## 1 Introduction
A CVD diagnosis from an ECG has significant importance in the biomedical domain. As reported in the medical literature, the two widely known heart diseases are cardiac arrhythmias and myocardial ischemia. Myocardial ischemia occurs due to the shortage of blood supply to the myocardium, which affects the morphology of the heart ECG signal. Conversely, cardiac arrhythmias indicate abnormalities in the rhythm of the heart, which may lead to cardiac arrest, chest pain or sudden cardiac death [18]. For an effective diagnosis of CVD using ECG signal data, several expert systems (ES) and a decision support system (DSS) have been developed. ES is a problem-solving system that can produce a better performance in a specialized problem area that typically requires expert knowledge and skills. An ES program processes the knowledge of experts to mimic their skills, thinking and perception [19]. ECG data collected from the sensors can be transferred to a cloud server to conduct a preliminary automatic diagnosis based on the knowledgebase of the ES without the presence of medical experts [4; 20]. A knowledgebase is initially developed using a large sample dataset, which can be updated frequently by specialists to improve the accuracy of the ES and DSS for disease prediction [4]. Such ES and DSS systems will assist patients in detecting abnormalities in their hearts before seeing their doctor or physician for treatment [2; 4; 5]. In this paper, a comprehensive review of existing state-of-the-art studies on ES and DSS for ECG interpretation and diagnosis is presented. To avoid duplication of the review, we conducted a wide range of literature searches from reputable databases to identify any existing review articles on ES and DSS, specifically for ECG interpretation and diagnosis. Indeed, there have been review studies on related topics. The most recent of these studies are the review articles by [16; 21; 22] published in IEEE Xplore, PubMed and ScienceDirect, respectively. [PERSON], [PERSON] [16] presented a survey of smart health monitoring approaches by considering different parameters such as the framework, targeted population, monitoring device, processor utilized, integrated sensors and communication module. This review summarized different technologies implemented for health monitoring towards making the healthcare domain smarter. They outlined smart and traditional monitoring systems by categorizing them as mobile, wearable and remote health monitoring systems. The authors presented challenges hindering such a system development. To understand the real-time societal need for this smart healthcare system, a survey questionnaire was conducted targeting 183 valid independent entries collected from various regions. The study concluded that there is a need for developing an integrated platform to achieve a comprehensive healthcare monitoring system that is adaptable to different situations. [PERSON], [PERSON] [21] conducted a narrative review on a number of mobile phone apps that are capable of monitoring atrial fibrillation (AF), heart rate (HR) and heart rate variability (HRV). The authors searched the PubMed and EMBASE databases until October 2018 for relevant articles on the subject. They observed that the operation of the mobile apps involved the use of either a photoplethysmographic recording or a handheld external electrocardiographic recording device connected to the mobile phone or wristband. The authors concluded that mobile phone apps have a significant role to play in the screening, diagnosis and monitoring of arrhythmias and HR. [PERSON], [PERSON] [22] investigated the application of artificial intelligence (AI) in the biomedical domain. The authors summarized the latest developments of applications of AI in the biomedical fields, including disease diagnosis, living assistance, biomedical information processing and investigation. Their review considered 20 years of literature from 1999 to 2018 and concluded that the application of AI in biomedicine is still in its infantile stage.