## 9 Conclusions  
An ECG is one of the most widely used recordings in clinical medicine, and several advancements in ECG tracing for diagnosing different diseases have been made over the past few years. The use of ECG-recorded data for disease diagnosis has recently attracted large attention. Even though ECG signals are widely recorded in hospitals, they are also used in epidemiological studies, as well as in large clinical trials. ES and DSS solutions have been developed to aid in the effective diagnosis of arrhythmias and other forms of abnormalities using ECG tracings. This paper presents an extensive review of studies that deal with the development of ES and DSS for ECG interpretation and diagnosis. Ninety-six (96) articles were reviewed, which spanned over a decade from 2010 to 2022 through a careful selection procedure guided by the PRISMA model. During the review, we proposed a taxonomy of the features and methods to categorize the existing studies on ES/DSS for ECG interpretation and diagnosis. Specifically, we identified fiducial, non-fiducial and hybrid approaches as the three major categories of the features that were considered in the reviewed studies. Furthermore, we categorized the existing methods into knowledge-based, fuzzy-based, supervised machine learning, unsupervised machine learning, ensembled-based, neural-based, hybrid and big data-based methods. Summarily, we observed that machine learning-based methods for ECG interpretation started gaining huge attention from 2016 with supervised, ensemble and neural-based methods being consistently applied in the literature. The trends also revealed that traditional supervised learning methods and neural-based methods are dominant for ECG interpretation and diagnosis. On the other hand, unsupervised approaches have witnessed very limited applications for ECG interpretation. Moreover, with the superior performance of deep learning methods and the ability to automatically extract salient ECG features, neural-based methods are gaining more attention from researchers in recent times. Furthermore, big data-based methods are recently used and discussed by researchers. This recent trend can be attributed to the need for processing a large volume of ECG signals. This method is also useful for handling the complexity of processing ECG signals. This review further summarized the challenges and open research issues that need to be addressed in future research to develop efficient and effective ES/DSS solutions for ECG interpretation and diagnosis. Conceptualization, K.S.A., H.A.M., L.A.G. and H.C.; methodology, K.S.A., H.A.M., J.A.O., N.F. and L.A.G. ; software, K.S.A., A.A., E.I. and Y.Y.F. ; validation, A.A.O. and L.A.O. ; formal analysis, K.S.A., N.F. and J.A.O. ; investigation, I.A.S., M.N. and A.Y.G. ; resources, K.S.A., N.F., L.A.G. and H.C.; data curation, H.A.M., I.A.S. and A.Y.G. ; writing--original draft preparation, K.S.A., H.A.M., E.I., Y.Y.F. and A.A.; writing--review and editing, A.A.O., H.C., J.A.O., N.F. and L.A.O. ; visualization, K.S.A. and H.A.M. ; supervision, K.S.A., A.A., H.C. and N.F. ; project administration, N.F. ; funding acquisition, L.A.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project, number PNURSP2022R178, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.