International publication trends in Biomedical Signal Processing research: A bibliometric analysis (2009-2018)

Background This study aimed to analyze and assess the scientific outputs of biomedical signal processing by using bibliometric analysis.Methods Data were obtained from the WoSCC of Thomson Reuters, on January 21, 2019. VOSviewer (Leiden University, Van Eck and Waltman, Netherlands) and carrot 2 (Poznan University of Technology, Dawid Weiss, Poland) were used to analyze the knowledge maps and clusters of countries, research area and hot topics.Results A total of 335 articles on biomedical signal processing were identified. The number of publications increased only mildly during from 2009 (n=14) to 2018 (n=62). The majority of articles were published in the USA, and the leading institute was University of California System. Van Huffel S was the top authors on the topic, and the research area of “Engineering” generated the most publications. Cluter analysis (keywords and terms) indicated that “algorithm” and “extracted features” was the most hot topics on biomedical signal processing.Conclusion Overall, Through analysis of biosignal single processing related research in the past 10 years, the results found that the close international cooperation in this field, and the future research trends may be the signal acquisition methods and signal processing algorithms. They can provide reference for researchers in related field to choose research directions and find cooperative resources.

was selected for visual analysis of bibliometrics, which can convert the large amount of literature data into a bibliometric map [10]. The researchers can understand the knowledge structure more directly, and find the hot topics and research areas from the maps. Biomedical signal processing have published a substantial amount of original research based on care digital signal processing, electrocardiosignal, and brain-computer interface, etc [11][12][13]. However, there are no bibliometric analyses that have explored research related to biomedical signal processing. This study used CitespaceIII to analyze biomedical signal processing articles retrieved on the Web of Science (Thomson Reuters Company) database and provides a retrospective and current view of the mainstream research on biomedical signal processing throughout the world.

Methods
All the data for this study were obtained from the Web of Science Core Collection (WoSCC) of Thomson Reuters, on January 21, 2019. The WoSCC, which includes the Social Sciences Citation Index, Current Chemical Reactions, and Index Chemicus, is the most frequently used source of scientific information. The search term "biomedical signal processing" was used to retrieve titles, keywords, author information, abstracts, and references published from 2009 to 2018. The following search string was used: TS=("biomedical signal processing") AND document type:(ARTICLE OR REVIEW). Data were downloaded from WoS in "Full record and cited references" and "Comma Separated Values (CSV)" formats.
The information statistics of this study mainly include authors, research institutions, countries and research fields, and the indicators were publication and citation. Microsoft Excel 2016 (Microsoft,  4 2008 (n=14) to 2017 (n=62), the number of citation increased substantially from 2008 (n=5) to 2016 (n=1,081) ( Figure 1). 269 studies (80.03%) were cited at least once, with an average of 13.08 citations per article for 4,381 total citations. We calculated the linear regression and found y=4.77x-7.27 with r 2 =0.884 (publication); y=125.66x-258.60 with r 2 =0.954(citation), respectively ( Figure 2).

Hot hotspots
There were 335 papers on biomedical signal processing research had been included in this analysis. In Figure 5, the left hand is the network of keywords, and the right hand is the network of keywords plus. In this study, keyword plus clustering results were used as the research direction of hot topics. Therefore, there are 1,114 keywords had been used to analyze by VOSviewer. The 53 keywords Publication and citation trends Biomedical signal processing research publication trends show that the publication of biomedical signal processing related articles increased slowly in the past decade, but its citation increased rapidly. This interest may be due to the number of basic research in this field is relatively small, but its application field is relatively wide, especially the interdisciplinary subject like biomedical signal processing.
From the 10 most-cited articles, their main research direction comes from the algorithm and mathematical model of biological signal processing. The most-cited is the research from Cifrek M, which summarized the signal methods and techniques from the standpiont of applicability to sEMG signals in fatigue-inducing situations relevant to the broad field biomechanics [16]; and this review as a basic konwleage had been mainly cited in research area of sports science, engineering biomedical and neurosciences, et al [26][27][28].

Distribution of scientific research forces
The USA published the most research on biomedical signal processing; this phenomenon is the same as many other research fields. This shows that the United States is a leader in the field. However, it is worth noting that German, Turkish and Italian research publications are small, but the average citation is high. In addition, although Peoples R China has the same publication as Germany, its quotation is the lowest in Top10, which indicates that the quality of academic papers in this field needs to be further improved. 2) Classification accuracy: Through optimization algorithm, the accuracy of signal recognition model can be improved, including time series, wavelet analysis and machine learning [31][32][33].
3) Electrocardiogram ECG: Because of ECG is highly correlated with the diagnosis of many diseases, it has become an important part of the field of biomedical signal processing; research directions include signal monitoring, model algorithm optimization, disease screening, etc [34][35][36]. 4) Proposed model: According to the data characteristics of biological signals, an algorithm model for signal recognition is constructed [37][38]. 5) Proposed design: According to the requirement of biological signal acquisition, the hardwares and equipments were designed and improved [39][40][41].
From the clustering analysis of keywords, the main research contents of these 4 clusters are as follows: 1) Cluster 1 (model and algorithm): Establishment and optimization of mathematical models in the process of biological signal processing, including signal extraction, recognition and classification, and data conversion, etc [27,[29][30].
2) Cluster 2 (physiological signal recording): Signal acquisition and recording, including the design of signal acquisition hardware, the selection of recording methods and the determination of signal types, etc [39,[42][43]. guidance, the key term extraction program development and the revision of this paper. All authors read and approved the final manuscript. University of Bologna 6 (1.80%) 9 Chen X 3 (0.90%) 10 University of Oxford 6 (1.80%) 10 Cifrek M 3 (0.90%)  Figure 1 Guidelines Flow Diagram.

Figure 2
The number of publication and citation from 2009 to 2018.

Figure 3
The national collaboration network of biomedical signal processing.

Figure 4
The terms clusters of biomedical signal processing.

Figure 5
The network of keywords on biomedical signal processing.