**1. Introduction**

Cardiac problems are one of the most important problems across the globe. According to autopsy studies, heart disease has increased since the 1960s due to a rise in the frequency of coronary atherosclerosis with resultant coronary heart disease. The number of CVD deaths in India each year is anticipated to increase from 2.26 to 4.77 million between the years 1990 and 2020. The coronary heart disease frequency rates in India have fluctuated from 1.6 to 7.4% in rural populations whereas from 1 to 13.2% in urban populations during the last several decades [1]. Heart disease claims the lives of about 17 lakh individuals in India each year, and the number is estimated to rise to 2.3 crores by 2030. This rise is linked to an increase in smoking and dietary changes, resulting in higher blood cholesterol levels. The symptoms like angina, chest pain, difficulty breathing, edema, fatigue, and lightheadedness may indicate a heart problem or heart attack. Heart attack can lead to cardiac arrest, which occurs when the heart's rhythm is disrupted, or the heart stops beating, and the body can no longer function [2].

Any disorder that affects the cardiovascular system is alluded to as heart disease [3]. Heart disease comes in various forms, each of which affects the heart and blood arteries in distinct ways. The most typical kinds of heart disease are coronary artery disease, arrhythmia, heart valve disease, and heart failure [4]. Coronary artery disease is the most noticeable type of heart disease. It happens when plaque accumulates in the arteries that deliver blood to the heart. It can cause a reduction in blood flow to your heart muscle, preventing it from receiving the oxygen it requires. Atherosclerosis, often known as artery hardening, is the most common cause of the illness. Arrhythmia refers to an improper beating of the heart [5]. It happens when the electrical impulses that regulate the heartbeat do not even function properly. As a result, the heart may beat excessively fast, too slowly, or in an irregular pattern. Heart valve disease occurs when a heart valve is damaged [6]. Infectious diseases such as rheumatic fever, congenital heart disease, excessive blood pressure, coronary artery disease are all causes of heart valve disorders. Heart failure does not imply that the heart has ceased to beat. A condition in which the heart is not pumping blood as efficiently as it should be to satisfy the body's demands. There are some more heart diseases like pericardial disease, myocardial infarction [7], cardiomyopathy, mitral valve regurgitation, congenital heart disease, etc.

Over the last several decades, the rapid advancement of cardiology has profoundly changed the natural course of cardiac patients. Cardiac care has evolved, with technology playing an increasingly significant role. With the appropriate technology and artificial intelligence (AI) and machine learning, cardiac care providers have been motivated to improve treatment methods [8]. Then there's remote care that enables electrocardiogram (ECG) diagnosis [9], which uses cloud technology and Bluetoothenabled cardiac devices to test the parameters and send them back to healthcare practitioners without attending the clinic. Some emerging technologies used every day in cardiology are transcatheter mitral and tricuspid valve interventions, artificial intelligence, wearable devices, big data, structured reporting, robots in the cath lab, virtual and augmented reality, FFR technologies, holographic procedural navigation in the Cath Lab, etc. [10].

There are many cardiac implantable electronic devices like pacemakers, implanted cardioverter defibrillators (ICDs), biventricular pacemakers, and cardiac loop recorders, which are used to control or monitor irregular heartbeats in persons with specific heart rhythm problems and heart failure. An implanted cardioverterdefibrillator is a device that can do cardioversion, defibrillation, and cardiac pacing. ICD is capable of rectifying the majority of life-threatening cardiac arrhythmias. A pacemaker is a device that is implanted beneath the skin and communicates with the heart through electrical leads. Pacemakers are used to treat bradycardia, a condition where the heart beats too slowly (less than 60 times per minute). The pacemaker sends electrical pulses to the heart to maintain it beating normally. A biventricular pacemaker is a compact, battery-operated device and light. This gadget aids with the proper pumping of your heart. It also protects from harmful cardiac arrhythmias. An implantable loop recorder is a heart-monitoring device implanted beneath the chest skin. It has a variety of applications. Searching for reasons of fainting, palpitations, very rapid or slow heartbeats, and hidden rhythms that might cause strokes are among the most prevalent. Computer-aided diagnosis (CAD) [11] refers to software that helps clinicians understand medical images. The radiologist or other medical expert must assess and evaluate a large amount of data in a short amount of time using imaging modalities such as X-ray, MRI, and ultrasound diagnostics. The Kurt Rossmann Laboratories for Radiologic Image Research in the Department of

#### *Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders DOI: http://dx.doi.org/10.5772/intechopen.103075*

Radiology at the University of Chicago began large-scale systematic research and development of several CAD methods in the early 1980s. The idea of computer-aided design was established in 1966 and has been completely implemented since 1980.

Nowadays, computer-aided diagnosis has become a contentious research topic in medical imaging and diagnostic radiology research. CAD technology aids in the improvement of the performance of radiologists in increasing productivity by costeffectively enhancing sensitivity rate. CAD can improve image diagnostic accuracy by detecting illnesses that are too premature to be detected by naked eyes. It enables early detection, which can lead to better treatment results. Computer-aided detection is a relatively new advancement in the area of breast imaging that aims to increase the throughput of radiologists to identify diseases like breast cancer [12] even at an early stage. In recent times, computer-aided diagnosis is used to diagnose acute lymphoblastic leukemia, which suggested a solution to the flaws in manual diagnosis techniques. Even ECG-based computer-aided diagnosis [13] is also used for cardiovascular diseases which have the potential to improve diagnosis accuracy while also lowering costs.

Medical images nowadays play a crucial role in the identification and diagnosis of awide range of disorders. To aid in the interpretation of medical images, a variety of computer-aided detection and diagnosis technologies have recently been developed in order to achieve a more reliable and accurate diagnosis. CT, MR imaging, digital radiography, biomagnetism, and optical range sensing are examples of imaging systems that take advantage of sophisticated computer technology.

The real-life problem with manual experimentation is that manual diagnostic procedures are time-consuming, less accurate, and prone to mistakes due to different human variables such as stress, exhaustion, fatigue, and so forth. As a result, many automated techniques have been developed to combat the flaws in manual diagnostic approaches. When compared to manual diagnosis procedures, these computer-aided technologies are faster, more dependable, more efficient, more standardization and more accurate. Computer-aided diagnosis (CAD) aids in the calculation of computational and statistical features that people cannot gather visually or intuitively. Computer-assisted diagnosis also reduces the reliance on the operator in ultrasonic imaging and makes the diagnosis procedure reproducible. Interference testing and 3D animations are simple to accomplish in computer-aided diagnosis [14].

Machine learning has been applied in a variety of fields all over the world and the health industry is no exception. On the other hand, deep learning is part of the family of machine learning algorithms relying on representation and artificial neural networks are being utilized for the analysis of medical data. For quite some time, these algorithms were used to assess patients'status with respect to the image or non-imagebased medical data acquired using new generation medical equipment. These developments are attributable to the emergence of new CAD systems known as knowledgebased systems, including expertise or knowledge. As a result, the modern CAD systems include some intelligence [15]. The major job of the software related to these systems nowadays is to automate the analytical phases. To ensure that components and assemblies achieve design standards, CAD software is used to make computer modeling, fit them together, and simulate their performance. Because design reviews, conducted by specialists, evaluate if changes should be made, the analytical phases of the design process are repeated (design synthesis). Design synthesis may be done immediately with AI-based technologies without the need for a separate design review, and they are correctly implemented.

Based on the recent advancements, computer-aided diagnosis is used to diagnose heart abnormalities such as arrhythmias and heart blockages using electrocardiogram (ECG) signal analysis [16]. Although electrocardiography (ECG) is affordable and commonly available, ECG abnormalities are not specific for the diagnosis of congestive heart failure (CHF) which is the inability of the heart to efficiently circulate blood throughout the body without a rise in intracardiac pressure. Based on the ECG, a welldesigned computer-aided detection (CAD) system for CHF might possibly eliminate subjectivity and give a quantitative evaluation for better decision-making.

Cardiologists and medical practitioners frequently utilize ECG to assess heart health. The difficulty in identifying and classifying distinct waveforms and morphologies in ECG signals is the major issue with manual analysis. This task is both timeconsuming and error-prone for a human. Cardiovascular illnesses are the leading cause of mortality worldwide, accounting for around one-third of all fatalities. Millions of individuals, for example, suffer from irregular heartbeats, which can be fatal in some circumstances. As a result, precise and low-cost arrhythmic heartbeat diagnosis is extremely desirable.

Many research in the literature investigated the utilization of machine learning approaches to reliably detect abnormalities in ECG data to solve the drawbacks present in human analysis. Pre-processing, like passing through bandpass or high pass filter, is used in most of these methods to prepare the signal to be compatible for machine-based analysis. The handcrafted features, which are typically statistical summarizations of signal windows, are then retrieved from these signals and employed in subsequent processing. For the last categorization task, conduct an analysis.

In terms of the conclusion, for ECG, traditional machine learning algorithms [2] like support vector machines, multi-layer perceptrons, decision trees, and other methods of analysis were used previously. Automated feature extraction and representation approaches have been shown to be more scalable and capable of producing more accurate predictions, according to current machine learning research. In this study, we are going to elaborate on a few of the new emerging and compatible technologies and their applications.

The rest of the article has been organized in the following manner. First, Section 2 provides a brief theoretical and mathematical background related to this domain of study which is followed by the problem statement in Section 3. Next, Section 4 discusses about the significance of noise removal with stages of data processing. Section 5 gives a brief survey about the recent state-of-the-art techniques related to automated signal processing of ECG signals that is followed by the promising experimental results reported in the recent literature. Finally, Section 7 concludes this chapter.
