Sensing, Modeling and Optimization of Cardiac Systems: A New Generation of Digital Twin for Heart Health Informatics
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham
Springer
2023
|
| Edition: | 1st ed |
| Series: | SpringerBriefs in Service Science Series
|
| Online Access: | DE-2070s |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (96 Seiten) |
| ISBN: | 9783031359521 |
Staff View
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| 245 | 1 | 0 | |a Sensing, Modeling and Optimization of Cardiac Systems |b A New Generation of Digital Twin for Heart Health Informatics |
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| 264 | 1 | |a Cham |b Springer |c 2023 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (96 Seiten) | ||
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 700 | 1 | |a Yao, Bing |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Yang, Hui |t Sensing, Modeling and Optimization of Cardiac Systems |d Cham : Springer,c2023 |z 9783031359514 |
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Record in the Search Index
| _version_ | 1818760560460693504 |
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| adam_text | |
| any_adam_object | |
| author | Yang, Hui |
| author_facet | Yang, Hui |
| author_role | aut |
| author_sort | Yang, Hui |
| author_variant | h y hy |
| building | Verbundindex |
| bvnumber | BV050100556 |
| collection | ZDB-30-PQE |
| 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 |
| ctrlnum | (ZDB-30-PQE)EBC30711963 (ZDB-30-PAD)EBC30711963 (ZDB-89-EBL)EBL30711963 (OCoLC)1395074860 (DE-599)BVBBV050100556 |
| dewey-full | 611.120113 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 611 - Human anatomy, cytology, histology |
| dewey-raw | 611.120113 |
| dewey-search | 611.120113 |
| dewey-sort | 3611.120113 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV050100556 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T07:00:34Z |
| institution | BVB |
| isbn | 9783031359521 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035437718 |
| oclc_num | 1395074860 |
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| physical | 1 Online-Ressource (96 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| record_format | marc |
| series2 | SpringerBriefs in Service Science Series |
| spelling | Yang, Hui Verfasser aut Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics 1st ed Cham Springer 2023 ©2023 1 Online-Ressource (96 Seiten) txt rdacontent c rdamedia cr rdacarrier SpringerBriefs in Service Science Series Description based on publisher supplied metadata and other sources 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 Yao, Bing Sonstige oth Erscheint auch als Druck-Ausgabe Yang, Hui Sensing, Modeling and Optimization of Cardiac Systems Cham : Springer,c2023 9783031359514 |
| spellingShingle | Yang, Hui Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics 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 |
| title | Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics |
| title_auth | Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics |
| title_exact_search | Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics |
| title_full | Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics |
| title_fullStr | Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics |
| title_full_unstemmed | Sensing, Modeling and Optimization of Cardiac Systems A New Generation of Digital Twin for Heart Health Informatics |
| title_short | Sensing, Modeling and Optimization of Cardiac Systems |
| title_sort | sensing modeling and optimization of cardiac systems a new generation of digital twin for heart health informatics |
| title_sub | A New Generation of Digital Twin for Heart Health Informatics |
| work_keys_str_mv | AT yanghui sensingmodelingandoptimizationofcardiacsystemsanewgenerationofdigitaltwinforhearthealthinformatics AT yaobing sensingmodelingandoptimizationofcardiacsystemsanewgenerationofdigitaltwinforhearthealthinformatics |