# A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram

## TL;DR

This paper analyzes the 50 most cited AI-related ECG studies to track trends and key contributors in the field.

## Contribution

The study provides a bibliometric overview of top AI in ECG research, highlighting trends and collaboration patterns.

## Key findings

- The 50 most cited AI in ECG articles were published between 2000 and 2020 with an average of 488 citations per article.
- ‘IEEE Transactions on Biomedical Engineering’ and ‘Computers in Biology and Medicine’ published the most articles in this domain.
- The USA and China contributed 14 articles, and Singapore showed the most international collaborations.

## Abstract

Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The objective of this bibliometric analysis was to identify the 50 most cited articles in the domain of AI in ECG, emphasizing publication trends, citation metrics, prominent authors and journals, leading institutions, and significant contributing countries.

The 50 most cited articles on AI in ECG were published between 2000 and 2020 across 25 journals. The mean citations per article were 488.0, with the highest citations count being 1870. ‘IEEE Transactions on Biomedical Engineering’ and ‘Computers in Biology and Medicine’ published the highest number of articles, while Rajendra Acharya U and RS Tan were the most contributing authors. The USA and China had a total of 14 publications, and Singapore was the country with most collaborations.

This bibliometric analysis provides clinicians and researchers with an overview of evolution and progression of AI in the domain of ECG. Improved collaborations among different countries and institutions are essential for achieving advancements in the utilization of AI in ECG.