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Preface
Our knowledge of human biology, especially related to the heart, increases every day. This makes it nearly impossible for physicians to stay current on the latest research in their fields, let alone in all of the others that directly affect their ability to treat their patients properly. This book will help in learning the mechanics and symptoms of heart failure and the various approaches, including conventional and modern techniques for diagnosing it. Moreover, the book provides a detailed presentation of the latest research data for preventing and treating heart failure.
In this book, 13 chapters address different conditions related to the heart, with detailed descriptions of each. The first chapter discusses invasive, non-invasive, machine learning, and artificial intelligence-based methods for predicting heart failure. In addition, it discusses heart failure causes, symptoms, and treatment, as well as research related to heart failure. In the second chapter, we examine the traditional methods of predicting heart diseases and the implementation of artificial intelligence technology to predict heart diseases accurately. A discussion of the main characteristics of cardiovascular biosensors is presented in Chapter 3, along with their open issues for development and application. We summarize the difficulties of wireless sensor communication and power transfer in Chapters 4, 5, and 6, which outline the utility of artificial intelligence in cardiology. Chapter 7 discusses how to predict heart diseases using data mining classification techniques. Applied machine learning is discussed in Chapters 8 and 9 as are advanced methods for estimating heart failure severity and for diagnosing and predicting heart failure. In Chapter 10, the present state of artificial intelligence and biosensors based on materials is briefly discussed. The underlying technologies of various invasive and non-invasive devices, and their benefits, are discussed and analyzed in Chapter 11. A discussion of the risks and issues associated with remote monitoring systems is also included in this chapter. An overview of these heart failure prediction devices is presented in Chapter 12 along with their invasive and non-invasive alternatives. The chapter also highlights the potential of artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations. Chapter 13 assesses the potential applications of implantable and wearable devices in heart failure detection, summarizes the available data for wearables and machine learning for improving patients’ cardiac health, and discusses the future of wearables for early prediction of heart failure.
Finally we strongly believe this book will provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. The book provides readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. It provides the latest research data needed for the diagnosis and treatment of heart failure. Moreover, this book is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.
Abbreviations
Chapter 1
| 3-D | Three-dimensional, 10 |
| BNP | Brain Natriuretic Peptide, 9 |
| CAD | Coronary Artery Disease, 2 |
| CHF | Congestive Heart Failure, 3 |
| CRP | C-reactive Protein, 8 |
| HF | Heart Failure, 3 |
| HRV | Heart Rate Variability, 24 |
| IoT | Internet of Things, 11 |
| LS-SVM | Least Squares SVM, 24 |
| LV | Left Ventricular, 23 |
| SVM | Support Vector Machine, 18 |
Chapter 2
| ACS | Acute Coronary Syndrome, 8 |
| AI | Artificial Intelligence, 18 bp Blood Pressure, 12 |
| CAC | Coronary Artery Calcium, 21 |
| CCA | Common Carotid Artery, 19 |
| CHF | Congestive Heart Failure, 20 |
| CNN | Convolutional Neural Network, 20 |
| CQ-NSGT | Constant-Q Non-Stationary Gabor Transform, 20 |
| CT | Computed Tomography, 19 |
| CVDs | Cardiovascular Diseases, 19 |
| DNN | Deep Neural Network, 20 |
| DOE | Dyspnea on Exertion, 10 |
| ECG | Electrocardiogram, 5 |
| ELM | Extreme Learning Machines, 20 |
| FCN | Fully Convolutional Network, 20 |
| GERD | Gastroesophageal Reflux Disease, 9 |
| HDL | High-density Lipoprotein, 12 |
| ICA | Internal Carotid Artery, 19 |
| IMT | Intima-Media Thickness, 19 |
| LDL | Low-density Lipoprotein, 12 |
| LI | Lumen-intima, 19 |
| LII | Lumen-Intima Interface, 20 |
| MA | Media Adventitia, 19 |
| MAI | Media-Adventitia Interface, 20 |
| MLP | Multilayer Perceptron, 21 |
| MPI | Myocardial Perfusion Imaging, 22 |
| NSR | Normal Sinus Rhythm, 20 |
| PND | Paroxysmal Nocturnal Dyspnea, 11 |
Chapter 3
| BNP | Brain Natriuretic Peptide, 5 |
| CRP | C-Reactive Protein, 3 |
| CSGMs | Comb Structured Gold Microelectrode Arrays, 28 |
| cTnI | Cardiac Troponin I, 5 |
| CV | Cyclic Voltammetry, 28 |
| CVDs | Cardiovascular Diseases, 1 |
| DPV | Differential Pulse Voltammetry, 28 |
| EIS | Electrochemical Impedance Spectroscopy, 28 |
| ELISA | Enzyme-Linked Immunosorbent Assay, 29 |
| ENPs | Enzyme Nanoparticles, 29 |
| GDF | Growth Differentiation Factor, 7 |
| GK | Glycerol Kinase, 29 |
| GO | Graphene Oxide, 28 |
| GPO | Glycerol-3- Phosphate Oxidase, 29 |
| HDL | High-density Lipoprotein, 8 |
| hour-FABP | Heart Fatty Acid-Binding Protein, 7 |
| IL-6 | Interleukin-6, 7 |
| LDL | Low-density Lipoprotein, 8 |
| LOD | Limit of Detection, 10 |
| LSPR | Localized Surface Plasmon Resonance, 12 |
| miRNAs | Micro RNAs, 6 |
| MPO | Myeloperoxidase, 6 |
| NPs | Nanoparticles, 29 |
| NSE | Neuron-specific Enolase, 6 |
| PCT | Procalcitonin, 5 |
| PEC | Photoelectrochemical,
|