## 7 Taxonomy of Methods for ES Development in ECG Interpretation  
Nowadays, ES has become a new shift in biomedical and bioinformatics technology. This is because ES achieves good performance in a specialized problem domain (such as medical diagnosis) that is considered difficult and requires specialized expertise and skills. It does so by processing the knowledge of medical experts and trying to mimic their skills and intuition. Recently, ECG interpretation has witnessed an increase in the application of ES in developing decision-support systems for diagnosing and monitoring patients with heart diseases [5, 63, 71, 131, 132, 133, 134]. Different methods and approaches have been applied to the modelling, design and development of ES-based decision support systems in ECG interpretation for the diagnosis of heart diseases. This section focuses on the taxonomy and review of ES methods and developmental approaches in existing literature for ECG interpretation. In Figure 10, the identified taxonomies along with the different methods employed under each taxonomy and their frequencies for the past decade are presented after a thorough review of the existing works. Seven (7) different approaches have been employed for developing ES and DSS for automatic ECG interpretation in heart abnormalities and disease diagnosis. They include knowledge-based, fuzzy, supervised learning, unsupervised learning, ensemble, neural-based, hybrid and big data-based approaches. This study identified 62 works that proposed different methods for modelling, designing, and developing ES and DSS for automatic ECG interpretation. Knowledge-based approaches represent expert knowledge computationally using mathematical representations and design, whereas a rule-based system uses the represented knowledge for interpretation of ECG signals. Three methods were employed for building the knowledge-base: (1) interpretation reports from human experts, (2) patient ECG data collected and (3) the combination of expert interpretations and ECG data. Fuzzy approaches interpret knowledge using fuzzy logic or fuzzy inference engine. Fuzzy approaches take a set of input data, fuzzify them and apply some membership functions to the fuzzy set to predict the output. Fuzzy approach has witnessed some applications in ECG interpretation with about five works published. Supervised learning approach analyses a labeled set of inputs and generates a mapping between the input set and the output. It consists of regression and classification algorithms. Majorly, machine learning classification algorithms have been employed in most studies for the interpretation of ECG signals. We identified fifteen (15) works in this category that have been published in the last decade employing statistical, linear, nonlinear and instance-based classification methods. Notably, Support Vector Machine (SVM) has recorded more applications recently. Unsupervised learning approach explores an unlabeled set of input data and determines its underlying structure by generating clusters containing input sets of similar characteristics. Unsupervised learning approach employs clustering and association rule mining algorithms. For the interpretation of ECG, we identified only one work that applied density-based clustering  
Figure 10: Taxonomy of methods for ES/DSS development in ECG interpretation. method. Ensemble approach combines one or more weak supervised learning approaches to build stronger learning model called metamodels. Ensemble methods (both homogenous and heterogenous) were applied in seven works for developing expert system and decision support systems in ECG interpretation as identified in our study. A neural-based approach (called a neural network) is a type of machine learning approach that models the nervous system structure of the brain and how it works. The model is comprised of interconnected nodes organized into a connected network of neurons called neural network. Neurons in the neural networks are activated according to the data inputs (for example, ECG readings).