Mapping the evolution of respiratory signal research analysis: a fifty-year bibliometric analysis of trends, influential works, and emerging themes

Abstract Despite the critical role of respiratory signal in diagnosing and monitoring respiratory diseases, there is a lack of analysis to map the evolution, trends, and research gaps in this field. Therefore, a systematic bibliometric analysis of 2,017 published documents from Scopus and Web of Science database (1975–2025) is presented to characterize the research landscape, map influential contributions, and capture recent trends. Various tools were used to analyse publication counts, citations, authors, and keywords metadata. The analysis demonstrated a gradual increase in publications spurred by technological advancements and diseases like H1N1 and COVID-19. The result shows that United States was the leader in publication, followed by China that contribute highest citation impact. The highest cited paper had focused on 4D CT imaging, while few studies evaluated techniques for respiratory signal analysis and discussed the utilization of AI in respiratory signal diagnostic. These findings highlight the global and nature of respiratory signal research in multidisciplinary field such as engineering, computer sciences and medicine. The most frequently utilized keywords such as “lung sounds”, “deep learning”, “machine learning”, and “CNN” highlight an important focus on respiratory conditions and the integration of advanced computational methods to enhance diagnostic accuracy and clinical decision-making. In summary, this study adds new bibliometric studies in order to shape developments in clinical practice, support interdisciplinary study, and allow researchers to develop new answers to the problems of respiratory health.