## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Hybrid Methods  
As asserted by statistical studies carried out by [PERSON], [PERSON] [44], the earliest hybrid expert systems employed neuro-fuzzy and rough neural expert systems. Nevertheless, few hybrid expert system approaches have been presented in the literature for ECG interpretation and classification.  
[PERSON], [PERSON] [42] proposed an ECG-based decision support system to deduce the cardiac condition of patients by hybridizing a Type-2 FML-based fuzzy inference machine with ontology to develop a knowledge-based fuzzy inference system. The system employed ontologies to improve the expressiveness of ECG knowledgebase and Type-2 FML to model type-2 fuzzy systems. Empirical evaluation on 40 subjects revealed that the approach is feasible and can work effectively to infer the cardiac quality of patients. Additionally, when applied to a set of exams students, it was revealed that the diagnostic framework proposed yields good performances in terms of precision and recall. A hybrid framework based on k-NN and a rule-based system were proposed by [PERSON], [PERSON] [52] for false alarm reduction in a continuous cardiac monitoring system using 3D acceleration ECG signal. k-nearest neighbor was employed for constructing the model and rule-based expert system which consists of seven rules were constructed for false alarm detection. An accuracy of 96.7% with reduced false alarm rate was recorded based on validation on two datasets collected from the MIT-BIH arrhythmia database and 10 subjects conducting daily living activities (DLAs).  
Recent works have focused on hybrid deep learning approaches. [PERSON], [PERSON] [81] presented a novel two-staged approach that combines a deep learning model with a supervised machine learning approach for anomaly detection and classification in ECG signals. In their work, LSTM, a recurrent deep neural network, was used for initial anomaly detection. In the first stage, LSTM was trained on large normal ECG patterns to identify the anomaly using prediction error. The error output was used in the second stage to predict the actual anomaly class in the pattern using Multi-Layer Perceptron (MLP), SVM and Logistic Regression. Seven types of anomalies were investigated including Premature Ventricular Contraction (PVC), Atrial Premature Contraction (APC), pacing Beat (PB), Ventricular Bigeminy (VB), Ventricular Couplets (VCs), Ventricular Tachycardia (VT) and Right Bundle Branch Block (RBBB). Experimentation on MIT-BIH Arrhythmia dataset indicates that the proposed approach gave a promising result with the highest accuracy recorded by SVM (83.3%). This result indicates that the error vectors represented by their summary features carry useful predictive information about the actual ECG anomaly type. To build a deep learning model for rapid and effective detection of left ventricular hypertrophy (LVH) using 12-lead ECG, [PERSON] et al. [104] combined CNN with LSTM. The experiment was performed with the model on the echocardiogram and ECG of 1863 patients obtained within 1 week of hospital admission. The CNN-LSTM model achieved AUC 66%, sensitivity 72% and specificity 60% for male patients compared to that of female patients with AUC 59%, sensitivity 50% and specificity 71%.  
**Findings:** Although few studies have considered hybrid approaches for ECG interpretation in the last decade, they have been found to perform better than singular approaches as indicated by the performance results reported in the considered studies. Hybrid approaches capitalize on the strength of the combined models to reach a better and optimized performance level. The majority of the hybrid approaches hybridized other methods with deep learning model as the most viable method for complex ECG signal interpretation. Hybrid approaches have also been found as useful methods in real-life implementation of ES applications for ECG interpretation and diagnosis.