Predicting Heart Failure
Description
Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it.
This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies 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. Readers will also find:
* Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application
* Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology
* Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure
* Discussion of the risks and issues associated with the remote monitoring system
* Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection
* Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations.
Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.
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About the Editors
Dr Kishor Kumar Sadasivuni, Center for Advanced Materials, Qatar University, Qatar
Dr Hassen M. Ouakad, Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Oman
Prof. Somaya Al-Maadeed, Department of Computer Science and Engineering, Qatar University, Qatar
Dr Huseyin C. Yalcin, Biomedical Research Center, Qatar University, Qatar
Dr Issam Bait Bahadur, Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Oman
This publication was supported by Qatar University Internal Grant No. IRCC-2020-013 and Sultan Qaboos University through Grant # CL/SQU-QU/ENG/20/01, respectively. The findings achieved herein are solely the responsibility of the authors.
Content
Preface vii
Abbreviations ix
Acknowledgment xvii
1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure 1 Hidayet Takci
2 Conventional Clinical Methods for Predicting Heart Disease 23 Aisha A-Mohannadi, Jayakanth Kunhoth, Al Anood Najeeb, Somaya Al-Maadeed, and Kishor Kumar Sadasivuni
3 Types of Biosensors and their Importance in Cardiovascular Applications 47 S Irem Kaya, Leyla Karadurmus, Ahmet Cetinkaya, Goksu Ozcelikay, and Sibel A Ozkan
4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors 81 Mohamed Zied Chaari and Somaya Al-Maadeed
5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview 109 Huseyin Enes Salman, Mahmoud Khatib A.A Al-Ruweidi, Hassen M Ouakad, and Huseyin C Yalcin
6 Artificial Intelligence Techniques in Cardiology: An Overview 139 Ikram-Ul Haq and Bo Xu
7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases 155 Ahmad Mousa Altamimi and Mohammad Azzeh
8 Applications of Machine Learning for Predicting Heart Failure 171 Sabri Boughorbel, Yassine Himeur, Huseyin Enes Salman, Faycal Bensaali,Faisal Farooq, and Huseyin C Yalcin
9 Machine Learning Techniques for Predicting and Managing Heart Failure 189 Dafni K Plati, Evanthia E Tripoliti, Georgia S Karanasiou, Aidonis Rammos,Aris Bechlioulis, Chris J Watson, Ken McDonald, Mark Ledwidge, Yorgos Goletsis, Katerina K Naka, and Dimitrios I Fotiadis
10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers 227 Meena Laad, Sajna M.S, Kishor Kumar Sadasivuni, and Sadiya Waseem
11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review 243 Jayakanth Kunhoth, Nandhini Subramanian, and Ahmed Bouridane
12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction 269 Kanchan Kulkarni, Eric M Isselbacher, and Antonis A Armoundas
13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices 295 Muhammad E.H Chowdhury, Amith Khandaker, Yazan Qiblawey, Fahmida Haque, Maymouna Ezeddin, Tawsifur Rahman, Nabil Ibtehaz, and Khandaker Reajul Islam
Index 321
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, 18Chapter 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, 11Chapter 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, 28 PG Pencil Graphite, 29 QDs Quantum Dots, 28 SEM Scanning Electron Microscopy, 28 SERS Surface-Enhanced Raman Scattering, 12 SPEs Screen-Printed Electrodes, 28 SPGEs Screen-Printed Gold Electrodes, 28 SPR Surface Plasmon Resonance, 12 SPRi Surface Plasmon Resonance Imaging, 12 sST2 Soluble Suppressor of Tumorgenicity 2, 8 TG Triglycerides, 9 TNF-a, Tumor Necrosis Factor-alpha, 7Chapter 4
AC Alternative Current, 9 BSN Body Sensor Network, 2 CE Counter Electrode, 5 CMT Coupled-Mode Theory, 12 EDAS European Aeronautic Defense and Space Company, 10 EM Electromagnetic Interference, 11 EMF Electromagnetic Field, 9 HPF High-Pass Filter, 17 IoT Internet of Things, 2 LPF Low Pass Filter, 17 MEMS Micro-Electromechanical Systems, 4 MPT Microwave Power Transmission, 8 RE Reference Electrode, 5 RF Radio Frequency, 3 SHM Structural Health Supervising, 2 SoC System on a Chip, 5 VSWR Voltage Standing Wave Ratio, 24 WBAN Wireless Body Area Network, 2 WE Working Electrode, 5Chapter 5
BCG Ballistocardiography, 13 BLUE Bedside Lung Ultrasound, 10 BNP B-type Natriuretic Peptide, 14 C.A.USE Cardiac Arrest Ultrasound Exam, 10 CHF Chronic Heart Failure, 2 DNN Deep Neural Network, 12 ECG Electrocardiography, 3 EVLW Extravascular Lung Water, 8 FALLS Fluid Administration Limited by Lung Sonography, 10 GPS Global Positioning System, 6 HF Heart failure, 2 ICD Implantable Cardioverter Defibrillator, 12 LuCUS Lung and Cardiac Ultrasound, 10 LUS Lung Ultrasound, 8 LV Left Ventricular, 7 MEMS Microelectromechanical System, 6 MFCCs Mel-frequency Cepstral Coefficients, 12 NT-proBNP Amino-terminal Pro-B-type Natriuretic Peptide, 14 PA Pulmonary Arterial, 7 PCG Phonocardiogram, 12 PPG Photoplethysmogram, 14 RCTs Randomized Control Trials, 15 ReDS Remote Dielectric Sensing Technology, 5 RPM Remote Patient Monitoring, 15 RV Right Ventricular, 7 SCG Seismocardiography, 12Chapter 6
ACM All-cause Mortality, 8 AI Artificial Intelligence, 3, 8 ANN Artificial Neural Networks, 3,12 AUC Area Under the Curve, 9 CAD Coronary Artery Disease, 8 CCTA Cardiac Computed Tomographic Angiography, 8 CMR Cardiac Magnetic Resonance, 12 CNN Convolutional Neural Networks,...