## 1 Introduction  
Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG involves the recording of electrical activity that is generated by the heart through the surface of the body. Historically, the first ECG recording dates back to 1887 [1]. The electrical activity generated by the heart is measured using electrodes placed on specific parts of the body surface. ECG devices are typically used to obtain information about the physiological condition of the heart by measuring the electrical signals generated by the heart through electrodes attached noninvasively to the body surface [2]. Single-lead three-electrode ECG recordings have been widely used [3,4]. However, due to the advancement in research, the number of leads has gradually increased to obtain a more accurate ECG recording [5,6]. An ECG recording is one of the most valuable data used in diagnosing heart disease. It offers unique information that is not obtainable from any other type of test. Even though ECG signals are widely recorded in hospitals, they are also used in epidemiological studies, as well as in large clinical trials [4,5,7]. One technique commonly used in epidemiological studies for classifying ECG is known as the Minnesota coding, whereby an internationally agreed on set of rules is used to describe the ECG patterns [8]. This is particularly favored by epidemiologists in large-scale population studies. Currently, sensors can be used to automatically measure ECG signals instead of conventional discrete measurement [4,5,8,9]. Automated techniques for Minnesota coding are available and are valuable when a large number of ECGs require coding. Recently, a computer analysis of the ECG was developed for recording and interpreting ECGs. This helps in developing ECG data repositories and reducing the time lag in ECG reports. Aside from ECG signals, other forms of real-time biomedical signals have been collected from the human body, which includes blood pressure, respiration rate, heart rate, blood glucose, galvanic skin response and oxygen saturation levels, among others. The list of collectable physiological signals is rapidly expanding owing to the speed of ongoing technological advancements in the biomedical domain [10,11]. These signals, especially the ECG, have helped in detecting and diagnosing various types of diseases, including cardiovascular diseases (CVD). CVD is a group of diseases that involves the heart and/or blood vessels [12]. CVD includes coronary artery diseases (CAD) such as angina and myocardial infarction (heart attack), heart failure, stroke, hypertensive heart disease, rheumatic heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, cardiitis, aortic aneurysms, peripheral artery disease, thromboembolic disease and venous thrombosis, among others [12]. CVD is the leading global cause of death and accounts for 17.3 million deaths per year. This number is estimated to increase and reach about 23.6 million by 2030 [13,14]. According to the World Health Organization (WHO), 17.7 million people died from CVD in 2015, representing 31% of all global deaths and about half of the total deaths from noncommunicable diseases (NCD). Out of the 17.7 million, an estimated 7.4 million were caused by CAD, and 6.7 million were caused by stroke. Increasingly, the population mostly affected are those in low- and middle-income countries, where about 80% of these deaths occurred at younger ages [13,15,16]. To predict the occurrence of CVD such as heart arrhythmia, an ECG is generally used by doctors to identify the condition in patients. However, widespread recognition and early prevention of such diseases is a challenge due to the shortage of cardiologists who are proficient in ECG interpretation and the affordability of ECG equipment. Hence, the automated analysis and classification of CVD are expected to play a significant role in accurately detecting the abnormalities of the heart in advance [17].