sleep deprivation study that deprived 20 participants of sleep for a period of up to 25 hours. During this time, subjects completed multiple tasks, including the Psychomotor Vigilance Test (PVT) every few hours. A non-invasive monitoring system collected physiological data from participants, which includes eye tracking, electrocardiography, electrodermal activity, and facial tracking (e.g., blink metrics, heart rate variability, skin conductance levels, facial action units). Using this multimodal approach, biometrics were extracted and evaluated to determine their predictive power on PVT performance. Multiple linear regression, using predictors selected via sequential forward selection, was used to develop a model of performance at an individual level based on a subset of these metrics chosen using principal component regression.

Results

Thirty-eight biometrics were extracted from the collected data and used to produce a predictive model of PVT performance. Sequential forward selection was used to select 11 primary biometrics. The criteria for primary metric inclusion in the model was minimization of root mean squared error. The resultant model had a correlation coefficient (r) of 0.71 (p < 0.001) with a root mean squared error (RMSE) of 49.8 ms between the predicted reaction time and true reaction time for each subject.

Conclusion

Non-invasive, non-disruptive monitoring could be used to track individual cognitive performance decrement due to sleep deficiency. This study examined the capability of combining the data from four physiological monitors that can be contained within a wrist worn device and a desk or helmet mounted camera. Utilizing 11 biometrics obtained from these monitors a stepwise regression model was developed that significantly correlates with PVT reaction time at both an individual and group level.

Support

This work was supported by the Military Operational Medicine Research Program.

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.

Detection of “Noisy” Chaos in a Time Series

Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely “noise”. The output from most biological systems is probably the result of both the internal dynamics of the systems, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

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.

Detection of low-frequency oscillations in renal blood flow

Detection of the low-frequency (LF; ∼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 ∼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.

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.

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.

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 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.

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.

Accurate identification of periodic oscillations buried in white or colored noise using fast orthogonal search

We use a previously introduced fast orthogonal search algorithm to detect sinusoidal frequency components buried in either white or colored noise. We show that the method outperforms the correlogram, modified covariance autoregressive (MODCOVAR) and multiple-signal classification (MUSIC) methods. Fast orthogonal search method achieves accurate detection of sinusoids even with signal-to-noise ratios as low as -10 dB, and is superior at detecting sinusoids buried in 1/f noise. Since the utilized method accurately detects sinusoids even under colored noise, it can be used to extract a 1/f noise process observed in physiological signals such as heart rate and renal blood pressure and flow data.

A new algorithm for autoregression moving average model parameter estimation using group method of data handling

A new algorithm for autoregresive moving average (ARMA) parameter estimation is introduced. The algorithm is based on the group method of data handling (GMDH) first introduced by the Russian cyberneticist, A. G. Ivakhnenko, for solving high-order regression polynomials. The GMDH is heuristic in nature and self-organizes into a model of optimal complexity without any a priori knowledge about the system's inner workings. We modified the GMDH algorithm to solve for ARMA model parameters. Computer simulations have been performed to examine the efficacy of the GMDH and comparison of the GMDH is made to one of the most accurate and one of the most widely used algorithms, the fast orthogonal search (FOS) and the least-squares methods, respectively. The results show that in some cases with noise contamination and incorrect model order assumptions, the GMDH performs better than either the FOS or the least-squares methods in providing only the parameters that are associated with the true model terms.

Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate model predictions. Furthermore, we compare the performance of the new approach to that of the deterministic recurrent neural network approach. Using this simple two-step procedure, we obtain more robust model predictions than with the deterministic recurrent neural network approach despite the presence of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals.

Nonlinear analysis of renal autoregulation in rats using principal dynamic modes

This article presents results of the use of a novel methodology employing principal dynamic modes (PDM) for modeling the nonlinear dynamics of renal autoregulation in rats. The analyzed experimental data are broadband (0-0.5 Hz) blood pressure-flow data generated by pseudorandom forcing and collected in normotensive and hypertensive rats for two levels of pressure forcing (as measured by the standard deviation of the pressure fluctuation). The PDMs are computed from first-order and second-order kernel estimates obtained from the data via the Laguerre expansion technique. The results demonstrate that two PDMs suffice for obtaining a satisfactory nonlinear dynamic model of renal autoregulation under these conditions, for both normotensive and hypertensive rats. Furthermore, the two PDMs appear to correspond to the two main autoregulatory mechanisms: the first to the myogenic and the second to the tubuloglomerular feedback (TGF) mechanism. This allows the study of the separate contributions of the two mechanisms to the autoregulatory response dynamics, as well as the effects of the level of pressure forcing and hypertension on the two distinct autoregulatory mechanisms. It is shown that the myogenic mechanism has a larger contribution and is affected only slightly, while the TGF mechanism is affected considerably by increasing pressure forcing or hypertension (the emergence of a second resonant peak and the decreased relative contribution to the response flow signal).

Nonlinear system analysis of renal autoregulation in normotensive and hypertensive rats

We compared the dynamic characteristics in renal autoregulation of blood flow of normotensive Sprague-Dawley rats (SDR) and spontaneously hypertensive rats (SHR), using both linear and nonlinear systems analysis. Linear analysis yielded only limited information about the differences in dynamics between SDR and SHR. The predictive ability, as determined by normalized mean-square errors (NMSE), of a third-order Volterra model is better than for a linear model. This decrease in NMSE with a third-order model from that of a linear model is especially evident at frequencies below 0.2 Hz. Furthermore, NMSE are significantly higher in SHR than SDR, suggesting a more complex nonlinear system in SHR. The contribution of the third-order kernel in describing the dynamics of renal autoregulation in arterial blood pressure and blood flow was found to be important. Moreover, we have identified the presence of nonlinear interactions between the oscillatory components of the myogenic mechanism and tubuloglomerular feedback (TGF) at the level of whole kidney blood flow in SDR. An interaction between these two mechanisms had previously been revealed for SDR only at the single nephron level. However, nonlinear interactions between the myogenic and TGF mechanisms are not detected for SHR.

Detection of "noisy" chaos in a time series

Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". The output from most biological systems is probably the result of both the internal dynamics of the systems, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

Application of fast orthogonal search to linear and nonlinear stochastic systems

Standard deterministic autoregressive moving average (ARMA) models consider prediction errors to be unexplainable noise sources. The accuracy of the estimated ARMA model parameters depends on producing minimum prediction errors. In this study, an accurate algorithm is developed for estimating linear and nonlinear stochastic ARMA model parameters by using a method known as fast orthogonal search, with an extended model containing prediction errors as part of the model estimation process. The extended algorithm uses fast orthogonal search in a two-step procedure in which deterministic terms in the nonlinear difference equation model are first identified and then reestimated, this time in a model containing the prediction errors. Since the extended algorithm uses an orthogonal procedure, together with automatic model order selection criteria, the significant model terms are estimated efficiently and accurately. The model order selection criteria developed for the extended algorithm are also crucial in obtaining accurate parameter estimates. Several simulated examples are presented to demonstrate the efficacy of the algorithm.

Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions

A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise.

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Detection of chaotic determinism in time series from randomly forced maps

Time series from biological system often display fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". Despite this effort, it has been difficult to establish the presence of chaos in time series from biological sytems. The output from a biological system is probably the result of both its internal dynamics, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes, i.e., a positive characteristic exponent that leads to sensitivity to initial conditions. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

A dual-input nonlinear system analysis of autonomic modulation of heart rate

Linear analyses of fluctuations in heart rate and other hemodynamic variables have been used to elucidate cardiovascular regulatory mechanisms. The role of nonlinear contributions to fluctuations in hemodynamic variables has not been fully explored. This paper presents a nonlinear system analysis of the effect of fluctuations in instantaneous lung volume (ILV) and arterial blood pressure (ABP) on heart rate (HR) fluctuations. To successfully employ a nonlinear analysis based on the Laguerre expansion technique (LET), we introduce an efficient procedure for broadening the spectral content of the ILV and ABP inputs to the model by adding white noise. Results from computer simulations demonstrate the effectiveness of broadening the spectral band of input signals to obtain consistent and stable kernel estimates with the use of the LET. Without broadening the band of the ILV and ABP inputs, the LET did not provide stable kernel estimates. Moreover, we extend the LET to the case of multiple inputs in order to accommodate the analysis of the combined effect of ILV and ABP effect on heart rate. Analyzes of data based on the second-order Volterra-Wiener model reveal an important contribution of the second-order kernels to the description of the effect of lung volume and arterial blood pressure on heart rate. Furthermore, physiological effects of the autonomic blocking agents propranolol and atropine on changes in the first- and second-order kernels are also discussed.

Detection of interactions between myogenic and TGF mechanisms using nonlinear analysis

Pages F160–F173: K. H. Chon, Y.-M. Chen, V. Z. Marmarelis, D. J. Marsh, and N.-H. Holstein-Rathlou. “Detection of interactions between myogenic and TGF mechanisms using nonlinear analysis.” Pages F168–F169: The original Figs. 8–11 do not give sufficient detail of the contour plots. New, higher resolution images are reproduced here with the original legends.

Detection of interactions between myogenic and TGF mechanisms using nonlinear analysis

Pages F160-F173: K. H. Chon, Y.-M. Chen, V. Z. Marmarelis, D. J. Marsh, and N.-H. Holstein-Rathlou. “Detection of interactions between myogenic and TGF mechanisms using nonlinear analysis.” Pages F168-F169: The original Figs. 8-11 do not give sufficient detail of the contour plots. New, higher resolution images are reproduced here with the original legends.

Detection of interactions between myogenic and TGF mechanisms using nonlinear analysis

Previous studies using linear techniques have provided valuable insights into the dynamic characteristics of whole kidney autoregulation and have led to the general conclusion that the myogenic mechanism and tubuloglomerular feedback (TGF) are highly nonlinear control mechanisms. To explore further the dynamic nature of these nonlinear autoregulatory mechanisms, we introduce the technique of nonlinear modeling using Volterra-Wiener kernels. In the past several years, use of Volterra-Wiener kernels for nonlinear approximation has been most notably applied to neurophysiology. Recent advances in algorithms for computation of the kernels have made this technique more attractive for the study of the dynamics of nonlinear physiological systems, such as the system mediating renal autoregulation. In this study, the general theory and requirements for using this technique are discussed. The feasibility of using the technique on whole kidney pressure and flow data is examined, and a basis for using the Volterra-Wiener kernels to detect interactions between physiological control mechanisms is established. As a result of this method, we have identified the presence of interactions between the oscillating components of the myogenic and the TGF mechanisms at the level of the whole kidney blood flow in normotensive rats. An interaction between these oscillatory components had previously been demonstrated only at the single-nephron level.

Ventilatory load characteristics during ventilatory muscle training

The response of skeletal muscle to training is influenced by both the intensity and nature of the training stimulus. In this study we investigated the characteristics of the ventilatory load applied to the ventilatory muscles during several different modes of ventilatory muscle training. Patients with chronic obstructive pulmonary disease (COPD) performed the following breathing maneuvers: (1) Unloaded hyperpnea (UH), (2) resistive breathing through a fixed orifice (0.5 cm diameter) at frequencies of 15 and 30 breaths/min (RT15, RT30), (3) loaded breathing through a threshold valve set at 30% of the PImax at frequencies of 15 and 30 breaths/min (TT15, TT30), and (4) repetitive maximal inspiratory maneuvers against a closed shutter (PImax). During these maneuvers were recorded airflow and pressures at the month and esophagus, and from these measurements we derived VE and the work of breathing (WOB), tension time index (TTI), and pressure time product (PTP). The VE during UH was significantly higher than all other modes (p < 0.01), whereas the Pesmax was significantly lower during UH than during the resistive and loaded maneuvers (p < 0.01). The WOB did not differ during UH, TT30, and RT30, but was significantly higher in all three modes than at TT15 and RT15 (p < 0.05). During RT30 the TTI was higher than during TT30, TT15, and RT15 (p < 0.05), whereas the TTI during UH was significantly lower than during other maneuvers (p < 0.01). As expected, the highest Pesmax and PTP were found during the PImax maneuver. These data show that important qualitative differences in ventilatory muscle loading can be achieved by means of different devices and breathing strategies.(ABSTRACT TRUNCATED AT 250 WORDS)

Nonlinear analysis of renal autoregulation under broadband forcing conditions

Linear analysis of renal blood flow fluctuations, induced experimentally in rats by broad-band (pseudorandom) arterial blood pressure forcing at various power levels, has been unable to explain fully the dynamics of renal autoregulation at low frequencies. This observation has suggested the possibility of nonlinear mechanisms subserving renal autoregulation at frequencies below 0.2 Hz. This paper presents results of 3rd-order Volterra-Wiener analysis that appear to explain adequately the nonlinearities in the pressure-flow relation below 0.2 Hz in rats. The contribution of the 3rd-order kernel in describing the dynamic pressure-flow relation is found to be important. Furthermore, the dependence of 1st-order kernel waveforms on the power level of broadband pressure forcing indicates the presence of nonlinear feedback (of sigmoid type) based on previously reported analysis of a class of nonlinear feedback systems.

On the efficacy of linear system analysis of renal autoregulation in rats

In order to assess the linearity of the mechanisms subserving renal blood flow autoregulation, broad-band arterial pressure fluctuations at three different power levels were induced experimentally and the resulting renal blood flow responses were recorded. Linear system analysis methods were applied in both the time and frequency domain. In the frequency domain, spectral estimates employing FFT, autoregressive moving average (ARMA) and moving average (MA) methods were used; only the MA model showed two vascular control mechanisms active at 0.02-0.05 Hz and 0.1-0.18 Hz consistent with previous experimental findings [Holstein-Rathlou et al., Amer. J. Physiol., vol. 258, 1990.]. In the time domain, impulse response functions obtained from the MA model indicated likewise the presence of these two vascular control mechanisms, but the ARMA model failed to show any vascular control mechanism at 0.02-0.05 Hz. The residuals (i.e., model prediction errors) of the MA model were smaller than the ARMA model for all levels of arterial pressure forcings. The observed low coherence values and the significant model residuals in the 0.02-0.05 Hz frequency range suggest that the tubuloglomerular feedback (TGF) active in this frequency range is a nonlinear vascular control mechanism. In addition, experimental results suggest that the operation of the TGF mechanism is more evident at low/moderate pressure fluctuations and becomes overwhelmed when the arterial pressure forcing is too high.