## 8 Challenges and Open Research Issues  
In the last decade, there has been a lot of significant progress in the literature and industry towards the development of a robust ES and DSS for the interpretation and diagnosis of ECG traces. However, there are still some challenges and open research areas that need to be explored to improve the state-of-the-art approaches. In this section, the identified challenges and open research areas that need concentration are highlighted based on the following categories:  
1. **ECG leads, Privacy-preservation and Mobile App for ECG Interpretation:** At the acquisition step stands the mobile ECG App for collecting and preprocessing ECG traces directly from patients. The major challenge faced by this app is their inability to acquire the full ECG data obtainable with 12-leads. The majority of the mobile apps in the market and the ones proposed in the literature are based on 1-6 leads which could not capture the complete electrophysiological events that occur in the heart. The limitation has affected the quality of interpretation and diagnosis made by ES systems that use such mobile apps. Novel approaches are needed to build mobile apps with the capability to acquire ECG data using 12-leads. In addition, what combination of ECG leads could achieve better performance remains unexplored. Another major challenge is the security of the patient health data captured by the ECG mobile app. With the known sensitivity of the ECG data of patients and their intended privacy, the majority of the ECG apps do not consider securing the sensitive data from unauthorized access as it traverses the network. As an open research direction, lightweight mobile-compliant encryption and steganography techniques are needed to ensure the security and privacy of the patients' data. Lastly, with the proliferation of mobile apps for the interpretation of ECG in the market, the majority of them are not validated. Effort is needed from the research community to carry out empirical validation of those apps in order to ascertain their capabilities and reliability as well as proffer recommendations for their adoption. 2. **ECG Signal Preprocessing and Feature Extraction for ECG interpretation:** Feature extraction is a major step in building ES or DSS for ECG interpretation that identifies salient characteristics from the ECG signals recorded for classification and interpretation. Due to the nonlinearity of the ECG signals, researchers in this area face some challenges in extracting characteristic features from the ECG. A major challenge in dealing with ECG is the existence of a lot of noise in the signal. Although different approaches have been proposed to reduce the effect of the existing noise within the ECG signals in the final interpretation, effective and applicable noise removal techniques are still required to clean the raw ECG signal before extraction. This is important to critically reduce, if not completely remove the classification error of the diagnostic ES built into the ECG signals. Another open research area that needs more concentration and focus is error detection and correction from the ECG signals. As the recorded signals traverse the network, there is the possibility that the signal gets distorted or modified due to noise or transmission error. Therefore, the signal received by the feature extraction module might be dissimilar to the recorded ECG signal. Therefore, a robust error detection and correction technique such as hash function, checksum and cyclic redundancy check techniques can be introduced to further preprocess the signal before actual extraction. High dimensionality of the extracted features is another major problem encountered in building robust ES for ECG interpretation and diagnosis. It is a known fact that high data dimensionality affects the accuracy, speed of classification and prediction algorithms. Majority of previous studies did not consider feature selection as a means to reduce the dimensionality of the extracted ECG features.