with Mid-range or Mildly Reduced EF, 5Chapter 10
| AI | Artificial Intelligence, 1 bp Blood Pressure, 11 |
| CAD | Coronary Artery Disease, 15 |
| CT | Computed Tomography, 15 |
| CVDs | Cardiovascular Diseases, 1 |
| DL | Deep Learning, 12 |
| ECG | Electrocardiogram, 1 |
| EIS | Electrochemical Impedance Spectroscopy, 4 |
| IoT | The Internet of Things, 11 |
| MRI | Magnetic Resonance Imaging, 2,13 |
| RI | Refractive Index, 6 |
Chapter 11
| BMI | Body Mass Index, 9 |
| CHF | Chronic Heart Failures, 2 |
| CIHM | Chronicle Implantable Hemodynamic Monitor, 19 |
| CRT | Cardiac Resynchronization Therapy, 19 |
| HCG | Human Chorionic Gonadotropin, 21 |
| IASD | Inter Atrial Shunt Device, 22 |
| LA | Left Atrial, 17 |
| LAP | Left Atrial Pressure, 18 |
| MCT | Mobile Cardiac Telemetry, 6 |
| NYHA | New York Heart Association, 19 |
| PA | Pulmonary Artery, 17 |
| PAM | Patient Advisory Module, 20 |
| RV | Right Ventricle, 17 |
Chapter 12
| AI | Artificial Intelligence, 3 |
| ANN | Artificial Neural Networks, 4 |
| AUC | Area Under Curve, 6 |
| CNN | Convolutional Neural Network, 4 |
| CRT | Cardiac Resynchronization Therapy, 6 |
| DL | Deep Learning, 3 |
| DNN | Deep Neural Network, 5 |
| ECG | Electrocardiographic, 5 |
| HF | Heart Failure, 1 |
| k-NN | k-Nearest Neighbors, 5 |
| LVAD | Left Ventricular Assist Device, 7 |
| ML | Machine Learning, 3 |
| PPGs | Photoplethysmograms, 8 |
| RF | Random Forest, 4 |
| RNN | Recurrent Neural Network, 5 |
| RV | Right Ventricular, 2 |
| RVF | Right-ventricular Failure, 7 |
| RVFRS | Right Ventricular Failure Risk Score, 7 |
| SVM | Support Vector Machine, 4 |
Chapter 13
| ABP | Arterial Blood Pressure, 4 |
| AF | Atrial Fibrillation, 11 |
| CAD | Coronary Artery Diseases, 19 |
| CardioMEMS | Cardio-Microelectromechanical system, 3 |
| CRT | Cardiac Resynchronization Therapy, 18 |
| CRT-D | Cardiac Resynchronization Therapy Defibrillator, 4 |
| CVDs | Cardiovascular Diseases, 3 |
| ECG | Electrocardiogram, 2 |
| FDA | Food and Drug Administration, 13 |
| HF | Heart Failure, 1 |
| hour | Heart Rate, 13 |
| ICDs | Implantable Cardioverter-defibrillators, 5 |
| LAP | Left Atrial Pressure, 5 |
| LVAD | Left Ventricular Assist Device, 18 |
| MI | Myocardial Infarction, 14 |
| NSTEMI | Non-ST-elevation Myocardial Infarction, 18 |
| NYHA | New York Heart Association, 10 |
| OHRM | Optical Heart Rate Monitor, 9 |
| PAP | Pulmonary Artery Pressure, 3 |
| PD | Photodiode, 9 |
| PPG | Photoplethysmogram, 4 |
| RR | Respiration Rate, 13 |
| STEMI | ST-elevation Myocardial Infarction, 18 |
| SVM | Support Vector Machine, 18 |
| VF | Ventricular Fibrillation, 11 |
Acknowledgment
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.
1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure
Hidayet Takcı
1.1 Introduction
Heart diseases are the deadliest in the world. Of the many diseases included in the category of heart disease, the most prominent is coronary artery disease (CAD), which causes heart attacks. CAD, high blood pressure, and many other heart diseases cause heart failure (HF). With HF being a consequence of heart disease, the prediction of HF is related to the prediction of diseases categorized as heart disease.
In this chapter, the diagnosis of HF is discussed in terms of invasive/non-invasive and artificial intelligence/machine learning techniques. Invasive and non-invasive techniques are distinct in the way the patient is treated. Invasive methods are usually associated with a physical intervention in the body. This intervention involves operations such as taking blood for blood analysis and not pressing strongly on the abdominal area. Non-invasive methods include physical therapy, taking blood pressure, and temperature measurement. In today’s world, where information technologies have evolved in every field, the field of health has also received its share. Computer-aided clinical decision support systems provide the strongest support for diagnostic studies today. The most important components of computer-aided diagnosis are artificial intelligence and machine learning systems that offer a wide range of services from smart assistant applications to imaging techniques. Artificial intelligence and machine learning have a healing role in electrocardiography, echocardiography, and similar invasive and non-invasive techniques.
In the second section of this chapter, HF will be described and its causes, symptoms, and treatment revealed. The third section will explain diagnosis by invasive and non-invasive methods. Computer-aided diagnosis and decision support systems are briefly mentioned in the fourth section. The fifth section looks first at what artificial intelligence is and then presents fields and examples of artificial intelligence supported studies. The sixth section explains machine learning, learning types, machine learning algorithms, and machine learning based diagnostic studies. The chapter’s concluding section summarizes studies and comments upon diagnostic studies for HF.
1.2 Heart Failure
HF belongs to a class of diseases that occur due to several diseases known as heart diseases and can be fatal if left untreated. In this section, HF is defined and the factors causing it, its symptoms, and its treatment are examined.
1.2.1
