Differences in the asymmetry of beat-to-beat fetal heart rate accelerations and decelerations at preterm and term active labor

The fetal autonomic nervous system responds to uterine contractions during active labor as identified by changes in the accelerations and decelerations of fetal heart rate (FHR). Thus, this exploratory study aimed to characterize the asymmetry differences of beat-to-beat FHR accelerations and decelerations in preterm and term fetuses during active labor. In an observational study, we analyzed 10 min of fetal R-R series collected from women during active preterm labor (32–36 weeks of pregnancy, n = 17) and active term labor (38–40 weeks or pregnancy, n = 27). These data were used to calculate the Deceleration Reserve (DR), which is a novel parameter that quantifies the asymmetry of the average acceleration and deceleration capacity of the heart. In addition, relevant multiscale asymmetric indices of FHR were also computed. Lower values of DR, calculated with the input parameters of T = 50 and s = 10, were associated with labor occurring at the preterm condition (p = 0.0131). Multiscale asymmetry indices also confirmed significant (p < 0.05) differences in the asymmetry of FHR. Fetuses during moderate premature labor may experience more decaying R-R trends and a lower magnitude of decelerations compared to term fetuses. These differences of FHR dynamics might be related to the immaturity of the fetal cardiac autonomic nervous system as identified by this system response to the intense uterine activity at active labor.Secretaría de Educación Pública: 511-6/2020-7841, fortalecimiento de cuerpos académicos 2020

Fetal heart rate spectral analysis in raw signals and PRSA-derived curve: normal and pathological fetuses discrimination

Cardiotocography (CTG) is the most common technique for electronic fetal monitoring and consists of the simultaneous recording of fetal heart rate (FHR) and uterine contractions. In analogy with the adult case, spectral analysis of the FHR signal can be used to assess the functionality of the autonomic nervous system. To do so, several methods can be employed, each of which has its strengths and limitations. This paper aims at performing a methodological investigation on FHR spectral analysis adopting 4 different spectrum estimators and a novel PRSA-based spectral method. The performances have been evaluated in terms of the ability of the various methods to detect changes in the FHR in two common pregnancy complications: intrauterine growth restriction (IUGR) and gestational diabetes. A balanced dataset containing 2178 recordings distributed between the 32nd and 38th week of gestation was used. The results show that the spectral method derived from the PRSA better differentiates high-risk pregnancies vs. controls compared to the others. Specifically, it more robustly detects an increase in power percentage within the movement frequency band and a decrease in high frequency between pregnancies at high risk in comparison to those at low risk

Novel Approaches to ECG-Based Modeling and Characterization of Atrial Fibrillation

This thesis deals with signal processing algorithms for analysis of the electrocardiogram (ECG) during atrial fibrillation (AF). Such analysis can be used for diagnosing patients, and for monitoring and predicting their response to various treatment. The thesis comprises an introduction and five papers describing methods for ECG-based modeling and characterization of AF. Paper I--IV deal with methods for characterization of the atrial activity, whereas Paper V deals with modeling of the ventricular response, both problems with the assumption that AF is present. In Paper I, a number of measures characterizing the atrial activity in the ECG, obtained using time-frequency analysis as well as nonlinear methods, are evaluated for their ability to predict spontaneous termination of AF. The AF frequency, i.e, the repetition rate of the atrial fibrillatory waves of the ECG, proved to be a significant factor for discrimination between terminating and non-terminating AF. Noise is a common problem in ECG signals, particularly in long-term ambulatory recordings. Hence, robust algorithms for analysis and characterization are required. In Paper II, a robust method for tracking the AF frequency in noisy signals is presented. The method is based on a hidden Markov model (HMM), which takes the harmonic pattern of the atrial activity into account. Using the HMM-based method, the average RMS error of the frequency estimates at high noise levels was significantly lower compared to existing methods. In Paper III, the HMM-based method is employed for analysis of 24-h ambulatory ECG signals in order to explore circadian variation in AF frequency. Circadian variations reflect autonomic modulation; attenuation or absence of such variations may help to diagnose patients. Methods based on curve fitting, autocorrelation, and joint variation, respectively, are employed to quantify circadian variations, showing that it is present in most patients with long-standing persistent AF, although the short-term variation is considerable. In Paper IV, 24-h ambulatory ECG recordings with paroxysmal and persistent AF are analyzed using an entropy-based method for characterization of the atrial activity. Short segments are classified based on these measures, showing that it is feasible to distinguish between patient with paroxysmal and persistent AF from 10-s ECGs; the average classification rate was above 95%. The ventricular response during AF is mainly determined by the AV nodal blocking of atrial impulses. In Paper V, a new model-based approach for analysis of the ventricular response during AF is proposed. The model integrates physiological properties of the AV node and the atrial fibrillatory rate; the model parameters can be estimated from ECG signals. Results show that ventricular response is sufficiently represented by the estimated model in a majority of the recordings; in 85.7% of the analyzed 30-min segments the model fit was considered accurate, and that changes of AV nodal properties caused by autonomic modulation could be tracked through the estimated model parameters. In summary, the work within this thesis contributes with new methods for non-invasive analysis of AF, which can be used to tailor and evaluate different strategies for AF treatment

Robust Algorithms for Unattended Monitoring of Cardiovascular Health

Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring

Theroetical Analysis of Autonomic Nervous System Effects on Cardiac Elestrophysiology and its Relationship with Arrhythmic Risk

Las enfermedades cardiovasculares representan la principal causa de mortalidad y morbilidad en las sociedades industrializadas. Un porcentaje significativo de las muertes asociadas a estas enfermedades está relacionado con el desarrollo de arritmias cardíacas, siendo éstas definidas como anomalías en el funcionamiento eléctrico del corazón.Tres son los elementos principales que están involucrados en el desarrollo de las arritmias: un sustrato arritmogénico, un desencadenante y factores de modulación. El Sistema Nervioso Autónomo (SNA) es el más relevante de estos factores moduladores.El SNA está compuesto por dos ramas, simpática y parasimpática, que encierta medida actúan de forma antagónica entre sí. La posibilidad de revelar cómo el sistema nervioso simpático modula la actividad ventricular y participa en el desarrollo de arritmias, tal y como se ha observado experimentalmente, podría ser crucial para avanzar en el diseño de nuevas terapias clínicas dirigidas a prevenir o tratar estas anomalías rítmicas.Esta tesis investiga y analiza la variabilidad espacio-temporal de la repolarización ventricular humana, su modulación por el sistema nervioso simpático, los mecanismos que subyacen a incrementos notables en dicha variabilidad y la relación que existe con la generación de arritmias ventriculares. Para ello, se proponen metodología que combinan el procesado de señales ventriculares y el modelado in silico de miocitos ventriculares humanos. Los modelos in silico desarrollados incluyen descripciones teóricas acopladas de la electrofisiología, la dinámica del calcio, el estiramiento mecánico y la señalización -adrenérgica. Para tener en cuenta la variabilidad temporal(latido a latido) de la repolarización, se añade estocasticidad en las ecuaciones que definen la apertura y cierre de los canales iónicos de las principales corrientes activas durante la fase de repolarización del potencial de acción (AP), es decir, durante el retorno de la célula al estado de reposo después de una excitación. Por otro lado, para tener en cuenta la variabilidad espacial (célula a célula) de la repolarización, se construye y calibra una población de modelos representativos de diferentes características celulares utilizando para ellos datos experimentales disponibles. La investigación teórica y computacional de este estudio, combinada con el procesado de señales ventriculares tanto clínicas como experimentales, sienta las bases para futuros estudios que tengan como objetivo mejorar los métodos de estratificación del riesgo arrítmico y guiar la búsqueda de terapias antiarrítmicas más eficaces.En el Capítulo 2, se construye una población de modelos computacionales estocásticos representativos de células ventriculares humanas, los cuales se calibran experimentalmente.Estos modelos combinan la electrofisiología, la mecánica y la señalización-adrenérgica y se utizan para caracterizar de modo teórico la variabilidadespacio-temporal. La calibración de los modelos se basa en rangos experimentales de una serie de marcadores derivados del AP que describen su duración, amplitud y morfología.Mediante el uso de esta población de modelos estocásticos de AP se reproducenlas interacciones descritas experimentalmente entre un tipo particular de variabilidad temporal, asociada con las oscilaciones de baja frecuencia (LF) de la duración del AP (APD), y la variabilidad global latido a latido de la repolarización (BVR) en respuesta a un incremento de la actividad simpática. Además en este capítulo, se han estudiado los mecanismos iónicos que esán detrás de los incrementos simultáneos de ambos fenómenos y se ha demostrado que dichos mecanismos están asociados con la disminución de las corrientes rectificadora de entrada y rectificadora retardada rápida de K+ y a su vez de la corriente de Ca2+ tipo-L. Finalmente, se ha probado que niveles elevados de oscilaciones de baja frecuencia del APD y de BVR en ventrículos enfermosconducen a inestabilidades eléctricas y al desarrollo de eventos arritmogénicos.En el Capítulo 3, se investiga el retardo necesario para la manifestación de las oscilaciones LF del APD, como una forma particular de variabilidad de repolarización, en los miocitos ventriculares en respuesta a la provocación simpática. Mediante el uso de una población calibrada experimentalmente de modelos de AP ventriculares humanos, como en el Capítulo 2, se ha demostrado que esta latencia oscilatoria está asociada con la cinética lenta de fosforilación de la corriente rectificadora retardada lenta de K+ (IKs) en respuesta a la estimulación -adrenérgica. La estimulación previa de los receptores reduce sustancialmente el tiempo requerido para el desarrollo de oscilaciones de LF. Además, se ha demostrado que lapsos de tiempo cortos están íntimamente relacionados con mayores magnitudes oscilatorias del APD, medidas en elCapítulo 3, particularmente en células susceptibles de desarrollar eventos arritmogénicos en respuesta a la estimulación simpática.La calibración experimental de la población de modelos utilizados en los Capítulos 2 y 3 no garantiza que cada modelo de la población construida represente las medidas de un cardiomiocito ventricular humano individual. Es por esta razón que en el Capítulo 4 se desarrolla una metodología novedosa para construir poblaciones computacionales de modelos celulares ventriculares humanos que recapitulen más fielmente las evidencias experimentales disponibles. La metodología propuesta se basa en la formulación de representaciones estado-espacio no lineales y en el uso del filtro de Kalman (UKF) para la estimación de los parámetros y las variables de estado de un modelo AP estocástico subyacente para cada señal de potencial dada como entrada.Las pruebas realizadas sobre series de potencial sintéticas y experimentales demuestran que esta metodología permite establecer una correspondencia entre las trazas AP de entrada y los conjuntos de parámetros del modelo (conductancias de corriente iónicas) y las variables de estado (variables relacionadas con la apertura/cierre de los canales iónicos y concentraciones iónicas intracelulares). A su vez, se ha demostrado que la metodología propuesta es robusta y adecuada para la investigación de la variabilidad espacio-temporal en la repolarización ventricular humana.En el Capítulo 5 se proponen varias mejoras a la metodología desarrollada en elCapítulo 4 para estimar con mayor precisión los parámetros y las variables de estado de los modelos estocásticos de células ventriculares humanas a partir de señales individuales de AP dadas como entradas, y a su vez para reducir el tiempo de convergencia a fin de proporcionar una estimación más rápida. Las mejoras se han basado en el uso combinado del método UKF, presentado en el Capítulo 4, junto con el método Double Greedy Dimension Reduction (DGDR) con generación automática de biomarcadores.Además de estimar las conductancias de las corrientes iónicas en condiciones basales, el enfoque presentado en este capítulo también proporciona el conjunto de niveles de fosforilación inducidos por la estimulación -adrenérgica, contribuyendo así al análisis de patrones de repolarización espacio-temporal con y sin modulación autonómica.En conclusión, esta tesis presenta novedosas metodologías enfocadas hacia lacaracterización de la variabilidad espacio-temporal de la repolarización ventricular humana, el análisis de sus mecanismos subyacentes y la determinaci´ón de la relación entre aumentos en la variabilidad y el mayor riesgo de sufrir arritmias ventriculares y muerte súbita cardíaca. Se desarrollan conjuntos de modelos computacionales estocásticos celulares humanos con representación de la electrofisiología ventricular, la mecánica y la señalización -adrenérgica para analizar la variabilidad global de larepolarización, latido a latido y célula a célula, así como de un tipo particular de variabilidad en forma de oscilaciones de baja frecuencia. Para reproducir fielmente los patrones de variabilidad medidos experimentalmente de manera individual, se proponen metodologías para construir poblaciones de modelos AP ventriculares humanos donde los parámetros y las variables de estado de cada modelo se estiman a partir de una serie de potencial de entrada dada. Estos modelos personalizados abren la puerta a una investigación más robusta de las causas y consecuencias de la variabilidad espacio-temporal de la repolarización ventricular humanCardiovascular diseases represent the main cause of mortality and morbidity in industrialized societies. A significant percentage of deaths associated with these diseases is related to the generation of cardiac arrhythmias, defined as abnormalities in the electrical functioning of the heart. Three major elements are involved in the development of arrhythmias, which include an arrhythmogenic substrate, a trigger and modulating factors. The Autonomic Nervous System (ANS) is the most relevant of these modulators. The ANS is composed of two branches, sympathetic and parasympathetic, which to a certain extent act antagonistically to each other. The possibility of revealing how the sympathetic nervous system modulates the activity of the ventricles (lower heart chambers) and participates in the development of arrhythmias, as reported experimentally, could be crucial to advance in the design of new clinical therapies aimed at preventing or treating these rhythm abnormalities. This thesis investigates spatio-temporal variability of human ventricular repolarization, its modulation by the sympathetic nervous system, the mechanisms behind highly elevated variability and the relationship to the generation of ventricular arrhythmias. To that end, methodologies combining signal processing of ventricular signals and in silico modeling of human ventricular myocytes are proposed. The developed in silico models include coupled theoretical descriptions of electrophysiology, calcium dynamics, mechanical stretch and -adrenergic signaling. To account for temporal (beat-to-beat) repolarization variability, stochasticity is added into the equations defining the gating of the ion channels of the main currents active during action potential (AP) repolarization, i.e. during the return of the cell to the resting state after an excitation. To account for spatial (cell-to-cell) repolarization variability, a population of models representative of different cellular characteristics are constructed and calibrated based on available experimental data. The theoretical computational research of this study, combined with the processing of clinical and experimental ventricular signals, lays the ground for future studies aiming at improving arrhythmic risk stratification methods and at guiding the search for more efficient anti-arrhythmic therapies. In Chapter 2, a population of experimentally-calibrated stochastic human ventricular computational cell models coupling electrophysiology, mechanics and -adrenergic signaling are built to investigate spatio-temporal variability. Model calibration is based on experimental ranges of a number of AP-derived markers describing AP duration, amplitude and shape. By using the proposed population of stochastic AP models, the experimentally reported interactions between a particular type of temporal variability associated with low-frequency (LF) oscillations of AP duration (APD) and overall beat-to-beat variability of repolarization (BVR) in response to enhanced sympathetic activity are reproduced. Ionic mechanisms behind correlated increments in both phenomena are investigated and found to be related to downregulation of the inward and rapid delayed rectifier K+ currents and the L-type Ca2+ current. Concomitantly elevated levels of LF oscillations of APD and BVR in diseased ventricles are shown to lead to electrical instabilities and arrhythmogenic events. In Chapter 3, the time delay for manifestation of LF oscillations of APD, as a particular form of repolarization variability, is investigated in ventricular myocytes in response to sympathetic provocation. By using an experimentally-calibrated population of human ventricular AP models, as in Chapter 2, this oscillatory latency is demonstrated to be associated with the slow phosphorylation kinetics of the slow delayed rectifier K+ current IKs in response to -adrenergic stimulation. Prior stimulation of -adrenoceptors substantially reduces the time required for the development of LF oscillations. In addition, short time lapses are shown to be related to large APD oscillatory magnitudes, as measured in Chapter 2, particularly in cells susceptible to develop arrhythmogenic events in response to sympathetic stimulation. The experimental calibration of the population of models used in Chapter 2 and Chapter 3, despite ensuring that simulated population measurements lie within experimental limits, does not guarantee that each model in the constructed population represents the experimental measurements of an individual human ventricular cardiomyocyte. It is for that reason that in Chapter 4 a novel methodology is developed to construct computational populations of human ventricular cell models that more faithfully recapitulate individual available experimental evidences. The proposed methodology is based on the formulation of nonlinear state-space representations and the use of the Unscented Kalman Filter (UKF) to estimate parameters and state variables of an underlying stochastic AP model given any input voltage trace. Tests performed over synthetic and experimental voltage traces demonstrate that this methodology successfully renders a one-to-one match between input AP traces and sets of model parameters (ionic current conductances) and state variables (ionic gating variables and intracellular concentrations). The proposed methodology is shown to be robust for investigation of spatio-temporal variability in human ventricular repolarization. Chapter 5 improves the methodology developed in Chapter 4 to more accurately estimate parameters and state variables of stochastic human ventricular cell models from individual input voltage traces and to reduce the converge time so as to provide faster estimation. The improvements are based on the combined use of the UKF method of Chapter 4 together with Double Greedy Dimension Reduction (DGDR) method with automatic generation of biomarkers. Additionally, on top of estimating ionic current conductances at baseline conditions, the approach presented in this chapter also provides a set of -adrenergic-induced phosphorylation levels, thus contributing to the analysis of spatio-temporal repolarization patterns with and without autonomic modulation. In conclusion, this thesis presents novel methodologies for characterization of spatio-temporal variability of human ventricular repolarization, for dissection of its underlying mechanisms and for ascertainment of the relationship between elevated variability and increased risk for ventricular arrhythmias and sudden cardiac death. Sets of stochastic human computational cell models with representation of ventricular electrophysiology, mechanics and -adrenergic signaling are developed and used to analyze overall beat-to-beat and cell-to-cell repolarization variability as well as a particular type of variability in the form of LF oscillations. To faithfully reproduce experimentally measured variability patterns in a one-to-one manner, methodologies are proposed to construct populations of human ventricular AP models where the parameters and state variables of a model are estimated from a given input voltage trace. These personalized models open the door to more robust investigation of the causes and consequences of spatio-temporal variability of human ventricular repolarization.<br /

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Assessment Of Blood Pressure Regulatory Controls To Detect Hypovolemia And Orthostatic Intolerance

Regulation of blood pressure is vital for maintaining organ perfusion and homeostasis. A significant decline in arterial blood pressure could lead to fainting and hypovolemic shock. In contrast to young and healthy, people with impaired autonomic control due to aging or disease find regulating blood pressure rather demanding during orthostatic challenge. This thesis performed an assessment of blood pressure regulatory controls during orthostatic challenge via traditional as well as novel approaches with two distinct applications 1) to design a robust automated system for early identification of hypovolemia and 2) to assess orthostatic tolerance in humans. In chapter 3, moderate intensity hemorrhage was simulated via lower-body negative pressure (LBNP) with an aim to identify moderate intensity hemorrhage (-30 and -40 mmHg LBNP) from resting baseline. Utilizing features extracted from common vital sign monitors, a classification accuracy of 82% and 91% was achieved for differentiating -30 and -40 mmHg LBNP, respectively from baseline. In chapter 4, cause-and-effect relationship between the representative signals of the cardiovascular and postural systems to ascertain blood pressure homeostasis during standing was performed. The degree of causal interaction between the two systems, studied via convergent cross mapping (CCM), showcased the existence of a significant bi-directional interaction between the representative signals of two systems to regulate blood pressure. Therefore, the two systems should be accounted for jointly when addressing physiology behind fall. Further, in chapter 5, the potential of artificial gravity (2-g) induced via short-arm human centrifuge at feet towards evoking blood pressure regulatory controls analogous to standing was investigated. The observation of no difference in the blood pressure regulatory controls, during 2-g centrifugation compared to standing, strongly supported the hypothesis of artificial hypergravity for mitigating cardiovascular deconditioning, hence minimizing post-flight orthostatic intolerance

Advances in point process filters and their application to sympathetic neural activity

This thesis is concerned with the development of techniques for analyzing the sequences of stereotypical electrical impulses within neurons known as spikes. Sequences of spikes, also called spike trains, transmit neural information; decoding them often provides details about the physiological processes generating the neural activity. Here, the statistical theory of event arrivals, called point processes, is applied to human muscle sympathetic spike trains, a peripheral nerve signal responsible for cardiovascular regulation. A novel technique that uses observed spike trains to dynamically derive information about the physiological processes generating them is also introduced. Despite the emerging usage of individual spikes in the analysis of human muscle sympathetic nerve activity, the majority of studies in this field remain focused on bursts of activity at or below cardiac rhythm frequencies. Point process theory applied to multi-neuron spike trains captured both fast and slow spiking rhythms. First, analysis of high-frequency spiking patterns within cardiac cycles was performed and, surprisingly, revealed fibers with no cardiac rhythmicity. Modeling spikes as a function of average firing rates showed that individual nerves contribute substantially to the differences in the sympathetic stressor response across experimental conditions. Subsequent investigation of low-frequency spiking identified two physiologically relevant frequency bands, and modeling spike trains as a function of hemodynamic variables uncovered complex associations between spiking activity and biophysical covariates at these two frequencies. For example, exercise-induced neural activation enhances the relationship of spikes to respiration but does not affect the extremely precise alignment of spikes to diastolic blood pressure. Additionally, a novel method of utilizing point process observations to estimate an internal state process with partially linear dynamics was introduced. Separation of the linear components of the process model and reduction of the sampled space dimensionality improved the computational efficiency of the estimator. The method was tested on an established biophysical model by concurrently computing the dynamic electrical currents of a simulated neuron and estimating its conductance properties. Computational load reduction, improved accuracy, and applicability outside neuroscience establish the new technique as a valuable tool for decoding large dynamical systems with linear substructure and point process observations