Separation of arterial pressure into a nonlinear superposition of solitary waves and a windkessel flow

A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization

In this paper, a multiple linear regression model for estimating the Carotid-to-femoral pulse wave velocity (cf-PWV) from a single non-invasive peripheral pulse wave, namely blood pressure or photoplethysmography, is proposed. The training and testing datasets were extracted from in-silico, publicly available, pulse waves and hemodynamics data. The proposed model relies on a preprocessing and features extraction steps, which are performed using a semi-classical signal analysis (SCSA) method. The obtained results provide more evidence for the feasibility of machine learning and the SCSA method as a smart tool for the efficient assessment of the cf-PWV.

Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method

In this paper we utilize a signal processing tool, which can help physicians and clinical researchers to automate the process of EEG epileptiform spike detection. The semi-classical signal analysis method (SCSA) is a data-driven signal decomposition method developed for pulse-shaped signal characterization. We present an algorithm framework to process and extract features from the patient's EEG recording by deriving the mathematical motivation behind SCSA and quantifying existing spike diagnosis criterion with it. The proposed method can help reduce the amount of data to manually analyse. We have tested our proposed algorithm framework with real data, which guarantees the method's statistical reliability and robustness.

Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure

In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information. The proposed framework predicts the ABP values by processing PPG signals with semi-classical signal analysis (SCSA) method, extracting several categories of features, which reflect the PPG signal morphology variations. Then, regression algorithms are selected for the ABP estimation. The proposed method is evaluated based on a virtual dataset with more than four thousand subjects and MIMIC II database with over eight thousand subjects for model training and testing. Mean average error (MAE) and standard deviation (STD) are evaluated for different machine learning algorithms during the prediction and estimation process. Multiple linear regression (MLR) meets the AAMI standard in terms of estimation accuracy, which proves that the ABP can be accurately estimated in a nonintrusive fashion. Given the easy implementation of the ABP estimation method, we regard that the proposed features and machine learning algorithms for the cuffless estimation of the ABP can potentially provide the means for mobile healthcare equipment to monitor the ABP continuously.

An adaptive delineator for photoplethysmography waveforms

Photoplethysmography (PPG) waveforms are rich in cardiovascular information, and hence, their analysis is significant in the diagnosis and prevention of cardiovascular diseases (CVDs). The second derivative of photoplethysmography (SDPPG) analysis for the accurate detection of significant points in characterising the PPG waveform is challenging. In this paper, a SDPPG analysis algorithm is proposed based on a resampling technique which normalises the signal and ensures the presence of all significant points of interest in all its recurrences. The proposed delineator detects a, b and e waves in SDPPG, which are based on the combined analysis of PPG waveforms and their second derivatives, characterising them beat-by-beat by electrocardiogram (ECG) signals. Experiments have been conducted on 46 PPG signal records, each of 10-s duration with low and varying amplitudes, and regular and irregular heart rhythms for healthy adults, as well as unhealthy and aged patients obtained from the large-scale openly available database PhysioNet. Based on the experiments conducted, it is found that the proposed algorithm performs better than existing methods in terms of sensitivity and positive predictivity with a highest sensitivity of 99.84% with respect to a (onset) and b waves, 99.67% for e waves (dicrotic notch), and 100% of positive predictivity for a and b waves and 99.82% in case of e waves.

Conceptual model of arterial tree based on solitons by compartments

Models define a simplification of reality, which help to understand function. The arterial system has been modeled in many ways: lumped models, tube models and anatomically based distributed models. In this work, arterial segments were modeled as thin nonlinear elastic tubes filled with an incompressible fluid, whose governing dynamics were denoted by the Korteweg and DeVries equation. In order characterize the pressure pulse propagation, a discrete multi-segmented conduit was proposed. Arterial wall mechanical parameters were obtained from existing literature and assigned to each individual segment. The numerical model was developed starting in the aortic arch, and ending at the femoral artery. The main idea of this article was to perform a computational simulation of pressure wave propagation, considered as a solitons combination, along several segments of the arterial tree.

Review of zero-D and 1-D models of blood flow in the cardiovascular system

Background

Zero-dimensional (lumped parameter) and one dimensional models, based on simplified representations of the components of the cardiovascular system, can contribute strongly to our understanding of circulatory physiology. Zero-D models provide a concise way to evaluate the haemodynamic interactions among the cardiovascular organs, whilst one-D (distributed parameter) models add the facility to represent efficiently the effects of pulse wave transmission in the arterial network at greatly reduced computational expense compared to higher dimensional computational fluid dynamics studies. There is extensive literature on both types of models.

Method and Results

The purpose of this review article is to summarise published 0D and 1D models of the cardiovascular system, to explore their limitations and range of application, and to provide an indication of the physiological phenomena that can be included in these representations. The review on 0D models collects together in one place a description of the range of models that have been used to describe the various characteristics of cardiovascular response, together with the factors that influence it. Such models generally feature the major components of the system, such as the heart, the heart valves and the vasculature. The models are categorised in terms of the features of the system that they are able to represent, their complexity and range of application: representations of effects including pressure-dependent vessel properties, interaction between the heart chambers, neuro-regulation and auto-regulation are explored. The examination on 1D models covers various methods for the assembly, discretisation and solution of the governing equations, in conjunction with a report of the definition and treatment of boundary conditions. Increasingly, 0D and 1D models are used in multi-scale models, in which their primary role is to provide boundary conditions for sophisticate, and often patient-specific, 2D and 3D models, and this application is also addressed. As an example of 0D cardiovascular modelling, a small selection of simple models have been represented in the CellML mark-up language and uploaded to the CellML model repository http://models.cellml.org/. They are freely available to the research and education communities.

Conclusion

Each published cardiovascular model has merit for particular applications. This review categorises 0D and 1D models, highlights their advantages and disadvantages, and thus provides guidance on the selection of models to assist various cardiovascular modelling studies. It also identifies directions for further development, as well as current challenges in the wider use of these models including service to represent boundary conditions for local 3D models and translation to clinical application.