Thomas J. Mullen

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. Analyses 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.

System identification: a multi-signal approach for probing neural cardiovascular regulation

Short-term, beat-to-beat cardiovascular variability reflects the dynamic interplay between ongoing perturbations to the circulation and the compensatory response of neurally mediated regulatory mechanisms. This physiologic information may be deciphered from the subtle, beat-to-beat variations by using digital signal processing techniques. While single signal analysis techniques (e.g., power spectral analysis) may be employed to quantify the variability itself, the multi-signal approach of system identification permits the dynamic characterization of the neural regulatory mechanisms responsible for coupling the variability between signals. In this review, we provide an overview of applications of system identification to beat-to-beat variability for the quantitative characterization of cardiovascular regulatory mechanisms. After briefly summarizing the history of the field and basic principles, we take a didactic approach to describe the practice of system identification in the context of probing neural cardiovascular regulation. We then review studies in the literature over the past two decades that have applied system identification for characterizing the dynamical properties of the sinoatrial node, respiratory sinus arrhythmia, and the baroreflex control of sympathetic nerve activity, heart rate and total peripheral resistance. Based on this literature review, we conclude by advocating specific methods of practice and that future research should focus on nonlinear and time-varying behaviors, validation of identification methods, and less understood neural regulatory mechanisms. Ultimately, we hope that this review stimulates such future investigations by both new and experienced system identification researchers.

Body Surface Laplacian Mapping of Cardiac Excitation in Intact Pigs

Relating body surface electrocardiographic signals to regional myocardial events has been a major effort in cardiac electrophysiology. Convenlional electrocardiographic means do not provide sufficient spatial resolution to resolve distributed cardiac electrical activity. The purpose of this investigation was to evaluate and study the validity of a new technique—body surface Lapiacian mapping—in a well‐controlled experimental setting, and to test the hypothesis that the body surface Laplacian map (BSLM) can resolve normal and abnormal ventricular depolarization patterns and localize the initial site of ventricular depolarization with high spatial resolution. In this study. BSLMs were constructed from direct measurements of the surface Laplacian of the body surface potentials using an array of 64 concentric bipolar Laplacian electrodes. BSLMs were compared to body surface potential maps (BSPMs) during normal and ectopic ventricular activation in intact anesthetized pigs. The BSLM displayed highly localized images of cardiac electrical activity, indicating its ability to resolve myacardiai events. The BSLM in pigs identified the pacemaking focus overlying the known location of ihe epicardial pacing electrode, and imaged the activation sequence associated with exogenous ventricular pacing. In contrast, in all cases the BSPM revealed a diffuse distribution of activity over the chest. The present results suggest that the BSLM provides sufficient spatial resolution to relate body surface recordings to regional myocardial events and is able to detect ventricular depolarization patterns with greater resolution than the conventional BSPM.

System identification of closed-loop cardiovascular control mechanisms: diabetic autonomic neuropathy

We applied cardiovascular system identification (CSI) to characterize closed-loop cardiovascular regulation in patients with diabetic autonomic neuropathy (DAN). The CSI method quantitatively analyzes beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure (ABP), and instantaneous lung volume (ILV) to characterize four physiological coupling mechanisms, two of which are autonomically mediated (the heart rate baroreflex and the coupling of respiration, measured in terms of ILV, to heart rate) and two of which are mechanically mediated (the coupling of ventricular contraction to the generation of the ABP wavelet and the coupling of respiration to ABP). We studied 37 control and 60 diabetic subjects who were classified as having minimal, moderate, or severe DAN on the basis of standard autonomic tests. The autonomically mediated couplings progressively decreased with increasing severity of DAN, whereas the mechanically mediated couplings were essentially unchanged. CSI identified differences between the minimal DAN and control groups, which were indistinguishable based on the standard autonomic tests. CSI may provide a powerful tool for assessing DAN.

Human heart rate variability relation is unchanged during motion sickness.

In a study of 18 human subjects, we applied a new technique, estimation of the transfer function between instantaneous lung volume (ILV) and instantaneous heart rate (HR), to assess autonomic activity during motion sickness. Two control recordings of ILV and electrocardiogram (ECG) were made prior to the development of motion sickness. During the first, subjects were seated motionless, and during the second they were seated rotating sinusoidally about an earth vertical axis. Subjects then wore prism goggles that reverse the left-right visual field and performed manual tasks until they developed moderate motion sickness. Finally, ILV and ECG were recorded while subjects maintained a relatively constant level of sickness by intermittent eye closure during rotation with the goggles. Based on analyses of ILV to HR transfer functions from the three conditions, we were unable to demonstrate a change in autonomic control of heart rate due to rotation alone or due to motion sickness. These findings do not support the notion that moderate motion sickness is manifested as a generalized autonomic response.

Transient cardio-respiratory responses to visually induced tilt illusions

Although the orthostatic cardio-respiratory response is primarily mediated by the baroreflex, studies have shown that vestibular cues also contribute in both humans and animals. We have demonstrated a visually mediated response to illusory tilt in some human subjects. Blood pressure, heart and respiration rate, and lung volume were monitored in 16 supine human subjects during two types of visual stimulation, and compared with responses to real passive whole body tilt from supine to head 80 degrees upright. Visual tilt stimuli consisted of either a static scene from an overhead mirror or constant velocity scene motion along different body axes generated by an ultra-wide dome projection system. Visual vertical cues were initially aligned with the longitudinal body axis. Subjective tilt and self-motion were reported verbally. Although significant changes in cardio-respiratory parameters to illusory tilts could not be demonstrated for the entire group, several subjects showed significant transient decreases in mean blood pressure resembling their initial response to passive head-up tilt. Changes in pulse pressure and a slight elevation in heart rate were noted. These transient responses are consistent with the hypothesis that visual-vestibular input contributes to the initial cardiovascular adjustment to a change in posture in humans. On average the static scene elicited perceived tilt without rotation. Dome scene pitch and yaw elicited perceived tilt and rotation, and dome roll motion elicited perceived rotation without tilt. A significant correlation between the magnitude of physiological and subjective reports could not be demonstrated.

Statistical accuracy of a moving equivalent dipole method to identify sites of origin of cardiac electrical activation

While radio frequency (RF) catheter ablation (RCA) procedures for treating ventricular arrhythmias have evolved significantly over the past several years, the use of RCA has been limited to treating slow ventricular tachycardias (VTs). In this paper, we present preliminary results from computer and animal studies to evaluate the accuracy of an algorithm that uses the single equivalent moving dipole (SEMD) model in an infinite homogeneous volume conductor to guide the RF catheter to the site of origin of the arrhythmia. Our method involves measuring body surface electrocardiographic (ECG) signals generated by arrhythmic activity and by bipolar current pulses emanating from a catheter tip, and representing each of them by a SEMD model source at each instant of the cardiac cycle, thus enabling rapid repositioning of the catheter tip requiring only a few cycles of the arrhythmia. We found that the SEMD model accurately reproduced body surface ECG signals with a correlation coefficients >0.95. We used a variety of methods to estimate the uncertainty of the SEMD parameters due to measurement noise and found that at the time when the arrhythmia is mostly localized during the cardiac cycle, the estimates of the uncertainty of the spatial SEMD parameters (from ECG signals) are between 1 and 3 mm. We used pacing data from spatially separated epicardial sites in a swine model as surrogates for focal ventricular arrhythmic sources and found that the spatial SEMD estimates of the two pacing sites agreed with both their physical separation and orientation with respect to each other. In conclusion, our algorithm to estimate the SEMD parameters from body surface ECG can potentially be a useful method for rapidly positioning the catheter tip to the arrhythmic focus during an RCA procedure.

Cardiovascular System Identification

Cardiovascular System Identification (CSI) provides a novel, non-invasive technology for the quantitative characterization of cardiovascular regulation in an individual subject. CSI quantifies physiologic coupling mechanisms by mathematically analyzing the relationship between ongoing second-to-second fluctuations in non-invasively measured physiologic variables such as heart rate and arterial blood pressure (ABP). In this way, important physiologic coupling mechanisms, such as the heart rate baroreflex and other measures of autonomic function, can be quantitatively described. By characterizing each of the coupling mechanisms, one can construct an individualized model of closed-loop cardiovascular regulation for each subject. The CSI approach may be adapted to the number of physiologic signals available for analysis. In general, if one measures n physiologic variables from a subject, then n(n−1) causal couplings can be identified. Thus, as more signals are made available, more physiologic coupling mechanisms may be characterized resulting in a more detailed model of closed-loop cardiovascular regulation.