Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction

Clinical applications that require extraction and interpretation of physiological signals or waveforms are susceptible to corruption by noise or artifacts. Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Since hemodynamic parameter estimation algorithms often detect events and features from measured ABP waveforms to generate hemodynamic parameters, noise and artifacts integrated into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms. In this article, we propose the use of the Kalman filter and the 4-element Windkessel model with static parameters, arterial compliance C, peripheral resistance R, aortic impedance r, and the inertia of blood L, to represent aortic circulation for generating accurate estimations of ABP waveforms through noise and artifact reduction. Results show the Kalman filter could very effectively eliminate noise and generate a good estimation from the noisy ABP waveform based on the past state history. The power spectrum of the measured ABP waveform and the synthesized ABP waveform shows two similar harmonic frequencies

Intracranial pressure estimation using nonlinear Kalman filtering

The intracranial hypertension is the medical name for high intracranial pressure, a condition that can be dangerous for the patient's health. This increase of intracranial pressure (ICP) may be caused by a congenital or acquired pathology (i.e. the hydrocephalus) as well as by the increasing volume of a cranial mass lesion, among others.\\\\ Considering that some of these pathologies are chronic, it is necessary to develop automatic drainage systems, so the human help is not needed (or at least reduced) in an emergency situation.\\\\ This thesis will deal with the design, implementation and improvement of an algorithm that predicts the intracranial pressure. It could be useful, for example, to able a device drain the cerebrospinal fluid (CSF) automatically when the pressure suddenly increases, common problem in Hydrocephalus patients. The cardiovascular model (R. Mondal et al.) will be used in order to emulate a patient and obtain an intracranial pressure estimation from aortic pressure measurements. Both signals will be processed with a Kalman filter variant (Unscented Kalman Filter), due to the cardiovascular model's non linearities, to obtain a prediction of the ICP.La hipertensión intracraneal (HTIC) es la condición en la que la presión intracraneal crece de manera que puede ser perjudicial para la salud del paciente. Este aumento de PIC (presión intracraneal) puede ser bien dado por una patología congénita o adquirida (como por ejemplo la hidrocefalia) como por una masa creciente fruto de una lesión craneal.\\\\ Teniendo en cuenta que algunas de estas patologías pueden ser crónicas, es necesario diseñar sistemas de drenaje automático de forma que no sea necesaria la intervención humana en caso de emergencia.\\\\ Este trabajo se centrará en diseñar e implementar un algoritmo de predicción de la presión intracraneal. Podría ser útil, por ejemplo, para hacer posible el drenaje de líquido cefalorraquídeo cuando se de un aumento espontáneo de presión, problema habitual en pacientes con Hidrocefalia. Se usará el modelo cardiovascular (R. Mondal et al.) que servirá para emular en modelo, y obtener una estimación de la PIC a partir de medidas de la presión aórtica. Ambas señales serán tratadas con una variante del filtro de Kalman (Unscented Kalman Filter), dada la no linealidad del modelo cardiovascular, para obtener una predicción de la presión intracraneal.La hipertensió intracranial (HTIC) és la condició en què la pressió intracranial creix de manera que pot ser perjudicial per a la salut del pacient. Aquest augment de PIC (pressió intracranial) pot ser tant donat per una patologia congènita o adquirida (per exemple, la hidrocefàlia) com per una massa creixent fruit d'una lesió cranial.\\\\ Tenint en compte que algunes d'aquestes patologies poden ser cròniques, és necessari dissenyar sistemes de drenatge automàtic de manera que no sigui necessària la intervenció humana en cas d'emergència. \\\\ Aquest treball es centrarà a dissenyar i implementar un algoritme de predicció de la pressió intracranial. Podria ser útil, per exemple, per fer possible el drenatge de líquid cefaloraquidi quan es doni un augment sobtat de pressió, problema habitual en pacients amb Hidrocefàlia. S'usarà el model cardiovascular (R. Mondal et al.) que servirà per emular un pacient, i obtenir una estimació de la pressió intracranial a partir de mesures de la pressió aòrtica. Ambdós senyals seran tractats amb una variant del filtre de Kalman (Unscented Kalman Filter), donada la no linearitat del model cardiovascular, per obtenir una predicció de la pressió intracranial

ECG Simulation and Integration of Kalman Filter in Cardio Pediatric Cases

This article will show an overview of the model and simulations of general cardio pediatrics cases. To avoid simulated interference, Kalman and lowpass filter blocks are placed. In pediatric cases normal ECG (Electrocardiogram) curve is a bit different in relation to the middle-age persons. In cardio pediatric is represented especially the ECG curve with higher beats/min. Depending on the age of the child\u27s heart rate is variable. Therefore, identifying irregularities of the heart rate in children should be implemented a particular type of filter to eliminate rough measurement error on measurement signals. The model is obtained computationally shown in the examples of simulation in LabView and Java application programming interfaces. The model realization of the ECG signal is based on a few methods. Therefore, it selected only one method to display a simulated ECG signal. Installation of additional software filters allows us for realistic expectations after hardware integration. The real practical case is provided by a developed system with compiled firmware in the microcontroller. Firmware defines the behavior of the ECG signal after the integration of Kalman and the lowpass filter. Some cardio pediatric cases are processed with the method which can be applied Kalman or lowpass filter

Reduced-order unscented Kalman filter in the frequency domain: Application to computational hemodynamics

Objective: The aim of this work is to assess the potential of the reduced order unscented Kalman filter (ROUKF) in the context of computational hemodynamics, in order to estimate cardiovascular model parameters when employing real patient-specific data. Methods: The approach combines an efficient blood flow solver for one-dimensional networks (for the forward problem) with the parameter estimation problem cast in the frequency space. Namely, the ROUKF is used to correct model parameter after each cardiac cycle, depending on the discrepancies of model outputs with respect to available observations properly mapped into the frequency space. Results: First we validate the filter in frequency domain applying it in the context of a set of experimental measurements for an in vitro model. Second, we perform different numerical experiments aiming at parameter estimation using patient-specific data. Conclusion: Our results demonstrate that the filter in frequency domain allows a faster and more robust parameter estimation, when compared to its time domain counterpart. Moreover, the proposed approach allows to estimate parameters that are not directly related to the network but are crucial for targeting inter-individual parameter variability (e.g., parameters that characterize the cardiac output). Significance: The ROUKF in frequency domain provides a robust and flexible tool for estimating parameters related to cardiovascular mathematical models using in vivo data

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration

Classification and regression of stenosis using an in-vitro pulse wave data set: Dependence on heart rate, waveform and location

Background: Data-based approaches promise to use the information in cardiovascular signals to diagnose cardiovascular diseases. Considerable effort has been undertaken in the field of pulse-wave analysis to harness this information. However, the inverse problem, inferring arterial properties from waveform measurements, is not well understood today. Consequently, uncertainties within the estimation hinder the diagnostic application of such methods. Method: This work contributes a publicly available data set measured at an in-vitro cardiovascular simulator, focusing on a set of input conditions (heart rate, waveform) and stenosis locations. Furthermore, a first attempt is undertaken to perform classification and regression on this data set using standard machine learning methods on features extracted from four peripheral pressure signals. Results: The locations of six different stenoses could be distinguished at high accuracy of 93%, where transfer function-based features outperformed features based solely on signal shape in almost all cases. Furthermore, regression on the stenosis position could be performed with a root mean square error of 2.4 cm along a 20 cm section of the arterial system using a shallow neural network. However, the performance difference between shape and transfer function features was not clear for this task. Conclusion: The data set contains 800 measurements and allows investigating the influence of different heart boundary conditions, such as heart rate and waveform shape, on classification and regression tasks. Extracting features that minimise this influence is a promising way of improving the performance of these tasks

Acoustic cardiac signals analysis: a Kalman filter–based approach

Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss–Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense