| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Medicine | 1811 | 60 | 64 | 14 |
The objective of the journal is to combine knowledge in the areas of Internal medicine, Biomedical engineering, Cardiology, Artificial intelligence and Electrical impedance tomography. The Internal medicine study tackled is a key component of adjacent topics in the area of Anesthesia. The journal focuses on Biomedical engineering but the discussions also offer insight into other areas such as Electrical impedance and Blood flow.
It focuses on Electrical impedance research which is adjacent to topics in Electronic engineering. The Electronic engineering study featured in the journal draws connections with the study of Acoustics. Hemodynamics is a major topic of Cardiology research.
The research on Artificial intelligence featured in it combines topics in other fields like Computer vision, Signal, Signal processing, Machine learning and Pattern recognition. The concepts on Electrical impedance tomography presented in Physiological Measurement can also apply to other research fields, including Algorithm and Iterative reconstruction.
The published papers mainly tackle studies in Biomedical engineering, Artificial intelligence, Electrical impedance tomography, Internal medicine and Cardiology. The study of Artificial intelligence in the most cited papers encompasses disciplines such as Pattern recognition, as well as fields such as Speech recognition, all of which overlap with one another. The works on Cardiology tackled in the most cited papers bring together disciplines like Anesthesia, Photoplethysmogram and Blood pressure.
Cardiology, Internal medicine, Artificial intelligence, Pattern recognition and Biomedical engineering are among the topics commonly tackled in Physiological Measurement. Topics in Cardiology explored in it were investigated in conjunction with research in Gestational age, Breathing and Heart rate variability, Blood pressure, Heart rate. While Breathing is the focus of Physiological Measurement, it also provided insights into the studies of Electrical impedance tomography and Mechanical ventilation.
Physiological Measurement explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning and Signal processing. It facilitates discussions on Pattern recognition that incorporate concepts from other fields like Photoplethysmogram, Signal, Deep learning and Spectrogram. Biomedical engineering study tackled is connected to the field of Supine position.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Physiological Measurement (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Physiological Measurement (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 3.75% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 7.14% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.49% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.99% of all publications and 73.38% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Understanding the specific background and credentials that are often required for professionals in various physiological fields can provide additional context for our discussions on research topics. For instance, certain studies might require specialized expertise, which often entails unique licensing requirements. For those looking to create a promising career in physiological measurement, it is crucial to grasp the educational prerequisites and licensing requirements specific to your preferred field. One example of this is those practicing in the field of nursing. In Minnesota, there are distinctive licensing benchmarks to meet before one can fully practice in the nursing profession. It's imperative that you are conversant with the process. For more details, please visit and read through our minnesota nursing license requirements nursing license education requirements page to get a comprehensive understanding. A better comprehension of these stipulations will enable you to have effective research engagement with professionals in the field. Moreover, knowing the licensing requirements also positions you adequately for any career advancement opportunities in your respective discipline.
Qiao Li;Qichen Li;Ayse Selin Cakmak;Giulia Da Poian
(2021)Andrew J Goodwin;Danny Eytan;Danny Eytan;Robert W Greer;Mjaye Mazwi
(2020)Julien Oster;Jemma C Hopewell;Klemen Ziberna;Rohan Wijesurendra
(2020)Victoria Ouyang;Botong Ma;Niccolo Pignatelli;Shantanu Sengupta
(2020)Robert W Spitz;Zachary W Bell;Vickie Wong;Ricardo B Viana;Ricardo B Viana
(2020)Robert W Spitz;Zachary W Bell;Vickie Wong;Yujiro Yamada
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