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Record Nr. |
UNINA9910739412903321 |
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Autore |
Yang Hui (Professor of industrial engineering) |
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Titolo |
Sensing, modeling and optimization of cardiac systems : a new generation of digital twin for heart health informatics / / Hui Yang, Bing Yao |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (x, 88 pages) : illustrations (some color) |
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Collana |
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SpringerBriefs in Service Science, , 2731-3751 |
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Altri autori (Persone) |
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YaoBing (Professor of industrial engineering) |
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Disciplina |
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Soggetti |
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Digital twins (Computer simulation) |
Heart - Computer simulation |
Heart - Mathematical models |
Medical informatics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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-- 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. |
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Sommario/riassunto |
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This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients’ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences. |
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