Development and characterization of silicone-based tissue phantoms for pulse oximeter performance testing.

Significance: Pulse oximeter measurements are commonly relied upon for managing patient care and thus often require human testing before they can be legally marketed. Recent clinical studies have also identified disparities in their measurement of blood oxygen saturation by race or skin pigmentation.

Aim: The development of a reliable bench-top performance test method based on tissue-simulating phantoms has the potential to facilitate pre-market assessment and the development of more accurate and equitable devices.

A Lumped-Parameter Model of the Cardiovascular System Response for Evaluating Automated Fluid Resuscitation Systems.

Physiological closed-loop controlled (PCLC) medical devices, such as those designed for blood pressure regulation, can be tested for safety and efficacy in real-world clinical settings. However, relying solely on limited animal and clinical studies may not capture the diverse range of physiological conditions. Credible mathematical models can complement these studies by allowing the testing of the device against simulated patient scenarios.

Tissue mimicking materials and finger phantom design for pulse oximetry.

Pulse oximetry represents a ubiquitous clinical application of optics in modern medicine. Recent studies have raised concerns regarding the potential impact of confounders, such as variable skin pigmentation and perfusion, on blood oxygen saturation measurement accuracy in pulse oximeters. Tissue-mimicking phantom testing offers a low-cost, well-controlled solution for characterizing device performance and studying potential error sources, which may thus reduce the need for costly in vivo trials.

Clinical Evaluation of the ECG Noise Extraction Tool as a Component of ECG Analysis Algorithms Evaluation.

Objective: The aim of this work is to demonstrate the performance of the ECG noise extraction tool (ECGNExT) which provides estimates of ECG noise that are not significantly different from the inherent noise in an ECG generated by motion artifacts and other sources. In addition, this paper elaborates on use of ECGNExT in an algorithm evaluation context comparing two QRS detection algorithms.

Methods: 140 simultaneous pairs of clean ECGs and ECGs corrupted with motion-induced noise from 29 participants under five different and separate motion conditions were collected and analyzed.

AFE-GAN: Synthesizing Electrocardiograms with Atrial Fibrillation Characteristics Using Generative Adversarial Networks.

Labeled ECG data in diseased state are, however, relatively scarce due to various concerns including patient privacy and low prevalence. We propose the first study in its kind that synthesizes atrial fibrillation (AF)-like ECG signals from normal ECG signals using the AFE-GAN, a generative adversarial network. Our AFE-GAN adjusts both beat morphology and rhythm variability when generating the atrial fibrillation-like ECG signals.

Mock circulatory loop generated database for dynamic characterization of pressure-based cardiac output monitoring systems.

Pulse contour cardiac output monitoring systems allow real-time and continuous estimation of hemodynamic variables such as cardiac output (CO) and stroke volume variation (SVV) by analysis of arterial blood pressure waveforms. However, evaluating the performance of CO monitoring systems to measure the small variations in these variables sometimes used to guide fluid therapy is a challenge due to limitations in clinical reference methods. We developed a non-clinical database as a tool for assessing the dynamic attributes of pressure-based CO monitoring systems, including CO response time and CO and SVV resolutions.

Monitoring Respiratory Motion With Wi-Fi CSI: Characterizing Performance and the BreatheSmart Algorithm.

Respiratory motion (i.e., motion pattern and rate) can provide valuable information for many medical situations.

Development and validation of a mathematical model of heart rate response to fluid perturbation.

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient's condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain pre-clinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial.

A Mathematical Model for Simulation of Vasoplegic Shock and Vasopressor Therapy.

Objective: To develop a high-fidelity mathematical model intended to replicate the cardiovascular (CV) responses of a critically ill patient to vasoplegic shock-induced hypotension and vasopressor therapy.

Methods: The mathematical model consists of a lumped-parameter CV physiology model with baroreflex modulation feedback and a phenomenological dynamic dose-response model of a vasopressor. The adequacy of the proposed mathematical model was investigated using an experimental dataset acquired from 10 pigs receiving phenylephrine (PHP) therapy after vasoplegic shock induced via sodium nitroprusside (SNP).

A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography.

Cardiovascular disease is the leading cause of death globally. To provide continuous monitoring of blood pressure (BP), a parameter which has shown to improve health outcomes when monitored closely, many groups are trying to measure blood pressure via noninvasive photoplethysmography (PPG). However, the PPG waveform is subject to variation as a function of patient-specific and device factors and thus a platform to enable the evaluation of these factors on the PPG waveform and subsequent hemodynamic parameter prediction would enable device development.

Model-based approach to investigate equipment-induced error in pressure-waveform derived hemodynamic measurements.

Advanced hemodynamic monitoring systems have provided less invasive methods for estimating pressure-derived measurements such as pressure-derived cardiac output (CO) measurements. These devices apply algorithms to arterial pressure waveforms recorded via pressure recording components that transmit the pressure signal to a pressure monitor. While standards have been developed for pressure monitoring equipment, it is unclear how the equipment-induced error can affect secondary measurements from pressure waveforms.

Credibility Assessment of a Subject-Specific Mathematical Model of Blood Volume Kinetics for Prediction of Physiological Response to Hemorrhagic Shock and Fluid Resuscitation.

Subject-specific mathematical models for prediction of physiological parameters such as blood volume, cardiac output, and blood pressure in response to hemorrhage have been developed. studies using these models may provide an effective tool to generate pre-clinical safety evidence for medical devices and help reduce the size and scope of animal studies that are performed prior to initiation of human trials. To achieve such a goal, the credibility of the mathematical model must be established for the purpose of pre-clinical testing.

Simulating Radial Pressure Waveforms with a Mock Circulatory Flow Loop to Characterize Hemodynamic Monitoring Systems.

Purpose: Mock circulatory loops (MCLs) can reproducibly generate physiologically relevant pressures and flows for cardiovascular device testing. These systems have been extensively used to characterize the performance of therapeutic cardiac devices, but historically MCLs have had limited use for assessing patient monitoring systems. Here, we adapted an MCL to include peripheral components and evaluated its utility for qualitative and quantitative benchtop testing of hemodynamic monitoring devices.

Simulating Study Design Choice Effects on Observed Performance of Predictive Patient Monitoring Alarm Algorithms.

There are multiple study design choices to be selected in order to perform evaluations of predictive patient monitoring algorithms related to the event and true positive alarm definitions (e.g., how far ahead of the event is a true positive alarm).

Accuracy assessment methods for physiological model selection toward evaluation of closed-loop controlled medical devices.

Physiological closed-loop controlled (PCLC) medical devices are complex systems integrating one or more medical devices with a patient's physiology through closed-loop control algorithms; introducing many failure modes and parameters that impact performance. These control algorithms should be tested through safety and efficacy trials to compare their performance to the standard of care and determine whether there is sufficient evidence of safety for their use in real care setting. With this aim, credible mathematical models have been constructed and used throughout the development and evaluation phases of a PCLC medical device to support the engineering design and improve safety aspects.

Technical considerations for evaluating clinical prediction indices: a case study for predicting code blue events with MEWS.

There have been many efforts to develop tools predictive of health deterioration in hospitalized patients, but comprehensive evaluation of their predictive ability is often lacking to guide implementation in clinical practice. In this work, we propose new techniques and metrics for evaluating the performance of predictive alert algorithms and illustrate the advantage of capturing the timeliness and the clinical burden of alerts through the example of the modified early warning score (MEWS) applied to the prediction of in-hospital code blue events..

Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data.

Individualizing physiological models to a patient can enable patient-specific monitoring and treatment in critical care environments. However, this task often presents a unique "practical identifiability" challenge due to the conflict between model complexity and data scarcity. Regularization provides an established framework to cope with this conflict by compensating for data scarcity with prior knowledge.

A framework to characterize the performance of early warning index alarm systems for patient monitoring.

In [Scully, C.G., and Daluwatte, C.

Evaluation of Fluid Resuscitation Control Algorithms via a Hardware-in-the-Loop Test Bed.

Objective: This paper presents a hardware-in-the-loop (HIL) testing platform for evaluating the performance of fluid resuscitation control algorithms. The proposed platform is a cyber-physical system that integrates physical devices with computational models and computer-based algorithms.

Methods: The HIL test bed is evaluated against in silico and in vivo data to ensure the hemodynamic variables are appropriately predicted in the proposed platform.

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine.

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used.

Applying a Computational Model Credibility Framework to Physiological Closed-loop Controlled Medical Device Testing.

Physiological closed-loop controlled medical devices are safety-critical systems that combine patient monitors with therapy delivery devices to automatically titrate therapy to meet a patient's current need. Computational models of physiological systems can be used to test these devices and generate pre-clinical evidence of safety and performance before using the devices on patients. The credibility, utility, and acceptability of such model-based test results will depend on, among other factors, the computational model used.

A method to extract realistic artifacts from electrocardiogram recordings for robust algorithm testing.

Objective: Recordings of signal noise and artifacts can be added to clean electrocardiogram (ECG) records to assess the performance of ECG and arrhythmia analysis algorithms in the presence of noise. We present a method to estimate device-specific signal noise and artifacts from ECG records. This method can be applied to obtain noise estimates from healthy subjects on any ECG lead, allowing a simple device-specific recording.

Control-oriented physiological modeling of hemodynamic responses to blood volume perturbation.

This paper presents a physiological model to reproduce hemodynamic responses to blood volume perturbation. The model consists of three sub-models: a control-theoretic model relating blood volume response to blood volume perturbation; a simple physics-based model relating blood volume to stroke volume and cardiac output; and a phenomenological model relating cardiac output to blood pressure. A unique characteristic of this model is its balance for simplicity and physiological transparency.

Multivariate physiological recordings in an experimental hemorrhage model.

In this paper we describe a data set of multivariate physiological measurements recorded from conscious sheep ( = 8; 37.4 ± 1.1 kg) during hemorrhage.