Hybrid Mock Circulatory Loop Simulation of Extreme Cardiac Events

OBJECTIVE

This paper presents preliminary methods of incorporating the pathological conditions of cardiac arrhythmias and valvular stenosis in hybrid mock circulation loop (hMCL) operation for the enhanced verification and validation of mechanical circulatory support devices such as VADs.

METHODS

The MGH/MF Waveform datasets from PhysioNet database (including both nominal and clinically diagnosed arrhythmic ECG measurements) as well as cardiovascular system model updates are used to recreate arrhythmic events and valvular stenosis in vitro.

RESULTS

Preliminary results show the hMCL can recreate each tested cardiac event within 2% and 4% mean error for reference pressure tracking in the aortic and left ventricular pressure chambers, respectively. Further, frequency spectrum analysis comparisons using the magnitude-squared coherence analysis shows close alignment between measured arrhythmic and hMCL realized pressure frequency content.

CONCLUSION

The generation of cardiac arrhythmias and valvular stenosis around a VAD via both model and acute measurement based methods was achieved.

SIGNIFICANCE

Pathological conditions such as cardiac arrhythmias and valvular stenosis are limited in documentation despite the large percentage of patients who experience these events. This paper provides a means to begin incorporating these events into hardware-in-the-loop mock circulatory systems for next generation VAD validation and verification.

Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset

Disease pathogenesis, a type of domain knowledge about biological mechanisms leading to diseases, has not been adequately encoded in machine-learning-based medical diagnostic models because of the inter-patient variabilities and complex dependencies of the underlying pathogenetic mechanisms. We propose 1) a novel pathogenesis probabilistic graphical model (PPGM) to quantify the dynamics underpinning patient-specific data and pathogenetic domain knowledge, 2) a Bayesian-based inference paradigm to answer the medical queries and forecast acute onsets. The PPGM model consists of two components: a Bayesian network of patient attributes and a temporal model of pathogenetic mechanisms. The model structure was reconstructed from expert knowledge elicitation, and its parameters were estimated using Variational Expectation-Maximization algorithms. We benchmarked our model with two well-established hidden Markov models (HMMs) - Input-output HMM (IO-HMM) and Switching Auto-Regressive HMM (SAR-HMM) - to evaluate the computational costs, forecasting performance, and execution time. Two case studies on Obstructive Sleep Apnea (OSA) and Paroxysmal Atrial Fibrillation (PAF) were used to validate the model. While the performance of the parameter learning step was equivalent to those of IO-HMM and SAR-HMM models, our model forecasting ability was outperforming those two models. The merits of the PPGM model are its representation capability to capture the dynamics of pathogenesis and perform medical inferences and its interpretability for physicians. The model has been used to perform medical queries and forecast the acute onset of OSA and PAF. Additional applications of the model include prognostic healthcare and preventive personalized treatments.

Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care

Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time. While many algorithms exist to assist in monitoring these patients, they often lack accuracy and specificity, leading to alarm fatigue among healthcare practitioners. In this study we propose a multimodal approach that incorporates salient physiological signals and EHR data to predict the onset of hemodynamic decompensation. A retrospective dataset of patients recovering from cardiac surgery was created and used to train predictive models. Advanced signal processing techniques were employed to extract complex features from physiological waveforms, while a novel tensor-based dimensionality reduction method was used to reduce the size of the feature space. These methods were evaluated for predicting the onset of decompensation at varying time intervals, ranging from a half-hour to 12 h prior to a decompensation event. The best performing models achieved AUCs of 0.87 and 0.80 for the half-hour and 12-h intervals respectively. These analyses evince that a multimodal approach can be used to develop clinical decision support systems that predict adverse events several hours in advance.

Controlled infusion of intravenous cardiac drugs using global optimization

OBJECTIVES:

The objective of the study is to develop an automatic drug infusion control system during cardiovascular surgery.

MATERIALS AND METHODS:

Based on the clinical drug dosage analysis, the modeling of cardiovascular system with baroreceptor model is mathematically modeled using compartmental approach, considering the relationship between the volume and flow rate of blood during each heartbeat. This model is then combined with drug modeling of noradrenaline and nitroglycerine by deriving the volume and drug mass concentration equations, based on pharmacokinetics and pharmacodynamics of the drugs. The closed-loop patient models are derived from the open-loop data obtained from the physiology-drug model with covariate as age. The proportional-integral controller is designed based on optimal values obtained from bacterial foraging-oriented particle swarm optimization algorithm. The controllers are implemented individually for each control variable such as aortic pressure and cardiac output (CO), irrespective of varying weights based on the relative gain array analysis which depicts the maximum influence of cardiac drugs on control variables.

RESULTS:

The physiology-drug model output responses are simulated using MATLAB. The controlled responses of aortic pressure and CO with infusion rate of cardiac drugs are obtained. The robustness of the controller is checked by introducing variations in cardiovascular model parameters. The efficiency of the controller during normal and abnormal conditions is compared using time domain analysis.

CONCLUSIONS:

The controller design was efficient and can be further improved by designing switching-based controllers.

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

OBJECTIVE

To develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG).

METHODS

A closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed.

RESULTS

Simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M, and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition.

CONCLUSION

The proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology.

SIGNIFICANCE

This paper provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present paper considers an average human body and can potentially be extended to include variability among individuals.

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to the blood flow dynamics leads to the occurrence of cardiac arrhythmias in the cardiac system. In this work, estimation of cardiac output (CO) parameter using blood flow rate analysis is carried out, which is a vital parameter to identify the subjects with left- ventricular arrhythmias (LVA). In particular, LVA is a resultant component of characteristic changes in blood rheology (blood flow rate). The CO is an intrinsic parameter derived from the stroke volume (SV) characterized by end-diastolic/systolic volumes (EDV/ESV) and heart rate. The pumping of blood from left ventricle (LV) reconciles in to R-R intervals depicted on ECG, which are used for heart rate estimation. The deviation from the nominal values of CO implies that, the subject is more prone to LVA. Further, the identification of subjects with LVA is accomplished by computing the features from the ECG signals. The proposed Feature Ranking Score (FRS) algorithm employs different statistical parameters to label the score of the extracted features. The feature score enables the selection optimal features for classification. The optimal features are further given to the Least Square- Support Vector Machine (LS-SVM) classifier for training and testing phases. The signals are acquired from public domain MIT-BIH arrhythmia database, used for validating the proposed technique for identifying the LVA using blood flow.

Analysis of central venous pressure (CVP) signals using mathematical methods

Central venous pressure (CVP) is an important clinical parameter for physicians but only the absolute CVP value is typically monitored in the intensive care unit (ICU). In this study, we propose a novel mathematical method to present and analyze CVP signals. A total of 44 suitable samples were chosen from a total of 65 collected in an ICU. Pre-processing of the samples included rate reduction and digital filtering. The statistical features of time and frequency domain, wavelet, and empirical mode decomposition of these signals were extracted. We found no significant difference among the CVP signals regarding sex, smoking, coronary disease, and respiration mode of the samples.