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: 0013-5585

Editor-in-chief: Olaf Dössel

Edited by: Peter Augat, Thomas M. Deserno, Jens Haueisen, Stefan Jockenhoevel, Steffen Leonhardt, Gernot Plank, Klaus M. Radermacher, Erik Schkommodau, Thomas Stieglitz

Impact Factor: 1.411

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

Back Issues

Details on availability and prices of recent back volumes and issues will be provided on request from service@degruyter.com. All volumes of the majority of our journals* with the noted exception of the current volume are being offered by

Schmidt Periodicals GmbH, Bad Feilnbach
Tel.: (+49) 80 64 – 221
Fax: (+49) 80 64 – 557
Email: schmidt@periodicals.com
http://www.periodicals.com

(* For periodicals not included in this agreement, please refer to the distribution address provided on the homepage of the periodical.)

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Supplementary Materials

Topics

External Links

Volume 66 Issue 6

December 2021

Accessible December 3, 2021

Frontmatter

Page range: i-iii

Download PDF

Review

November 15, 2021

Reliability and validity varies among smartphone apps for range of motion measurements of the lower extremity: a systematic review

Sarah Hahn, Inga Kröger, Steffen Willwacher, Peter Augat

Page range: 537-555

Abstract

The aim of this review was to determine whether smartphone applications are reliable and valid to measure range of motion (RoM) in lower extremity joints. A literature search was performed up to October 2020 in the databases PubMed and Cochrane Library. Studies that reported reliability or validity of smartphone applications for RoM measurements were included. The study quality was assessed with the QUADAS-2 tool and baseline information, validity and reliability were extracted. Twenty-five studies were included in the review. Eighteen studies examined knee RoM, whereof two apps were analysed as having good to excellent reliability and validity for knee flexion (“DrGoniometer”, “Angle”) and one app showed good results for knee extension (“DrGoniometer”). Eight studies analysed ankle RoM. One of these apps showed good intra-rater reliability and excellent validity for dorsiflexion RoM (“iHandy level”), another app showed excellent reliability and moderate validity for plantarflexion RoM (“Coach’s Eye”). All other apps concerning lower extremity RoM had either insufficient results, lacked study quality or were no longer available. Some apps are reliable and valid to measure RoM in the knee and ankle joint. No app can be recommended for hip RoM measurement without restrictions.

Research Articles

June 7, 2021

Extended measuring depth dual-wavelength Fourier domain optical coherence tomography

Haroun Al-Mohamedi, Ismael Kelly-Pérez, Theo Oltrup, Alan Cayless, Thomas Bende

Page range: 557-562

Abstract

In this work an enhanced wide range dual band spectral domain optical coherence tomography technique (SD-OCT) is presented to increase the depth and accuracy of the measurement of optical A-scan biometry. The setup uses a Michelson interferometer with two wide-spectrum Superluminescent Diodes (SLD). The emissions of the SLDs are filtered by a long-pass filter (900 nm) in front of the reference mirror. The light is spectrally decomposed using a single reflective diffraction grating (1,800 lines/mm) and the whole spectrum captured with two CCD line sensors. The capabilities of the system have been validated using a self-made human model eye.

August 12, 2021

Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine

Haitham S. Mohammed, Hagar M. Hassan, Michael H. Zakhari, Hassan Mostafa, Ebtesam A. Mohamad

Page range: 563-572

Abstract

Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies the disease. The accurate detection of seizures is a crucial step in any procedure of treatment. In the present study, electrocorticogram (ECoG) signals were recorded from awake and freely moving animals implanted with cortical electrodes before and after pentylenetetrazol, the chemo-convulsant injection. ECoG signals were segmented into 4-s epochs and labeled. Twenty-four linear and non-linear features were extracted from the time and frequency domains of the ECoG signals. The extracted features either individually or in combinations were fed to an automatic support vector machine (SVM) classification system. SVM classifier was trained with 5 min of ictal and non-ictal labeled ECoG signals to build the hyperplane that separates two sets of training signals. Sensitivity, specificity, and accuracy were determined for the testing dataset using the different feature combinations. It has been found that some linear features either individually or in combinations outperform non-linear features in terms of the accuracy for seizure detection. The maximum accuracy achieved by the system was 95.3% and has been obtained only after linear and non-linear features were combined. ECoG signals were classified without pre-processing or removal of artifacts to reduce the required computational time to be suitable for online implementation purposes. This may prove the detection system’s robustness and supports its use in online seizure detection protocols.

October 11, 2021

Modulation of neo-endothelialization of vascular graft materials by silk fibroin

Congcong Zhan, Chuanjun Xia, Pengfei Wang, Pingdeng Ming, Shanfeng Zhang, Junying Chen, Xia Huang

Page range: 573-580

Abstract

Controlled neo-endothelialization is critical to the patency of vascular grafts. Expanded polyethylene terephthalate (PET) vascular grafts were grafted with polyethylene glycol (PEG), irradiated with ultraviolet light, and subsequently coated with silk fibroin (SF) and EDC in a dip-coating process. Endothelial cells were cultivated on the coated samples for 1, 3, 5, and 7 days, and characterized by fluorescence microscopy and scanning electron microscopy (SEM). The quantitative analyse of CCK-8 method was used to assess ECs proliferation. The results reveal the correlation between grafting components and cell adhesion. We demonstrated that PET with SF grafting facilitated cell adhesion and spreading. Following 7 days of cell culture in vitro , PET-PEG6000-SF (PEG molecular weight 6,000) displayed spreading of cells over a significantly larger area. Rapid endothelialization on a modified PET surface resulted in large tissue pack that can be observed by SEM.

October 11, 2021

Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models

Ahana Priyanka, Kavitha Ganesan

Page range: 581-592

Abstract

The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.

October 26, 2021

Modular 3D printable orthodontic measuring apparatus for force and torque measurements of thermoplastic/removable appliances

Masoud Behyar, Anja Ratzmann, Sohrab Shojaei Khatouni, Maximilian Quasthoff, Christiane Pink, Jens Ladisch, Karl-Friedrich Krey

Page range: 593-601

Abstract

The magnitude of forces and moments applied on teeth during orthodontic treatment is crucial to achieve the desired tooth movement. The aim of this study is to introduce a modular 3D printable orthodontic measurement apparatus (M3DOMA), which can be used for measurements of forces and moments acting on teeth during treatment with aligners. The measurement device was characterized regarding signal to noise ratio (SNR) of the sensors, repeatability of measurements, influence of thermoforming, as well as reliability. Forces and moments were evaluated for an activation range of 0.1–0.4 mm, comparing them among different activation patterns with two aligner thicknesses. The sensors exhibited a SNR from 13–33 dB. Repeatability with repeated measurements showed standard deviations ≤0.015 N and 0.769 Nmm. The influence of thermoforming represented by standard deviation of forces ranges from 0.019–0.147 N. The device showed a range of intra class correlation (ICC) for repeated measurements for all sensors from 0.932 to 0.999. Hence the reliability of the device has been proven to be excellent.

November 2, 2021

Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming

Rene Peter Bremm, Christophe Berthold, Rejko Krüger, Klaus Peter Koch, Jorge Gonçalves, Frank Hertel

Page range: 603-611

Abstract

Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.

November 29, 2021

Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method

Lei Geng, Hongfeng Shan, Zhitao Xiao, Wei Wang, Mei Wei

Page range: 613-625

Abstract

Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT). The Mel filter bank acts on the spectrogram to enhance the signal’s harmonics and denoise. Second, a pre-trained convolutional neural network (CNN) is used as the backbone network to extract sound state features and vocal cord vibration features from the two signals. To obtain a better classification effect, the fused features are input into the long short-term memory (LSTM) network for voice feature selection and enhancement. The proposed system achieves 95.73% for accuracy with 96.10% F1-score and 96.73% recall using the Saarbrucken Voice Database (SVD); thus, enabling a new method for pathological speech detection.

Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 66 (2021)

Volume 65 (2020)

Volume 64 (2019)

Volume 63 (2018)

Volume 62 (2017)

Volume 61 (2016)

Volume 60 (2015)

Volume 59 (2014)

Volume 58 (2013)

Volume 57 (2012)

Volume 56 (2011)

Volume 55 (2010)

Volume 54 (2009)

Volume 53 (2008)

Volume 52 (2007)

Volume 51 (2006)

Volume 50 (2005)

Volume 49 (2004)

Volume 48 (2003)

Volume 47 (2002)

Volume 46 (2001)

Volume 45 (2000)

Volume 44 (1999)

Volume 43 (1998)

Volume 42 (1997)

Volume 41 (1996)

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Volume 39 (1994)

Volume 38 (1993)

Volume 37 (1992)

Volume 36 (1991)

Volume 35 (1990)

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Issue 1-6

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Issue 1-6

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Volume 6 (1961)

Volume 5 (1960)

Volume 4 (1959)

Volume 3 (1958)

Volume 2 (1957)

Volume 1 (1956)

5-year Impact Factor	1.404
Cite Score	2.2
Impact Factor	1.411
Journal Citation Indicator	0.35
SCImago Journal Rank	0.299
Source Normalized Impact per Paper	0.696

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Editorial

Editor-in-Chief
Olaf Dössel (Karlsruhe, Germany)

Associate Editors
Peter Augat (Murnau, Germany)
Thomas Deserno (Braunschweig, Germany)
Jens Haueisen (Ilmenau, Germany)
Anja Hennemuth (Berlin, Germany)
Stefan Jockenhövel (Aachen, Germany)
Steffen Leonhardt (Aachen, Germany)
Gernot Plank (Graz, Austria)
Klaus Radermacher (Aachen, Germany)
Erik Schkommodau (Muttenz, Switzerland)
Thomas Stieglitz (Freiburg, Germany)

Editorial Board
Ulrich Boenick (Berlin, 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 (Leipzig, Germany)
Klaus-Peter Schmitz (Rostock, Germany)
Tobias Schaeffter (London, UK)
Jess G. Snedeker (Zurich, Switzerland)
Leif Sörnmo (Lund, Sweden)
Nobuhiko Sugano (Osaka, Japan)
Jürgen Werner (Bochum, Germany)
Werner Wolf (Neubiberg, Germany)

BMT Editorial Office
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Genthiner Str. 13
10785 Berlin
Germany
Tel. +49-30-26005-176
Fax: +49-30-26005-352
E-mail: bmt.editorial@degruyter.com

(Deutsch)

Online ISSN: 0013-5585
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