## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Supervised Learning Methods  
Statistical supervised learning approach has earlier been used in developing expert system models for ECG interpretation and classification. [PERSON], [PERSON] [41] employed Dynamic Bayesian Networks (DBN) to automatically detect heart arrhythmia (specifically PVC) in recorded long-term ECG. The expert system was tested on MIT-BIH arrhythmia dataset using the Bayesian Network Toolbox of MATLAB. The results indicated that DBN outperformed the static Bayesian Networks with a maximum accuracy of 98%. Quadratic Discrimination Analysis (QDA) was applied in [PERSON], [PERSON] [54] to develop a classification framework to predict Atrial Fibrillation (AF) recurrence in patients with direct-current electric (DCE) cardioversion from the underlying structure of ECG traces based on the matching pursuit (MP) feature extraction technique. The technique achieved 100% and 95% sensitivity and specificity, respectively, indicating the capability of the approach in providing reliable information for effective management of AF. Some other studies considered linear and nonlinear supervised learning classifiers for automatic ECG interpretation. [PERSON], [PERSON] [55] applied both linear (J48, Jrip and ANN) and nonlinear (SVM) classifiers in building a web-based cardiovascular disease detection system that can identify different heart arrhythmias. The system used personal information of patients as input, together with several features extracted from the ECG signal analysis. The results showed that the highest classification accuracy was obtained by the linear classifiers, specifically J48, while the nonlinear classifier (SVM) had poor performance. The same direction (applying both linear and nonlinear classifier) was followed by [PERSON] and [PERSON] [87] in developing automatic classification models for four types of arrhythmias using Decision Tree, SVM and RF. However, the deep learning model CNN along with k-NN approach were utilized for feature extraction after ECG signals were converted to image format. The approach proved very effective with better performance than SVM using polynomial kernel. Instance-based supervised learning approach has also witnessed some applications in designing expert systems for ECG interpretation. [PERSON], [PERSON] [58] proposed a decision-supportive ECG pattern recognition expert system using k-NN classifier based on Independent Component Analysis (ICA) with DWT feature extraction for five cardiac arrhythmia classes: Non-ectopic (N), Fusion (F), Ventricular ectopic (V), Supraventricular ectopic (S) and Unknown (U) beats. The approach was compared with SVM, ANN and PNN based on different combinations of PCA and ICA with SVM, ANN and PNN. The proposed approach applied on MIT-BIH Arrhythmia dataset outperformed the existing methods with an overall accuracy of 99.77%. k-NN has also been applied to DWT for noise removal, DCT for feature extraction and PCA for dimensionality reduction by [PERSON] [67] to detect coronary heart diseases from ECG signals collected from European databases. The approach outperformed SVM with an overall detection accuracy of 99.62%. k-NN has also been applied by [PERSON] and [PERSON] [6] for developing an expert system for ECG-based human identification using the fusion of characteristic features of ECG and Information theory indices extracted using Information Gain Ratio (IGR). The approach yielded a higher average accuracy rate of 97.62% \(\pm\) 1.92 compared to the state-of-the-art approaches. In a current study, [PERSON], [PERSON] [93] employed k-NN based on Dual-Tree Complex Wavelet Transform for building heart beat arrhythmia classification decision support system using MIT-BIH Arrhythmia dataset. With the approach reaching an accuracy of 98.92%, it can be used effectively as a decision support system for detecting cardiac abnormalities. SVM has witnessed more applications in the literature compared to other supervised learning approaches for ECG analysis up till the present study.