Biomedizinische Technik

Top Research Topics at Biomedizinische Technik?

The journal mainly deals with areas of study such as Biomedical engineering, Artificial intelligence, General interest, Internal medicine and Cardiology. The studies in Artificial intelligence featured incorporate elements of Computer vision and Pattern recognition.

• Biomedical engineering (8.29%)
• Artificial intelligence (4.81%)
• General interest (4.29%)

What are the most cited papers published in the journal?

• Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet (368 citations)
• [Cap image--a new kind of computer-assisted video image analysis system for dynamic capillary microscopy]. (227 citations)
• A novel transcutaneous vagus nerve stimulation leads to brainstem and cerebral activations measured by functional MRI (144 citations)

Research areas of the most cited articles at Biomedizinische Technik:

The most cited articles generally zeroe in on subjects such as Biomedical engineering, Artificial intelligence, Pattern recognition, Electroencephalography and Internal medicine. The studies tackled in the published articles, which mainly focus on Biomedical engineering, apply to Electrical impedance as well. The Internal medicine research tackled in the most cited articles is interrelated with Cardiology which concerns subjects like Blood pressure and Heart rate variability.

What topics the last edition of the journal is best known for?

• Internal medicine
• Surgery
• Composite material

The previous edition focused in particular on these issues:

The concepts of Artificial intelligence, Pattern recognition, Artificial neural network, Signal and Support vector machine are tackled in Biomedizinische Technik. While the journal focused on Artificial intelligence, it was also able to explore topics like Filter (signal processing) and Computer vision. The journal focuses on Pattern recognition but the discussions also offer insight into other areas such as Sensitivity (control systems), Feature (computer vision) and Electroencephalography.

The concepts on Artificial neural network presented in the journal can also apply to other research fields, including Correlation, Audiology, Hippocampus segmentation, Curve evolution and Dementia. Biomedizinische Technik holds forums on Signal that merges themes from other disciplines such as Continuous positive airway pressure, Obstructive sleep apnea, ModelSim and Gate array. It explores topics in Support vector machine which can be helpful for research in disciplines like Time domain, Very low frequency, Heart rate variability, Internal medicine and Sample entropy.

The most cited articles from the last journal are:

• EEG-based emotion recognition with deep convolutional neural networks. (7 citations)
• Workflow and hardware for intraoperative hyperspectral data acquisition in neurosurgery (4 citations)
• Development and control of a home-based training device for hand rehabilitation with a spring and cable driven mechanism (2 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Biomedizinische Technik (based on the number of publications) are:

• Max Schaldach (147 papers) absent at the last edition,
• Olaf Dössel (122 papers) absent at the last edition,
• K.-P. Schmitz (94 papers) absent at the last edition,
• Ulrich Boenick (93 papers) absent at the last edition,
• Jens Haueisen (87 papers) absent at the last edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Biomedizinische Technik (based on the number of publications) are:

• Karlsruhe Institute of Technology (139 papers) absent at the last edition,
• University of Erlangen-Nuremberg (125 papers) absent at the last edition,
• RWTH Aachen University (105 papers) published 4 papers at the last edition,
• Dresden University of Technology (86 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Ruhr University Bochum (72 papers) absent at the last edition.

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.

Publication chance based on affiliation

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, 17.24% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.42% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.08% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 6.25% of all publications and 81.25% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.