## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Neural-Based Methods
Empirical evaluation of the proposed method on MITDB dataset resulted in 99.7% accuracy for VEB detection and 99.3% accuracy for SVEB detection The approach achieved state-of-the-art performance after evaluation. Evolutionary neural system has also been applied for heart disease prediction based on ECG interpretation. [PERSON] [76] employed evolutionary neural systems based on PNN, SVM and RBFNN for automatic detection of myocardial dysfunction from 1000 fragments of ECG signals collected from the MIH-BIH Arrhythmia database for one lead and MLII from 45 patients. The Evolutionary systembased on SVM produced the best result with accuracy of 98.99% and specificity of 99.46%, In another work, a deep Two-Stage ANN with an adaptive filter approach was applied to measure fetal heart rate from mother surface ECG recordings by [PERSON] and [PERSON] [85]. The approach was able to filter and detect the fetal signal marred by a pulse signal from the mother with a satisfying level of accuracy on 10 clinical cases from Physionet. The approach also recorded an SNR value of 34 and MSE value 0.981 and indicating a reduced error in detection. CNN, a deep learning model has also witnessed a considerable application for ECG interpretation in recent times. [PERSON] et al. [100] applied kernel based deep learning method with CNN for automatic classification of cardiac arrhythmia from 12-lead ECG. Additionally, SHapley Additive exPlanations method was used to interpret the model's behavior at both the patient and population levels. The results on the empirical dataset revealed that 12-lead ECGs produce better performance than single lead ECG. Additionally, The best-performing leads are lead I, aVR and V5 among the 12 leads. Lastly, the proposed approach outperformed the other four machine learning models that are based on feature engineering with an F1 score of 81.3%. [PERSON] et al. [101] developed an improved deep learning model for detecting drug-induced and congenital long QT syndrome from ECG signal. The authors applied CNN with dense layer for classification of ECG features extracted with kernel-based method. The results indicated that the CNN model outperformed other machine learning models based on QTc features with an achieved AUC of 98%. Moreover, [PERSON] et al. [107] develop deep learning CNN model as a solution for the identification of patients with impaired heart rate recovery (HRR) based on resting ECG. The model was tested among UK Biobank participants who had undergone exercise testing. The developed deep learning model moderately correlated with actual HRR (r = 0.48, at 95% confidence interval). [PERSON] et al. [102] applied CNN based on SqueezeNet deep learning network for 12-, 6-, 4-, 3- and 2-lead ECG data. The model integrates transfer learning and stochastic gradient with momentum optimizer as solver in the model. The proposed algorithm obtained a score of 0.30 on 12-lead, 6-lead, 4-lead, 3-lead and 2-lead test data. The algorithm was ranked among the best in the 2021 PhysioNet/Computing in Cardiology Challenge. In another work, a multi-layered deep learning CNN method with transfer learning and multitask learning was presented by [PERSON] et al. [109] for the identification of patients suffering with Brugada syndrome arrhythmia using ECG features. The authors used 276 ECG with a type 1 Brugada ECG pattern and another 276 non-Brugada type ECGs to train and test the deep learning model. These samples were selected from a total of 2257 RBBB and 2257 Non-RBBB ECGs data collected from ECG database at the Taipei Veterans General Hospital from January 2009 to February 2019 through a transfer learning process. The model achieved 96%, 88.4%, 89.1% in AUC, sensitivity and specificity, respectively. [PERSON] et al. [108] also developed a deep learning model for identification of left and right ventricular dysfunction from ECG dataset. The authors trained a two-dimensional CNN to classify left and right ventricular dysfunction simultaneously.