Spatio-temporal Spread Variation through Myocardium in Supply and Demand Ischemia.

In this paper, we investigate spatio-temporal progression of Myocardial ischemia (MI) and propose a metric for quantifying ischemic manifestation using cardiac activation time. Spatio-temporal spread is separately analyzed and compared for two different types of ischemia, namely 'Demand' and 'Supply' ischemia. This is done for both surface progression, along the epicardial surface as well as volume progression, along the three sub-myocardial layers. Cardiac activation time or depolarization time is computed from cardiac surface potential using a combined spatio-temporal derivative function. Ischemic zones in the cardiac surface is computed using Principal Component Analysis (PCA) and eigen vector projection of the depolarization time. Spatio-temporal ischemic spread analysis revealed different ischemic initiation and manifestation pattern for Demand and Supply ischemia, both in surface and volume progression.Clinical relevance Activation time based ischemic progression metric can serve as an alternate marker for ischemia detection and can provide more intuitive understanding on the pathological progression, and in turn assist in developing methods to prevent cell damage due to ischemic progression.

Numerical Analysis on Effect of Coronary Supply-Demand Equilibrium on Varying Coronary Blockage and Stress Conditions.

In this paper, we present a computational fluid dynamic (CFD) analysis to capture the effect of physical stress and stenosis severity in coronary arteries leading to changes in coronary supply demand oxygen equilibrium. We propose a coupled Od-3d coronary vessel model to predict the variation in flow dynamics of coronary as well as arterial system, modeled using an in-silico model replicating cardiovascular hemodynamics. CFD simulation were solved using subject specific CT scan for coronary and arterial flow and pressure along with metrics related to arterial wall shear stress. Simulations were performed for three heart rates (75, 90 and 120 bpm) and four stenosis states representing different stages of Coronary artery disease (CAD) namely healthy, 50%, 75%, 90% blockage in left anterior descending artery (LAD). Myocardial oxygen supply demand equilibrium were calculated for each cases using hemodynamic surrogate markers naming Diastolic pressure time index for supply and Tension time index for demand. The proposed 0d-3d coupled hemodynamic model of the coronary vessel bed along with supply-demand equilibrium estimated for different stress level and stenosis severity may provide useful insights on the dynamics of CAD manifestation and predict vulnerable regions in coronary bed for early screening and interventions.

Simulating Stenotic Conditions of the Coronary Artery in a Lumped Parameter Model of the Cardiovascular System.

Coronary flow control mechanisms maintain the average coronary blood flow (CBF) at 4% of the cardiac output (CO) in normal adults, with no prior diagnosis of coronary artery disease (CAD), under resting conditions. This paper explores a pulsatile sixth order lumped parameter (LP) model of the cardiovascular system (CVS) which utilizes the average CBF approximated from CO along with arterial blood pressure (ABP) waveform to estimate the coronary microvascular resistance using non-linear least square optimization technique. The CVS model includes a third order model of the coronary vascular bed and is shown to achieve phasic coronary flow. The coronary epicardial resistance is varied to emulate different degrees of stenosis and achieve realistic behavior of coronary microvascular resistance under these conditions.

Computational Fluid Dynamic Model of Left Atrium to Analyze Hemodynamic Manifestation during Atrial Fibrillation.

In this paper, we present a computational fluid dynamic (CFD) model of left atrium (LA) to analyze the manifestation and progression of atrial fibrillation (AF) in terms of hemodynamic metrics. We propose a coupled lumped-CFD (0d-3d) pipeline to model and predict the pulsatile flow and pressure fields of three-dimensional cardiac chamber under the influence of sinus rhythm, high frequency AF (HF-AF) and LA remodeled AF, considering the interactions between the heart and the arterial system through a separately modeled 0d lumped hemodynamic cardiac model. A novel rhythm generator is modeled to generate modulated cardiac chamber compliance and decoupled auricular and ventricular contraction rate to synthesize variation in sinus rhythm and subsequent AF generation. CFD simulation were solved using subject specific CT scan. Systemic and pulmonary flow and pressure along with metrics related to wall shear stress in LA were derived. Left ventricular (LV) hemodynamic parameters associated with global cardio vascular evaluation like ejection fraction, stroke volume, cardiac output, etc. were also generated for all the rhythmic disturbance under consideration. The proposed 0d-3d coupled hemodynamic model of the LA can provide useful insights on the dynamics of AF manifestation and predict vulnerable regions in the cardiac chambers as well as arterial vasculature for probable thrombogenic plaque formation that leads to stroke and infraction, leading to heart failure.

Transient Response of Basal Ganglia Network in Healthy and Low-Dopamine State.

The basal ganglia (BG) are crucial for a variety of motor and cognitive functions. Changes induced by persistent low-dopamine (e.g., in Parkinson's disease; PD) result in aberrant changes in steady-state population activity (ß band oscillations) and the transient response of the BG. Typically, a brief cortical stimulation results in a triphasic response in the substantia nigra pars reticulata (SNr; an output of the BG). The properties of the triphasic responses are shaped by dopamine levels. While mechanisms underlying aberrant steady state activity are well studied, it is still unclear which BG interactions are crucial for the aberrant transient responses in the BG. Moreover, it is also unclear whether mechanisms underlying the aberrant changes in steady-state activity and transient response are the same. Here, we used numerical simulations of a network model of BG to identify the key factors that determine the shape of the transient responses. We show that an aberrant transient response of the SNr in the low-dopamine state involves changes in the direct pathway and the recurrent interactions within the globus pallidus externa (GPe) and between GPe and subthalamic nucleus (STN). However, the connections from D2-type spiny projection neurons (D2-SPN) to GPe are most crucial in shaping the transient response and by restoring them to their healthy level, we could restore the shape of transient response even in low-dopamine state. Finally, we show that the changes in BG that result in aberrant transient response are also sufficient to generate pathologic oscillatory activity in the steady state.

Synthetic PPG Signal Generation to Improve Coronary Artery Disease Classification: Study With Physical Model of Cardiovascular System.

This paper presents a novel approach of generating synthetic Photoplethysmogram (PPG) data using a physical model of the cardiovascular system to improve classifier performance with a combination of synthetic and real data. The physical model is an in-silico cardiac computational model, consisting of a four-chambered heart with electrophysiology, hemodynamic, and blood pressure auto-regulation functionality. Starting with a small number of measured PPG data, the cardiac model is used to synthesize healthy as well as PPG time-series pertaining to coronary artery disease (CAD) by varying pathophysiological parameters. A Variational Autoencoder (VAE) structure is proposed to derive a statistical feature space for CAD classification. Results are presented in two perspectives namely, (i) using artificially reduced real disease data and (ii) using all the real disease data. In both cases, by augmenting with the synthetic data for training, the performance (sensitivity, specificity) of the classifier changes from (i) (0.65, 1) to (1, 0.9) and (ii) (1, 0.95) to (1, 1). The proposed hybrid approach of combining physical modelling and statistical feature space selection generates realistic PPG data with pathophysiological interpretation and can outperform a baseline Generative Adversarial Network (GAN) architecture with a relatively small amount of real data for training. This proposed method could aid as a substitution technique for handling the problem of bulk data required for training machine learning algorithms for cardiac health-care applications.

Walking Pole Gait to Reduce Joint Loading post Total Knee Athroplasty: Musculoskeletal modeling Approach.

Excessive knee contact loading is precursor to osteoarthritis and related knee ailment leading to knee athroplasty. Reducing contact loading through gait modifications using assisted pole walking offers noninvasive process of medial load offloading at knee joint. In this paper, we evaluate the efficacy of different configuration of pole walking for reducing contact force at the knee joint through musculoskeletal (MSK) modeling. We have developed a musculoskeletal model for a subject with knee athroplasty utilizing in-vivo implant data and computed tibio-femoral contact force for different pole walking conditions to evaluate the best possible configuration for guiding rehabilitation, correlated with different gait phases. Effect of gait speed variation on knee contact force, hip joint dynamics and muscle forces are simulated using the developed MSK model. Results indicate some interesting trend of load reduction, dependent on loading phases pertaining to different pole configuration. Insights gained from the simulation can aid in designing personalized rehabilitation therapy for subjects suffering from Osteoarthritis.

3D Cardiac Computational Model for Evaluating the Progression of Myocardial Ischemia in a Supply-Demand Paradigm.

In this paper, we present a cardiac computational framework aimed at simulating the effects of ischemia on cardiac potentials and hemodynamics. Proposed cardiac model uses an image based pipeline for modeling and analysis of the ischemic condition in-silico. We compute epicardial potential as well as body surface potential (BSP) for acute ischemic conditions based on data from animal model while varying both local coronary supply and global metabolic demand. Single lead ECG equivalent signal processed from computed BSP is used to drive a lumped hemodynamic model and derive left ventricular dynamics. Computational framework combining 3d structural information from image data and integrating electrophysiology and hemodynamics functionality is aimed to evaluate additional cardiac markers along with conventional electrical markers visible during acute ischemia and give a broader understanding of ischemic manifestation leading to pathophysiological changes. Simulation of epicardial to bodysurface potential followed by estimation of hemodynamic parameters like ejection fraction, contractility, blood pressure, etc, would help to infer subtle changes detectable beyond conventional ST segment changes.

A Lumped Parameter Model for Cardiac Output Estimation Using Arterial Blood Pressure Waveform.

This paper investigates a subject-specific lumped parameter cardiovascular model for estimating Cardiac Output (CO) using the radial Arterial Blood Pressure (ABP) waveform. The model integrates a simplified model of the left ventricle along with a linear third order model of the arterial tree and generates reasonably accurate ABP waveforms along with the Dicrotic Notch (DN). Non-linear least square optimization technique is used to obtain uncalibrated estimates of cardiovascular parameters. Thermodilution CO measurements have been used to evaluate the CO estimation accuracy. The model achieves less than 15% normalized error across 10 subjects with different shapes of ABP waveform.

Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy.

Wearable cardioverter defibrillator (WCD) is a life saving, wearable, noninvasive therapeutic device that prevents fatal ventricular arrhythmic propagation that leads to sudden cardiac death (SCD). WCD are frequently prescribed to patients deemed to be at high arrhythmic risk but the underlying pathology is potentially reversible or to those who are awaiting an implantable cardioverter-defibrillator. WCD is programmed to detect appropriate arrhythmic events and generate high energy shock capable of depolarizing the myocardium and thus re-initiating the sinus rhythm. WCD guidelines dictate very high reliability and accuracy to deliver timely and optimal therapy. Computational model-based process validation can verify device performance and benchmark the device setting to suit personalized requirements. In this article, we present a computational pipeline for WCD validation, both in terms of shock classification and shock optimization. For classification, we propose a convolutional neural network-"Long Short Term Memory network (LSTM) full form" (Convolutional neural network- Long short term memory network (CNN-LSTM)) based deep neural architecture for classifying shockable rhythms like Ventricular Fibrillation (VF), Ventricular Tachycardia (VT) vs. other kinds of non-shockable rhythms. The proposed architecture has been evaluated on two open access ECG databases and the classification accuracy achieved is in adherence to American Heart Association standards for WCD. The computational model developed to study optimal electrotherapy response is an in-silico cardiac model integrating cardiac hemodynamics functionality and a 3D volume conductor model encompassing biophysical simulation to compute the effect of shock voltage on myocardial potential distribution. Defibrillation efficacy is simulated for different shocking electrode configurations to assess the best defibrillator outcome with minimal myocardial damage. While the biophysical simulation provides the field distribution through Finite Element Modeling during defibrillation, the hemodynamic module captures the changes in left ventricle functionality during an arrhythmic event. The developed computational model, apart from acting as a device validation test-bed, can also be used for the design and development of personalized WCD vests depending on subject-specific anatomy and pathology.

Multimodal cardiovascular model for hemodynamic analysis: Simulation study on mitral valve disorders.

Valvular heart diseases are a prevalent cause of cardiovascular morbidity and mortality worldwide, affecting a wide spectrum of the population. In-silico modeling of the cardiovascular system has recently gained recognition as a useful tool in cardiovascular research and clinical applications. Here, we present an in-silico cardiac computational model to analyze the effect and severity of valvular disease on general hemodynamic parameters. We propose a multimodal and multiscale cardiovascular model to simulate and understand the progression of valvular disease associated with the mitral valve. The developed model integrates cardiac electrophysiology with hemodynamic modeling, thus giving a broader and holistic understanding of the effect of disease progression on various parameters like ejection fraction, cardiac output, blood pressure, etc., to assess the severity of mitral valve disorders, naming Mitral Stenosis and Mitral Regurgitation. The model mimics an adult cardiovascular system, comprising a four-chambered heart with systemic, pulmonic circulation. The simulation of the model output comprises regulated pressure, volume, and flow for each heart chamber, valve dynamics, and Photoplethysmogram signal for normal physiological as well as pathological conditions due to mitral valve disorders. The generated physiological parameters are in agreement with published data. Additionally, we have related the simulated left atrium and ventricle dimensions, with the enlargement and hypertrophy in the cardiac chambers of patients with mitral valve disorders, using their Electrocardiogram available in Physionet PTBI dataset. The model also helps to create 'what if' scenarios and relevant analysis to study the effect in different hemodynamic parameters for stress or exercise like conditions.

Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal.

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.

Parkinsonian Tremor Signal Decomposition: Segregating Effects of Deep Brain Stimulation and Medication.

Analysis of tremor signal is a crucial part in the study of Parkinsonian subject, specially to understand effectiveness of treatment and progression of disease. Aim of this paper is to segregate the effects of Deep Brain Stimulation (DBS) and medicinal components from Parkinson's disease (PD) tremor signal. Tremor signal has multiple effects embedded in a single channel and identifying the hidden components from it is a challenging process. Conventional methods like Empirical Mode Decomposition (EMD) and Ensemble EMD (EEMD) serve the purpose, however, these methods fail with increase in noise in the signal. We propose the usage of Variational Mode Decomposition (VMD) to identify the underlying hidden components in the tremor signal. It decomposes the tremor signal into different source components, which can be identified as medicinal or DBS components. Results show that VMD is more efficient in disintegrating the medicine and DBS component from the single channel tremor signal, compared to standard EMD and EEMD techniques. This study can help in better understanding of PD tremor suppression mechanism.

Muscle Synergy Control During Hand Reach Task on Varying Shoulder Configuration.

Control of human arm in reaching task is a result of complex neural interaction involving central nervous and musculoskeletal system, where, group of muscle activation are planned through synergistic and coordinated recruitment, often to reach an optimal strategy. Aim of this paper is to explore muscle synergy distribution on several reaching task of similar elbow trajectory but changing shoulder configuration. A musculoskeletal model of human arm comprising shoulder, elbow and wrist joint have been designed and is used to calculate muscle activation required to perform three specific reaching tasks. Muscle synergy have been computed on the simulated activation to find a relation between synergy and energy requirement with the change of rotation and elevation of shoulder and its effect on the motion path of the elbow joint. These findings may help to define optimal joint configuration for a planned range of motion during rehabilitation exercises and also in developing neural prosthesis and myoelectric interfaces for efficient arm motion control.

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics.

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.

Musculoskeletal modeling to predict and reduce Antetrior Cruciate Ligament injury during single leg drop jump activity: Synergistic muscle co-activation approach.

This paper presents a `drop jump' modeling to study the effect of synergistic muscle activation on controlling Anterior Cruciate Ligament (ACL) injury. ACL injuries are mostly caused during high impact loading. A full body musculoskeletal model with knee ligaments have been developed in `OpenSim platform' to simulate ACL injury during drop jump activity. The model is used to quantify the effect of change in muscle activation on different kinetic and kinematic parameters, which are associated with ACL injury. A neuromusculoskeletal controller have been designed which selects optimal muscle activation of Quadriceps, Hamstrings, Gastrocnemius and Tibilias anterior muscle group so as to reduce the chance of ACL injury and ankle inversion risk while jumping from elevated platforms. The OpenSim model along with the neuro-muscular controller forms an injury `predict-adapt' system, which can be useful in designing specific training sessions for athletics or for planning personalized rehabilitation therapy.

Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a Digital twin of cardiovascular system.

Synthetic data generation has recently emerged as a substitution technique for handling the problem of bulk data needed in training machine learning algorithms. Healthcare, primarily cardiovascular domain is a major area where synthetic physiological data like Photoplethysmogram (PPG), Electrocardiogram (ECG), Phonocardiogram (PCG), etc. are being used to improve accuracy of machine learning algorithm. Conventional synthetic data generation approach using mathematical formulations lack interpretability. Hence, aim of this paper is to generate synthetic PPG signal from a Digital twin platform replicating cardiovascular system. Such system can serve the dual purpose of replicating the physical system, so as to simulate specific `what if' scenarios as well as to generate large scale synthetic data with patho-physiological interpretability. Cardio-vascular Digital twin is modeled with a two chambered heart, haemodynamic equations and a baroreflex based pressure control mechanism to generate blood pressure and flow variations. Synthetic PPG signal is generated from the model for healthy and Atherosclerosis condition. Initial validation of the platform has been made on the basis of efficiency of the platform in clustering Coronary Artery Disease (CAD) and non CAD PPG data by extracting features from the synthetically generated PPG and comparing that with PPG obtained from Physionet data.

Mediolateral stability index as a biomarker for Parkinson's disease progression: A graph connectivity based approach.

This paper presents a novel metric to serve as a bio-marker for understanding and detecting progression of Parkinson's disease (PD). Proposed metric, termed as `Mediolateral Stability (MLS) index' has been derived from processing insole gait data using graph connectivity analysis. The proposed metric focuses on variability of mediolateral pressure in foot during gait progression. Vertical Ground Reaction Force (VGRF) and stride time information derived from a wearable insole for PD subjects as well as healthy controls are processed to create a connectivity graph. The insole contains eight pressure sensitive sensors for each foot and these sensors serve as the nodes of the connectivity graph. The proposed MLS index shows significant difference (p <; 0.05) in between control and PD groups and also in between progressive stages of PD, such as mild and moderate PD groups with p <; 0.05. Proposed graph connectivity based feature can act as a bio marker to correctly classify PD, identify early onset of PD and trace changes due to disease progression and can also provide information about dynamic pressure distribution during gait.

Evaluating Age-Related Variations of Gaze Behavior for a Novel Digitized-Digit Symbol Substitution Test.

Analysis of cognitive functioning from gaze behavior might serve as an early indicator of age related decline of cognitive functions. Standard psychological tests like the digit-symbol substitution test or the symbol-digit modalities test is used exclusively in this regard. In this paper, we have designed and developed a digitized version of the digit symbol substitution test. Three different versions have been designed in order to derive deeper insights of the user behavior. The test-retest validation of the versions reveals good correlation across sessions. Further, the difference in gaze behavior which might be used as an indicator of cognitive functions is tested for two different age groups (13 participants <30 years and 11 participants >40 years). It is seen that the designed digitized version along with the usage of physiological markers like eye tracking bestows additional information and is sensitive to age related factors which might be used for the assessment as well as for the training purpose in rehabilitation systems. Results show that the performance can be analyzed using gaze and pupillometric features in addition to the conventional test performance metrics. We derived an index to measure the performance related to visuo-spatial functioning on one of the designed versions of the test. Results of this index on the number of fixations for two age groups are found to be separated in a statistically significant (p<0.05) manner. The age related difference (p<0.05) is also evident in the pupillometric responses obtained.