## 8 Challenges and Open Research Issues
Therefore, feature selection approaches such as filter, wrapper, embedded and hybrid methods can be applied to ECG extracted features to reduce the high dimension and, in turn, improve the efficiency and accuracy of the diagnostic ES for ECG interpretation. iii. **ECG Interpretation and Diagnostic Models:** Various approaches such as Knowledge-based, fuzzy-based, machine learning (majorly supervised), neural networks, ensemble and hybrid approaches have been developed in the last decade to interpret the ECG traces and diagnose various abnormalities. Nevertheless, there is still room for improvement and extension of the current state-of-the-art approaches. Due to the complex structure of the ECG traces, especially the 12-led ECG, previously applied approaches have shown some limitations in correctly interpreting the ECG data and diagnosing multiple abnormalities. More studies are needed, especially in the application of deep learning and hybrid deep learning techniques to correctly interpret the complex 12-lead ECG data and diagnose more abnormalities. Moreover, the number of samples/participants used for testing the developed diagnostic models in the existing studies is too small to generalize their effectiveness. More extensive testing methods are required to be carried out by new studies in ES development for ECG interpretation to confirm the reliability of the developed approaches in diagnosing heart abnormalities from ECG recordings. Another important challenge encountered in building classification models for ES in ECG interpretation is the class imbalance of the training dataset. Majority of the models in the existing studies used MIT-BIH Arrhythmia database for training which contains more percentage of normal than abnormal ECG. This class imbalance has been shown to cause overfitting. Although few studies tried to tackle this problem by applying ensemble methods, nevertheless, new studies in this area should consider deploying different techniques for class imbalance correction such as oversampling and undersampling to prevent classification overfitting, which greatly affects the accuracy of the developed ES or DSS for ECG diagnosis. Another potential research area in this regard is the incorporation of other contextual information such as Body Mass Index (BMI), blood pressure, age, gender and other similar parameters into ECG traces. This information could enhance the capability of the diagnostic models within the ES to accurately diagnose heart abnormalities. iv. **ES/DSS Development:** The ES/DSS is an application that provides real-time interpretation and diagnosis of abnormalities from the ECG recording of patients based on the underlying classification models. It has witnessed a series of developments in the last decade. However, majority of the developed systems are based on static knowledge. The ES/DSS applications for ECG interpretation and diagnosis can be improved upon in new studies by introducing adaptive approaches that update ECG knowledgebase to provide a more precise diagnosis. This can be achieved by carrying out system validation on independent datasets for generalizability and new patterns can be included in the knowledgebase of ES system. Moreover, more studies should consider novel techniques for the fusion of ECG and other human biometrics for accurate identification and authorization of patients within the DSS system for ECG management. v. **Deep learning and related challenges:** Although existing studies on ECG interpretation and diagnoses have focused more on the application of traditional machine learning approaches, recent advancement in automated feature extraction of ECG characteristics using deep learning methods has attracted a great deal of attention in the research community. Nevertheless, the predictive accuracy of these deep learning models still needs to be improved to be on par with the traditional machine learning approaches.