## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Neural-Based Methods
The classification was divided into internal and external validation in a multicenter study involving 5 New York City hospitals data. For internal testing, AUC of 94%, 82% and 89% were achieved for classifying left ventricular ejection fraction (LVEF) dysfunction in three categories, respectively. For the prediction of the composite right ventricular ejection fraction (RVEF) outcome, AUC of 84% was achieved for both internal testing and external validation. Still on CNN application for ECG interpretation, [PERSON] et al. [106] utilized CNN with fully connected layers on ECG features extracted using CWT to overcome the problem of long segment-based ECG detection method. The use of smaller segments makes the model computationally simpler and faster without compromising the model performance. The model achieved average sensitivity, specificity and accuracy of 98.87%, 99.85% and 99.65%, respectively for arrhythmia detection on MIT-BIH arrhythmia dataset. Proposing a new diagnostic framework for mobile electrocardiogram signal, [PERSON] et al., [98] introduced sliding segmentation with large-scale convolution kernel into electrocardiogram interpretation for detecting heart defects. A one-dimensional CNN was employed for feature extraction, and an evaluation on the empirical dataset yielded an F1 score of 84.16%, making it the first to achieve that record performance accuracy for mobile ECG interpretation. In order to evaluate the performance of a deep learning model in the detection of CRP levels from the ECG in patients with sinus rhythm, [PERSON] et al. [103] proposed CNN with fully-connected layer. The CNN model was trained and validated to detect CRP levels from 12 leads ECGs recordings collected from an epidemiological survey of heart disease in Guangzhou containing 12,315 samples. After training and validation, the AUC of the validation set was 0.86 with sensitivity, specificity of 89.7 and 69.6%, respectively, while the AUC of the testing set was 0.85 with sensitivity, specificity of 90.7 and 67.6%, respectively. The findings from the study proved the existence of inflammation-related changes in cardiac electrophysiological signals by detecting CRP levels. [PERSON] et al. [105] focused on predicting the biological age of patients from ECG data with the use of a kernel-based CNN deep learning model. Contextual information about the patients, such as age and gender, was also integrated into the network. 71,741 cases ranging from 20 to 80 years old were used. Training data is 32,707, testing is 8295 and validation is 30,469 ECGs. All-cause mortality, cardiovascular-cause mortality, heart failure diabetes mellitus, chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke, coronary artery disease (CAD), atrial fibrillation (AF) and hypertension were examined during the model development. It was found that the ECG-age estimated via DLM provides additional information for CVD incidence; older ECG-age is correlated with not only mortality but also other CVDs. **Findings:** It is evident from our study that neural-based approaches have gained a lot of interest from researchers with respect to the quantity of studies done and the quality of results reported in the literature. Neural-based approaches were able to effectively explore and analyze the complex features of ECG signals and produce accurate interpretations in terms of detecting heart defects. It was also found that researchers are now focusing more on deep learning models, most especially different variants of CNN for developing ES/DSS for ECG analysis and interpretation.