Mapping the Landscape: A Bibliometric Analysis of Deep Learning in Cardiac Research (2014-2024)

Abstract

Deep learning has emerged as a promising technology in the field of biomedical signal processing, particularly for the purpose of automating the identification and categorization of cardiac arrhythmias based on electrocardiogram (ECG) data. The purpose of this study is to offer an introduction to deep learning algorithms for identifying and categorizing ECG arrhythmias. Additionally, a bibliometric analysis of research publishing trends, citation patterns, author collaborations, and institutional affiliations is presented. In the introduction, the clinical significance of ECG arrhythmias, the comparison between traditional methodologies and deep learning methodology, the most common deep learning architectures, the datasets that are extensively utilized, and the criteria for performance evaluation are discussed. There is also a discussion of the difficulties and potential advances that lie ahead in the field. Quantitative insights into academic production are provided by the bibliometric study, which highlights notable contributors, newly emerging research concerns, and possible areas for future growth. In order to facilitate collaboration and foster innovation in cardiovascular healthcare, the purpose of this study is to provide a comprehensive understanding of the current state-of-the-art in deep learning for ECG arrhythmia detection and classification. This will be accomplished through a systematic analysis of research trends and a synthesis of the existing literature.

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