## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Neural-Based Methods  
Several configurations of neural networks have been employed for automatic prediction of various heartbeat disorders based on ECG interpretation. [PERSON], [PERSON] [30] presented a rule-based expert system utilizing an artificial neural network model for predicting the severity of health episodes in heart disease patients. The model made use of the fusion of ECG signals from wearable sensors and other measured patient parameters such as respiration, oxygen saturation (Sp02) and temperature. The approach gave a competitive performance result. Although, with low accuracy, the system was still found very useful by cardiologists for providing critical information about the health status of the patients. Simple Artificial Neural Network (ANN) was also applied by [PERSON], [PERSON] [53] for the classification of cardiac arrhythmias from ECG beats based on dual-tree complex wavelet transform. The model evaluated on 12 files from MIT-BIH database yielded an overall accuracy of 97.8%. [PERSON], [PERSON] [59] applied General Regression Neural Network (GRNN) to automatically classify personalized heart beats from long-term ECG recordings. Performance on 103 datasets yielded 95% accuracy. MLP neural network-integrated with sample entropy and Fast Fourier Transform was used by [PERSON], [PERSON] [35] to automatically classify heartbeat arrhythmia. The study in particular, evaluated four databases from PhysioNet consisting of AHADB, CUDB, MITDB and NSTDB. The proposed integrated approach produced better episode sensitivity and positive predictivity of 80% under this setting than the state-of-the-art having 75%. Recently, [PERSON], [PERSON] [33] applied a feed-forward ANN for automatic classification of five types of arrhythmia beats including NB, LBBB, APB, RBBB and PVC. The technique was applied on Physionet MIT-BIH dataset based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for extracting linear features from the nonlinear ECG signal. Their approach was compared with the previous sixteen approaches and produced a better diagnostic accuracy of 99.9%. ANN was also explored by [PERSON], [PERSON] [91] for designing intelligent methods for fatigue detection based on EEG signals. Their findings revealed that the artificial neural networks can be used to recognize the alert and drowsy state of patients from ECG recordings. Moreover, the developed system was found effective for analyzing scanned results, as reported by the participating doctors and cardiologists. In another work, ELM, a neural network model, was applied by [PERSON] and [PERSON] [99] for ECG signal recognition. Although the study proposed a deep learning model to handle class imbalance during feature extraction, the actual classification/interpretation was performed with ELM. The model achieved sensitivity of 86.8%, specificity of 91%, accuracy of 89% and precision of 90.20% indicating its effective recognition performance. Advanced neural networks with deep structures have also been utilized in recent works for ECG interpretation. An extensive review on state-of-the-art deep learning studies for heartbeats detection using ECG has been presented in [95]. As a step ahead, [PERSON], [PERSON] [60] proposed a deep neural network with pretraining for automatic screening of patient ECG recordings for heartbeat abnormalities. The pretraining was done with the Restricted Boltzmann Machine (RBM) and the approach was tested through cross-validation on 416 test patients. The results from the evaluation indicated pretrained neural networks yielded an accuracy of 85.52% better than other networks without pretraining. This is due to the fact that neural networks with pretraining automatically generate a representation of the input data with no consideration for data annotation. Recurrent neural network, specifically LSTM, has also been applied by [PERSON], [PERSON] [69] to learn the time correlation among ECG signal points for classifying ECG beats with different heart rates.