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Table of Contents
Intro
Preface
Contents
1 Introduction
1.1 Cardiac Electrical Signaling
1.2 Spatiotemporal Heterogeneity of Heart Diseases
1.3 Multi-scale Modeling of Cardiac Systems
1.4 Summary
References
2 Multi-scale Simulation Modeling of Cardiac Systems
2.1 Computer Modeling of Ion Channels and Tissues
2.2 Statistical Metamodeling and Experiments in Cardiac Ion Channel Simulation
2.3 Whole-Heart Computer Simulation
2.4 Calibration of 3D Cardiac Simulation
References
3 Sensor-Based Modeling and Analysis of Cardiac Systems
3.1 Electrocardiogram (ECG) Sensing
3.2 Modeling Incomplete and Uncertain Data
3.2.1 Introduction
3.2.2 Modeling Approaches
3.2.3 Summary
3.3 Computationally Identify Sensory Biomarkers
3.3.1 Introduction
3.3.2 Modeling Approaches
3.3.3 Summary
3.4 Spatiotemporal Monitoring and Modeling
3.4.1 Introduction
3.4.2 Modeling Approaches
3.4.3 Summary
3.5 Automatic Disease Detection from ECG Signals
3.5.1 Introduction
3.5.2 Two-level DNN with Generative Adversarial Network
First-Level Model: MadeGAN for Anomaly Detection
Second-Level Model: Transfer-Learning- and Multi-Branching-Enhanced Classification
3.5.3 Summary
3.6 Characterization of Myocardial Infarction Using Inverse ECG Modeling
3.6.1 Introduction
3.6.2 Robust Inverse ECG Modeling
3.6.3 Characterization of MI on the Heart Surface
3.6.4 Summary
References
4 Simulation Optimization of Medical Decision Making
4.1 Introduction to Simulation Optimization
4.1.1 Rank and Selection
4.1.2 Response Surface Methodology
4.1.3 Stochastic Kriging
4.1.4 Simulation Optimization in Healthcare
4.2 Sequential Medical Decision Making
4.2.1 Model-Based Sequential Decision Making
4.2.2 Model-Free Sequential Decision Making
4.3 Optimal Cardiac Surgical Planning
4.3.1 Sequential Decision Making Formulation of Cardiac Surgery Problems
4.3.2 Bayesian Learning-Enhanced Tree Search for Optimal Cardiac Surgical Planning
4.4 Conclusions
References
5 Outlook and Future Research
Preface
Contents
1 Introduction
1.1 Cardiac Electrical Signaling
1.2 Spatiotemporal Heterogeneity of Heart Diseases
1.3 Multi-scale Modeling of Cardiac Systems
1.4 Summary
References
2 Multi-scale Simulation Modeling of Cardiac Systems
2.1 Computer Modeling of Ion Channels and Tissues
2.2 Statistical Metamodeling and Experiments in Cardiac Ion Channel Simulation
2.3 Whole-Heart Computer Simulation
2.4 Calibration of 3D Cardiac Simulation
References
3 Sensor-Based Modeling and Analysis of Cardiac Systems
3.1 Electrocardiogram (ECG) Sensing
3.2 Modeling Incomplete and Uncertain Data
3.2.1 Introduction
3.2.2 Modeling Approaches
3.2.3 Summary
3.3 Computationally Identify Sensory Biomarkers
3.3.1 Introduction
3.3.2 Modeling Approaches
3.3.3 Summary
3.4 Spatiotemporal Monitoring and Modeling
3.4.1 Introduction
3.4.2 Modeling Approaches
3.4.3 Summary
3.5 Automatic Disease Detection from ECG Signals
3.5.1 Introduction
3.5.2 Two-level DNN with Generative Adversarial Network
First-Level Model: MadeGAN for Anomaly Detection
Second-Level Model: Transfer-Learning- and Multi-Branching-Enhanced Classification
3.5.3 Summary
3.6 Characterization of Myocardial Infarction Using Inverse ECG Modeling
3.6.1 Introduction
3.6.2 Robust Inverse ECG Modeling
3.6.3 Characterization of MI on the Heart Surface
3.6.4 Summary
References
4 Simulation Optimization of Medical Decision Making
4.1 Introduction to Simulation Optimization
4.1.1 Rank and Selection
4.1.2 Response Surface Methodology
4.1.3 Stochastic Kriging
4.1.4 Simulation Optimization in Healthcare
4.2 Sequential Medical Decision Making
4.2.1 Model-Based Sequential Decision Making
4.2.2 Model-Free Sequential Decision Making
4.3 Optimal Cardiac Surgical Planning
4.3.1 Sequential Decision Making Formulation of Cardiac Surgery Problems
4.3.2 Bayesian Learning-Enhanced Tree Search for Optimal Cardiac Surgical Planning
4.4 Conclusions
References
5 Outlook and Future Research