## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Ensemble-Based Methods  
Ensembles of supervised machine learning methods have also been applied in the development of expert decision support systems for ECG interpretation in previous studies. [PERSON] and [PERSON] [34] designed a simple expert system for the detection and classification of arrhythmias based on Random Forest (RF) homogeneous ensemble classifier. DWT was employed for feature extraction by decomposing ECG signals into different successive frequency bands. The five most common arrhythmia beat types extracted from MIT-BIH and St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Databases were used. The results from the study showed that RF classifier resulted in better performance with a very high classification rate with an overall accuracy of 99.33% compared to other employed decision tree methods. RF in combination with Wavelet Packet Entropy (WPE) has also been used in [PERSON] and [PERSON] [135] for building high-performance ECG signal interpretation model. Extensive experimental results as conducted on MIT-BIH Arrhythmia database indicated superior performance to other state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification. [PERSON], [PERSON] [77] also employed RF as the underlying model in the development of a decision support system for classification of permutation entropy (PEn) and log energy entropy (LEE), features extracted from 1000 ECG signals collected from MIT-BIH Atrial fibrillation (AF) database using flexible analytic wavelet transform (FAWT). The LEE feature with RF produced the highest accuracy of 96.84% in the prediction of AF. Recently, [PERSON], [PERSON] [89] proposed a clinical decision support system that employed RF with co-operative co-evolution feature selection method for automatic classification of various clinical datasets, including the Cleveland Heart Disease dataset (CHD) containing 303 instances with 13 features. The approach gave an effective diagnostic accuracy of 93% in heart disease prediction. [PERSON] and [PERSON] [3] also reported better performance accuracy (93.26%) for the GentleBoost homogenous ensemble algorithm applied to automatically detect sleep apnea from ECG signals compared to other algorithms such as k-NN, SVM and ANN.  
In some other studies, a heterogenous ensemble approach has also been employed. [PERSON] and [PERSON] [10] proposed ensemble methods for building a hierarchical expert clinical decision support system from physiological readings generated by Wearable Medical Sensors (WMS) for diagnosing various diseases. Their approach employed stacking, bagging and boosting based on eight (8) different base classifiers. The results from experimentation indicated up to 86% diagnostic performance accuracy on arrhythmia.  
Findings: Both homogenous and heterogenous ensemble approaches have been widely and consistently utilized over the years for developing robust ECG interpretation models within ES systems. In most of the studies, ensemble approaches have been found superior to individual machine learning models. Moreover, RF homogenous ensemble method has gotten more attention and application with very good results in ECG interpretation over the years.