## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Knowledge-Based Approach  
A considerable number of works in the expert systems for ECG interpretation focused on developing a knowledgebase as a decision support for clinicians in differential diagnosis. Some of the works are based on collected ECG interpretations or rules from domain experts for building a knowledgebase for ES. [PERSON], [PERSON] [31] proposed a rule-based markup language; ecgRuleML to make ECG rules interpretation accessible externally. EcgRuleML utilizes the eXtensible Markup Language (XML) to represent quantitative rules for measuring different cardiac parameters. To test the ecgRuleML model, rules were defined to assess ST Elevation Myocardial Infarction (STEMI) in a Lux-192 Body Surface Potential Map (BSPM) based on a sample of 74 leads commonly used for STEMI assessment. Experimental evaluations showed that clinicians could use ecgRuleML to define new rules and update existing rules. [PERSON], [PERSON] [57] built a knowledgebase of thirty-eight identified ECG diagnostic interpretations constructed from online ECG library databases. The knowledgebase consists of characteristic data such as rhythm, wave, features, causes, sample images and diagnostic reports for each ECG recording. A query builder was built and connected to the knowledgebase for the cardiologist to use as a decision support tool during the interpretation of ECG. The system, however, did not build a complete expert system to perform automatic interpretation but provided a very beneficial tool in assisting cardiologists in fast interpretation of ECG recordings. Similarly, [PERSON],  
Figure 12: Trends in publications on ES/DSS-based ECG interpretations in the last decade. [PERSON] [71] developed \"Interactive Progressive-Based Interpretation\" (IPI), an expert system based on Differential Diagnosis Algorithm (DDA) integrated with collected 375 human ECG interpretations to build a knowledgebase for differential diagnosis. The system was modelled around the existing 12-lead ECG sensors. A rule-based reasoning algorithm was built to query the system. The IPI + DDA approach read 51.35% of the ECG readings correctly compared to the conventional approach with 42.61% correct interpretation of ECG readings. Other works built the knowledgebase from ECG and patient data collected from sensors and/or Electronic Health Records (EHR). [PERSON], [PERSON] [28] developed a rule-based decision support system for mobile cardiac heart monitoring using ontology. The system uses contextual information regarding the patient's posture and physical activities to detect and signal abnormal or emergency situations. The architecture of the proposed system is divided into three different layers, namely, Data Layer, Decisional Layer and Action Layer. Data layer collects sensor data and represents them in the form of ontology description as a knowledgebase. The Decision layer uses the ontology description and production rules to recognize all possible critical situations. Action layers execute the actions inferred by the Decision layer. The system or overall architecture was implemented in Java programming language and tested for effective performance. Experimental evaluation on a set of 15 healthy people indicated that the developed system reduced false-positive alarms at an acceptable rate. A similar approach was employed by [PERSON], [PERSON] [38] to develop an intelligent specialist decision support system for designing the best treatment plan for patients receiving Ventricular Assist Device (VAD) support before and after VAD implantation. The system works by analyzing patients' data to extract new knowledge and make intelligent decisions. The method was able to support medical and VAD experts through the different phases of VAD therapy. [PERSON], [PERSON] [4] presented a heart disease knowledge-based expert system built from a public heart disease dataset MIT-BIH Arrhythmia Database from Boston's Beth Israel Hospital for processing ECG signals from sensors to perform the diagnosis using a matching algorithm.