Published since January 1, 1956

Biomedical Engineering / Biomedizinische Technik

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering and the German Society of Biomaterials

ISSN: 1862-278X

Editor-in-chief: Jens Haueisen

Edited by: Peter Augat, Thomas M. Deserno, Frédéric Heim, Steffen Leonhardt, Gernot Plank, Klaus M. Radermacher, Erik Schkommodau, Thomas Stieglitz, Gudrun Wagenknecht

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About this journal

Objective
Biomedical Engineering / Biomedizinische Technik (BMT) is a high-quality forum for the exchange of knowledge in the fields of biomedical engineering, medical information technology and biotechnology/bioengineering. As an established journal with a tradition of more than 60 years, BMT addresses engineers, natural scientists, and clinicians working in research, industry, or clinical practice. All articles are peer-reviewed. All manuscripts must be submitted in English.

Topics

diagnostic imaging
image processing
biosignal processing
modelling and simulation
biomechanics
information and communication in medicine
telemedicine and e-health
surgery
minimum invasive interventions
endoscopy and image-guided therapy
diagnostic and therapeutic instrumentation
clinical engineering
micro- and nanosystems in medicine
active implants
biosensors
neuro-engineering
neural systems
rehabilitation and prosthetics
biomaterials
cellular and tissue engineering
artificial organs
biomedical engineering for audiology, ophthalmology, emergency and dental medicine
general subjects (e.g., education and training, health technology assessment)
special topics (e.g., linking proteomics and genomics with biomedical engineering, emergency medicine)

Article formats
Reviews, Research Articles, Short Communications

Your Benefits

Your benefits:

Imperative topic
Applicative approach to engineering facilities and methods
New areas of application of cutting-edge technologies in modern medicine
State-of-the-art solutions for requirements in current medical practice
Ahead-of-print online publication

History

The Journal was founded in 1956 as the official journal of the German Society of Biomedical Engineering Biomedizinische Technik and published firstly in German. In a transition period as of 2004 publication in German and English was possible, but since 2010 the journal appears only in English. Consistently the title was changed to Biomedical Engineering – Biomedizinische Technik.

Other publications in the field

Journals:

Books, books series and multi-volume works:

„Biomedizinische Technik” (12 Volumes). (Overview)
S. Dickel, M. Franzen, Ch. Kehl, Herausforderung Biomedizin. Gesellschaftliche Deutung und soziale Praxis, “VerKörperungen/MatteRealities - Perspektiven empirischer Wissenschaftsforschung 14”, 2014.(Overview)
D. Hochmann, Prüf- und Bewertungsmethoden für Knieorthesen, “Forschung für die Rehabilitationstechnik 1”, 2012.(Overview)
K. König, A. Ostendorf (Eds.), Optically Induced Nanostructures. Biomedical and Technical Applications, 2015. (Overview)
W. Niederlag, H. U. Lemke, H. Lehrach, H.-O. Peitgen, Der virtuelle Patient, “Health Academy 1”, 2014.(Overview)
W. Niederlag, H. U. Lemke et al., Der digitale Operationssaal, “Health Academy 2”, 2014.(Overview)
I. Roterman-Konieczna (Ed.), Simulations in Medicine. Pre-Clinical and Clinical Applications, 2015.(Overview)
J. Werner, Kooperative und autonome Systeme der Medizintechnik. Funktionswiederherstellung und Organersatz, 2014.(Overview)

Previous Titles

2012: from Biomedizinische Technik – Biomedical Engineering to Biomedical Engineering – Biomedizinische Technik

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Supplementary Materials

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External Links

Volume 68 Issue 2

April 2023

Publicly Available March 29, 2023

Frontmatter

Page range: i-iii

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Research Articles

Publicly Available October 14, 2022

Safe design of surgical robots – a systematic approach to comprehensive hazard identification

Lukas Theisgen, Florian Strauch, Matías de la Fuente Klein, Klaus Radermacher

Page range: 117-123

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Abstract

Objectives Since the 1980s, robotic arms have been transferred from industrial applications to orthopaedic surgical robotics. Adverse events are frequent and often associated with the adopted powerful and oversized anthropomorphic arms. The FDA’s 510(k) pathway encourages building on such systems, leading to the adoption of hazards, which is known as “predicate creep”. Additionally, the methodology of hazard identification for medical device development needs improvement. Methods We present an approach to enhance general hazard identification and prevent hazards of predicate creep by using the integrative, scenario-based and multi-perspective Point-of-View (PoV) approach. We also present the Catalogue of Hazards (CoH) as an approach for collecting and systematising hazards for future risk analysis and robot development. Results We applied seven predefined PoVs to the use case of robotic laminectomy and identified 133 hazards, mainly coming from HMI analysis and literature. By analysing the MAUDE and recalls databases of the FDA, we were able to classify historical hazards and adopt them into the use case. Conclusions The combination of PoV approach and CoH is suitable for integrating multiple established hazard identification methods, increasing comprehensiveness, and supporting the systematic and hazard-based development of surgical robots.

December 5, 2022

Concurrent validity and reliability of a smartphone-based application for the head repositioning and cervical range of motion

Natalia Romero-Franco, Ángel Oliva-Pascual-Vaca, Juan Carlos Fernández-Domínguez

Page range: 125-132

Abstract

Objectives To evaluate the validity and reliability of a smartphone-based application against inertial sensors to measure head repositioning (by using joint position sense –JPS) and cervical range of motion (ROM). Methods JPS and cervical ROM were evaluated for neck flexion, extension and both-sides lateral flexion in thirty-one volunteers. Participants were simultaneously evaluated with inertial sensors and the smartphone application. A total of 248 angles were compared for concurrent validity. Inter-tester and intra-tester reliability were evaluated through scoring of images with the smartphone application by two testers, and re-scoring images by the same tester. Results Very high correlation was observed between both methods for ROM in all neck movements and JPS in left-side lateral flexion (r>0.9), and high for JPS in the rest of movements (r>0.8). Bland-Altman plots always demonstrated absolute agreement. Inter-and intra-tester reliability was perfect for JPS and ROM in all the neck movements (ICC>0.81). Conclusions This smartphone-based application is valid and reliable for evaluating head repositioning and cervical ROM compared with inertial sensors in healthy and young adults. Health professionals could use it in an easier and portable way in field conditions.

October 6, 2022

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer

Ali Ekhlasi, Ali Motie Nasrabadi, Mohammadreza Mohammadi

Page range: 133-146

Abstract

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared to healthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta and delta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

December 30, 2022

Variational mode decomposition and binary grey wolf optimization-based automated epilepsy seizure classification framework

Vipin Prakash Yadav, Kamlesh Kumar Sharma

Page range: 147-163

Abstract

This work proposes a variational mode decomposition (VMD) and binary grey wolf optimization (BGWO) based seizure classification framework. VMD decomposes the EEG signal into band-limited intrinsic mode function (BL-IMFs) non-recursively. The frequency domain, time domain, and information theory-based features are extracted from the BL-IMFs. Further, an optimal feature subset is selected using BGWO. Finally, the selected features were utilized for classification using six different supervised machine learning algorithms. The proposed framework has been validated experimentally by 58 test cases from the CHB-MIT scalp EEG and the Bonn University database. The proposed framework performance is quantified by average sensitivity, specificity, and accuracy. The selected features, along with Bayesian regularized shallow neural networks (BR-SNNs), resulted in maximum accuracy of 99.53 and 99.64 for 1 and 2 s epochs, respectively, for database 1. The proposed framework has achieved 99.79 and 99.84 accuracy for 1 and 2 s epochs, respectively, for database 2.

October 5, 2022

An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification

Silambarasi Palanivel, Viswanathan Nallasamy

Page range: 165-174

Abstract

Objectives The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications. Methods The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells. Results After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%. Conclusions Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.

October 6, 2022

A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images

Alagu S., Kavitha Ganesan, Bhoopathy Bagan K.

Page range: 175-185

Abstract

Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of SCA is preferred to resolve this issue. A novel and effective deep learning approach for identification of sickle cell anemia is proposed in this work. Around nine hundred microscopic images of human red blood cells are obtained from the public database ‘erythrocytes IDB’. All the images are resized uniformly. About 2048 deep features are extracted from the fully connected layer of pre-trained model InceptionV3. These features are further subjected to classification using optimization-based methods. An improved wrapper-based feature selection technique is implemented using Multi- Objective Binary Grey Wolf Optimization (MO-BGWO) approach with KNN and SVM for classification. The detection of sickle cell is also performed using typical InceptionV3 model by using SoftMax layer. It is observed that the performance of the proposed system seems to be high when compared to the classification using the original InceptionV3 model. The results are validated by various evaluation metrics such as accuracy, precision, sensitivity, specificity and F1-score. The SVM classifier yields high accuracy of about 96%. The optimal subset of deep features along with SVM enhances the system performance in the proposed work. Thus, the proposed approach is appropriate for pathologists to take early clinical decisions on detection of sickle cells.

November 4, 2022

A deep learning network for Gleason grading of prostate biopsies using EfficientNet

Karthik Ramamurthy, Abinash Reddy Varikuti, Bhavya Gupta, Nehal Aswani

Page range: 187-198

Abstract

Objectives The most crucial part in the diagnosis of cancer is severity grading. Gleason’s score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work. Methods In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting. Result To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset. Conclusions The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.

December 30, 2022

Open structure magnetic particle imaging by nonlinear back projection tomography reconstruction

Wanni Zu, Li Ke, Qiang Du

Page range: 199-210

Abstract

Objectives In open structure MPI systems, the nonlinear variation of the field free lines in the large region of interest scanning process distorts the x-space image reconstruction. In this study, we propose a nonlinear field free line projection reconstruction algorithm to solve the edge distortion problem of open structure MPI imaging. Methods First, we calculate the curvature change law of the field free line in the scanning process. Then, we design a nonlinear back projection reconstruction algorithm according to the nonlinear characteristics of the field free line in the scanning process. Finally, the nonlinear back projection reconstruction algorithm is used to complete the tomography of blood vessels. Results The numerical calculation and simulation results show that the open structure MPI combined with a nonlinear back projection reconstruction algorithm can accomplish vascular fault reconstruction. The reconstruction algorithm proposed in this paper suppresses the edge distortion of the image and improves the positioning accuracy of the image. The size of the region of interest where distortions are low is increased 16 times by allowing 10.9% degradation in the gradient. Conclusions We provide a non-linear inverse projection reconstruction algorithm to reduce the structural artefacts caused by FFL distortion. It provides a reconstruction scheme for a large region of interest fine imaging of open structure FFL-MPI.

December 26, 2022

Development and verification of a novel blood viscoelastic monitoring method based on reciprocating motion of magnetic bead

Xinyu Du, Fupan Chen, Lijin Gan, Yong Liu, Yu Zheng, Linghua Xing, Qi Zhou

Page range: 211-223

Abstract

Blood coagulation function is an essential index in clinical examination, and it is of great significance to evaluate blood coagulation function comprehensively. Based on the blood viscoelasticity theory and hydrodynamics, we proposed a method to monitor the whole blood coagulation process based on the reciprocating motion of the magnetic bead (magnetic bead method for short). We have established a mathematical model between the moment acting on the magnetic bead and the viscoelasticity of blood in the process of blood coagulation. The change of blood viscoelasticity acks on the magnetic bead in the form of moment changes, which shows that the amplitude of the motion of the magnetic bead varies with the change of blood viscoelasticity. Designed and verified a blood coagulation monitoring device based on the reciprocating movement of the magnetic bead and discussed the device’s parameters through the orthogonal experiment. Lastly, the TEG5000 was used as the control group to test the thromboelasticity of four groups of thromboelastography quality control products in the same batch and 10 groups of human whole blood. It verified that our device has good repeatability, and has good consistency with TEG5000, it has particular application potential as a new blood coagulation monitoring method.

Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 68 (2023)

Volume 67 (2022)

Volume 66 (2021)

Volume 65 (2020)

Volume 64 (2019)

Volume 63 (2018)

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5-year Journal Impact Factor	1.382
CiteScore	3.2
Journal Impact Factor	1.426
Journal Citation Indicator	0.34
SCImago Journal Rank	0.348
Source Normalized Impact per Paper	0.786

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Editorial

Editor-in-Chief
Jens Haueisen (Ilmenau, Germany)

Associate Editors
Peter Augat (Murnau, Germany)
Thomas Deserno (Braunschweig, Germany)
Frédéric Heim (Mulhouse, France)
Steffen Leonhardt (Aachen, Germany)
Gernot Plank (Graz, Austria)
Klaus Radermacher (Aachen, Germany)
Erik Schkommodau (Muttenz, Switzerland)
Thomas Stieglitz (Freiburg, Germany)
Gudrun Wagenknecht (Jülich, Germany)

Editorial Board
Ulrich Boenick (Berlin, Germany)
Olaf Dössel (Karlsruhe, Germany)
Pamela Habibovic (Maastricht, Netherlands)
Branislav Jaramaz (Pittsburgh, USA)
Marc Kraft (Berlin, Germany)
Thomas Lenarz (Hannover, Germany)
Harry van Lenthe (Leuven, Belgium)
Benny Lo (London, UK)
Luca Mainardi (Milano, Italy)
Silvestro Micera (Lausanne, Switzerland)
Thomas Penzel (Berlin, Germany)
Andrea Robitzki (Karlsruhe, Germany)
Klaus-Peter Schmitz (Rostock, Germany)
Tobias Schaeffter (Berlin, Germany)
Jess G. Snedeker (Zurich, Switzerland)
Leif Sörnmo (Lund, Sweden)
Nobuhiko Sugano (Osaka, Japan)
Jürgen Werner (Bochum, Germany)
Werner Wolf (Neubiberg, Germany)

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Online ISSN: 1862-278X
Print ISSN: 0013-5585
Type: Journal
Language: English
Publisher: De Gruyter
First published: January 1, 1956
Publication Frequency: 6 Issues per Year
Audience: researchers and decision-makers in the field of bioengineering, managers in development laboratories and in the entire sector of medical research, chief consultants in clinics, sanatoriums and hospitals, self-employed businesspeople and traders in the area of biomedical engineering, professionals in health insurance, libraries