# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## Framework for Reference Search and Selection Process  
For a thorough survey, the search process was implemented to find the relevant research on using ECG for diagnosis with the help of artificial intelligence. Google Scholar and PubMed were used to find the studies or experiments performed and published, using a timeline from 2018 to 2023. The following terms were used to search: (\"ECG\" or \"electrocardiogram\", \"EEG\" or \"biomedical signals\", \"vital signs\") and (\"heart disease\" or  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Aspects (First ECG Is the**} & \multirow{2}{*}{**Summary**} & \multirow{2}{*}{**Limitations**} \\  & & & **Primary Signal, Second: AI** & & \\  & & **Is the Focus Technology;** & & & \\  & & **Third: Sufficient Discussion** & & & \\  & & **on Challenges and** & & & \\  & & **Future Directions** & & & \\ \hline \multirow{4}{*}{[33]} & \multirow{4}{*}{2023} & \multirow{4}{*}{1 and 2} & Focus: & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{A systematic cardiovascular disorder classification, detection, and identification} \\  & & & & review using ECG. & & \\  & & & & Highlighting datasets, ML techniques, and deep learning tools like CNNs and SVMs. & & \\ \hline \multirow{4}{*}{[34]} & \multirow{4}{*}{2023} & \multirow{4}{*}{1, partial 2 and 3} & Focus: & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{\(\bullet\)} \\  & & & & & \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)\"cardiovascular disease\" or \"Arrhythmia\" or \"health monitoring\") or (\"machine learning\" or \"ML\" or \"deep learning\" or \"DL\" or \"artificial neural network\" or \"convolutional neural network\" or \"ANN\" or \"CNN\" or \"LSTM\"). The number of articles found after using these search terms was significantly large. Therefore, a work plan had to be established to sort out the relevant ones. Figure 1 visually represents the article selection process used for this literature review. This process involved four stages: identification, screening, eligibility, and inclusion.  
The first stage, identification, involved searching for articles on Google Scholar and PubMed, retrieving 1214 articles. After removing duplicates, the number of articles was reduced to 856. These articles were subjected to the second stage of the selection process, screening. At this stage, the conference abstracts and articles with access restrictions were removed (from 856 to 560). Second, the titles and abstracts of the articles were reviewed to identify articles irrelevant to the study or outside the scope of the research. Four hundred thirty-five articles were removed during this stage (from 560 to 125). The third stage of the selection process involved a thorough review of the full texts of the remaining 125 articles identified as potentially relevant during the screening stage. During this review, 82 more articles were eliminated due to missing evaluations or assessments, inadequate dataset or sample numbers, the deficient rationale behind evaluations, and sketchy details of the experiment or method used. Finally, 43 articles were deemed relevant to the literature review after completing the eligibility stage. They met all the criteria specified in the framework. This rigorous selection process ensured that only high-quality, relevant articles were included in the final review, which would help to provide accurate and reliable findings.  
Figure 1: The article selection process for the literature review.