Predicting Heart Failure
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Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods
Predicting Heart Failure
Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods
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Predicting Heart Failure
Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods
Edited by
Kishor Kumar Sadasivuni
Qatar University
Doha, Qatar
Hassen M. Ouakad
Sultan Qaboos University
Muscat, Oman
Somaya Al-Maadeed
Qatar University
Doha, Qatar
Huseyin C. Yalcin
Qatar University
Doha, Qatar
Issam Bait Bahadur
Sultan Qaboos University
Muscat, Oman

This edition first published 2022
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Library of Congress Cataloging-in-Publication Data Names: Kumar Sadasivuni, Kishor, 1986- author. Title: Predicting heart failure : invasive, non-invasive,machine learning and artificial intelligence based methods / edited by Kishor Kumar Sadasivuni, Qatar University, Doha, Qatar, [and four others]. Description: Hoboken, NJ : John Wiley & Sons, 2022. | Includes bibliographical references and index. Identifiers: LCCN 2021043721 (print) | LCCN 2021043722 (Obook) | ISBN 9781119813019 (hardback) | ISBN 9781119813026 (pdf) | ISBN 9781119813033 (epub) | ISBN 9781119813040 (ebook) Subjects: LCSH: Heart failure--Risk factors. | Heart failure--Treatment. | Heart--Diseases--Patients. Classification: LCC RC685.C53 P74 2023 (print) | LCC RC685.C53 (ebook) | DDC 616.1/29--dc23/eng/20211028 LC record available at https://lccn.loc.gov/2021043721LC ebook record available at https://lccn.loc.gov/2021043722
Cover image: Courtesy of Kishor Kumar Sadasivuni
Cover design by Wiley
Set in 9.5/12.5pt STIXTwoText by Integra Software Services Pvt. Ltd, Pondicherry, India
Contents
1 Cover
2 Title page Predicting Heart Failure Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods Edited by Kishor Kumar Sadasivuni Qatar University Doha, Qatar Hassen M. Ouakad Sultan Qaboos University Muscat, Oman Somaya Al-Maadeed Qatar University Doha, Qatar Huseyin C. Yalcin Qatar University Doha, Qatar Issam Bait Bahadur Sultan Qaboos University Muscat, Oman
3 Copyright
4 Preface
5 Abbreviations
6 Acknowledgment
7 1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure
8 2 Conventional Clinical Methods for Predicting Heart Disease
9 3 Types of Biosensors and their Importance in Cardiovascular Applications
10 4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors
11 5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview
12 6 Artificial Intelligence Techniques in Cardiology: An Overview
13 7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Disease s
14 8 Applications of Machine Learning for Predicting Heart Failure
15 9 Machine Learning Techniques for Predicting and Managing Heart Failure
16 10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers
17 11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review
18 12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction
19 13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices
20 Index
21 End User License Agreement
List of Illustrations
1 Chapter 1Figure 1.1 Images for boundary detection in...Figure 1.2 Supervised and unsupervised machine learning.
2 Chapter 2Figure 2.1 Supine, left lateral decubitus...Figure 2.2 ECG. E denotes electrodes placed...Figure 2.3 Major waves of a single ECG signal...Figure 2.4 Treadmill assessment.Figure 2.5 Evolution of rubber-based stethoscopes.Figure 2.6 A digital stethoscope.Figure 2.7 The echocardiography method. The...Figure 2.8 Formation of plaques in the...Figure 2.9 Plaque formation in the heart arteries.
3 Chapter 3Figure 3.1 Statistics of the number of publications...Figure 3.2 A schematic representation of some...Figure 3.3 Application of wearable/implantable...
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