BioMedical Engineering OnLine (BIOMED ENG ONLINE)

Publisher: BioMed Central

Journal description

BioMedical Engineering OnLine is an Open Access, peer-reviewed, online journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world with an interest in using tools of the physical sciences to advance and understand problems in the biological and medical sciences. There are biomedical engineers in countries throughout the world, and the results of their work are scattered and often difficult to access. This publication promotes the rapid and free accessibility of articles for biomedical engineering researchers everywhere. The result is a worldwide community of biomedical engineers who are linked together by their various research interests and their values in promoting benefits to all of humanity.

Current impact factor: 1.43

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 1.427
2013 Impact Factor 1.746
2012 Impact Factor 1.608
2011 Impact Factor 1.405
2010 Impact Factor 1.119
2009 Impact Factor 1.639
2008 Impact Factor 1.8

Impact factor over time

Impact factor
Year

Additional details

5-year impact 1.99
Cited half-life 4.40
Immediacy index 0.21
Eigenfactor 0.00
Article influence 0.53
Website BioMedical Engineering Online website
ISSN 1475-925X
OCLC 50638424
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

BioMed Central

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Publisher's version/PDF may be used
    • Eligible UK authors may deposit in OpenDepot
    • Creative Commons Attribution License
    • Copy of License must accompany any deposit.
    • All titles are open access journals
    • 'BioMed Central' is an imprint of 'Springer Verlag (Germany)'
  • Classification
    green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: The biomechanical simulation of the human respiratory system is expected to be a useful tool for the diagnosis and treatment of respiratory diseases. Because the deformation of the thorax significantly influences airflow in the lungs, we focused on simulating the thorax deformation by introducing contraction of the intercostal muscles and diaphragm, which are the main muscles responsible for the thorax deformation during breathing. We constructed a finite element model of the thorax, including the rib cage, intercostal muscles, and diaphragm. To reproduce the muscle contractions, we introduced the Hill-type transversely isotropic hyperelastic continuum skeletal muscle model, which allows the intercostal muscles and diaphragm to contract along the direction of the fibres with clinically measurable muscle activation and active force–length relationship. The anatomical fibre orientations of the intercostal muscles and diaphragm were introduced. Thorax deformation consists of movements of the ribs and diaphragm. By activating muscles, we were able to reproduce the pump-handle and bucket-handle motions for the ribs and the clinically observed motion for the diaphragm. In order to confirm the effectiveness of this approach, we simulated the thorax deformation during normal quiet breathing and compared the results with four-dimensional computed tomography (4D-CT) images for verification. Thorax deformation can be simulated by modelling the respiratory muscles according to continuum mechanics and by introducing muscle contractions. The reproduction of representative motions of the ribs and diaphragm and the comparison of the thorax deformations during normal quiet breathing with 4D-CT images demonstrated the effectiveness of the proposed approach. This work may provide a platform for establishing a computational mechanics model of the human respiratory system.
    No preview · Article · Dec 2016 · BioMedical Engineering OnLine
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    ABSTRACT: Brain–computer interface (BCI) is an assistive technology that conveys users’ intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interactions. However, errors existing in BCI systems affect their performance greatly, which in turn confines the development and application of BCI technology. It has been demonstrated viable to extract error potential from electroencephalography recordings. This study proposed a new approach of fusing multiple-channel features from temporal, spectral, and spatial domains through two times of dimensionality reduction based on neural network. 26 participants (13 males, mean age = 28.8 ± 5.4, range 20–37) took part in the study, who engaged in a P300 speller task spelling cued words from a 36-character matrix. In order to evaluate the generalization ability across subjects, the data from 16 participants were used for training and the rest for testing. The total classification accuracy with combination of features is 76.7 %. The receiver operating characteristic (ROC) curve and area under ROC curve (AUC) further indicate the superior performance of the combination of features over any single features in error detection. The average AUC reaches 0.7818 with combined features, while 0.7270, 0.6376, 0.7330 with single temporal, spectral, and spatial features respectively. The proposed method combining multiple-channel features from temporal, spectral, and spatial domain has better classification performance than any individual feature alone. It has good generalization ability across subject and provides a way of improving error detection, which could serve as promising feedbacks to promote the performance of BCI systems.
    No preview · Article · Dec 2016 · BioMedical Engineering OnLine
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    ABSTRACT: Cervical cancer is the second leading cause of female-specific cancer-related deaths after breast cancer, especially in developing countries. However, the incidence of the disease may be significantly decreased if the patient is diagnosed in the pre-cancerous lesion stage or earlier. In recent years, computer-based algorithms are widely used in cervical cancer screening. Most of the proposed algorithms follow the procedure of segmentation, feature extraction, and then classification. Nevertheless, few of the existing segmentation methods are as flexible and robust as the human visual system, and the complexity of the algorithms makes it difficult for clinical application. In this study, a computer-assisted analytical approach is proposed to identify the existence of suspicious cells in a whole slide cervical cell image (WSCCI). The main difference between our method and the conventional algorithm is that the image is divided into blocks with certain size instead of segmented cells, which can greatly reduce the computational complexity. Via data analysis, some texture and color histogram features show significant differences between blocks with and without suspicious cells. Therefore these features can be used as the input of the support vector machine classifier. 1100 non-background blocks (110 suspicious blocks) are trained to build a model, while 1040 blocks (491 non-background blocks) from 12 other WSCCIs are tested to verify the feasibility of the algorithm. The experimental results show that the accuracy of our method is about 98.98 %. More importantly, the sensitivity, which is more fatal in cancer screening, is 95.0 % according to the images tested in the study, while the specificity is 99.33 %. The analysis of the algorithm is based on block images, which is different from conventional methods. Although some analysis work should be done in advance, the later processing speed will be greatly enhanced with the establishment of the model. Furthermore, since the algorithm is based on the actual WSCCI, the method will be of directive significance for clinical screening.
    No preview · Article · Dec 2016 · BioMedical Engineering OnLine
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    ABSTRACT: Double injection of blood into cisterna magna using a rabbit model results in cerebral vasospasm. An unacceptably high mortality rate tends to limit the application of model. Ultrasound guided puncture can provide real-time imaging guidance for operation. The aim of this paper is to establish a safe and effective rabbit model of cerebral vasospasm after subarachnoid hemorrhage with the assistance of ultrasound medical imaging. A total of 160 New Zealand white rabbits were randomly divided into four groups of 40 each: (1) manual control group, (2) manual model group, (3) ultrasound guided control group, and (4) ultrasound guided model group. The subarachnoid hemorrhage was intentionally caused by double injection of blood into their cisterna magna. Then, basilar artery diameters were measured using magnetic resonance angiography before modeling and 5 days after modeling. The depth of needle entering into cisterna magna was determined during the process of ultrasound guided puncture. The mortality rates in manual control group and model group were 15 and 23 %, respectively. No rabbits were sacrificed in those two ultrasound guided groups. We found that the mortality rate in ultrasound guided groups decreased significantly compared to manual groups. Compared with diameters before modeling, the basilar artery diameters after modeling were significantly lower in manual and ultrasound guided model groups. The vasospasm aggravated and the proportion of severe vasospasms was greater in ultrasound guided model group than that of manual group. In manual model group, no vasospasm was found in 8 % of rabbits. The ultrasound guided double injection of blood into cisterna magna is a safe and effective rabbit model for treatment of cerebral vasospasm.
    No preview · Article · Dec 2016 · BioMedical Engineering OnLine

  • No preview · Article · Dec 2016 · BioMedical Engineering OnLine
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    ABSTRACT: In the process of bone defective reparation and engineered bone tissue construction, osteoblasts are adhered to the surface of the scaffold materials and impart the external mechanical load to the osteoblasts. So, the dynamic mechanical property of the scaffolds play an important role in the bone tissue repair and it is valuable to research. Material type and the architectural design of scaffolds are also important to facilitate cell and tissue growth. The aim of this study was to prepare a kind of material with good pore connectivity and analyze its dynamic mechanical property. Fabrication and characterization of micro-hydroxyapatite(m-HA)/chitosan(CS) polymer composite scaffolds with well interconnected spherical pore architectures were reports. Micro-HA was prepared by being calcined and ball milled. Paraffin spheres in the range of 160–330 µm were fabricated with a dispersion method and used as the porogen in the fabrication of the scaffolds. Polymer scaffolds were fabricated by the technique of compression molding and particulate leaching method. The effects of the porogen content on the properties of the scaffolds were studied. With the increase of porogen, the pore of the scaffolds increased and became interconnected. Cyclic loading of three scaffolds were tested with 10 % strain under four levels of loading frequency, 0.1, 0.5, 1 and 1.5 Hz. The porous composite scaffolds exhibited a viscosity-elastic behaviour with a maximum stress of 3–4 kPa. At each frequency, modulus value is decreased with the paraffin microspheres content, but there was no significance difference in the peak stress of the three samples. All the samples tested displayed clear hysteresis loops. There was no significance difference in the peak hysteresis of the three samples, and the hysteresis difference values between the sixth compression cycle and the initial cycle for three samples was similar, with no statistically significant differences. Micro-HA/CS composite scaffolds with interconnected spherical macropores were fabricated using pherical paraffin as porogen. The porous composite scaffolds exhibited a viscosity-elastic behaviour with good repeatability. It is benefit to study the influence of the mechanical load on the cell of the scaffold.
    Preview · Article · Feb 2016 · BioMedical Engineering OnLine
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    ABSTRACT: Magneto-acoustic tomography with current injection involves using electrical impedance imaging technology. To explore the potential applications in imaging biological tissue and enhance image quality, a new scan mode for the transducer is proposed that is based on translational and circular scanning to record acoustic signals from sources. An imaging algorithm to analyze these signals is developed in respect to this alternative scanning scheme. Numerical simulations and physical experiments were conducted to evaluate the effectiveness of this scheme. An experiment using a graphite sheet as a tissue-mimicking phantom medium was conducted to verify simulation results. A pulsed voltage signal was applied across the sample, and acoustic signals were recorded as the transducer performed stepped translational or circular scans. The imaging algorithm was used to obtain an acoustic-source image based on the signals. In simulations, the acoustic-source image is correlated with the conductivity at the sample boundaries of the sample, but image results change depending on distance and angular aspect of the transducer. In general, as angle and distance decreases, the image quality improves. Moreover, experimental data confirmed the correlation. The acoustic-source images resulting from the alternative scanning mode has yielded the outline of a phantom medium. This scan mode enables improvements to be made in the sensitivity of the detecting unit and a change to a transducer array that would improve the efficiency and accuracy of acoustic-source images.
    Full-text · Article · Jan 2016 · BioMedical Engineering OnLine
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    ABSTRACT: The heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals. This article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect characteristics BP signals, and estimate the locations of the heartbeat. When both ECG and BP are not qualified, our fusion method resorts to the association model between ECG and EEG that allows us to apply an adaptive filter to ECG and EEG, extract the QRS candidates, and report heartbeats. The proposed method achieved an overall score of 86.26 % for the test data when the input signals are unreliable. Our method outperformed the traditional method, which achieved 79.28 % using QRS detector and BP detector from PhysioNet. Our multimodal signal processing method outperforms the conventional unimodal method of taking ECG signals alone for both training and test data sets. To detect the heartbeat robustly, we have proposed a novel multimodal data association method of supplementing ECG with a variety of physiological signals and accounting for the patient-specific lag between different pulsatile signals and ECG. Multimodal signal detectors and data-fusion approaches such as those proposed in this article can reduce false alarms and improve patient monitoring.
    Preview · Article · Jan 2016 · BioMedical Engineering OnLine
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    ABSTRACT: Background: Almost all promising non-invasive foetal ECG extraction methods involve accurately determining maternal ECG R-wave peaks. However, it is not easy to robustly detect accurate R-wave peaks of the maternal ECG component in an acquired abdominal ECG since it often has a low signal-to-noise ratio (SNR), sometimes containing a large foetal ECG component or other noises and interferences. This paper discusses, under the condition of acquiring multi-channel abdominal ECG signals, how to improve the robustness of maternal ECG R-wave peak detection. Methods: On the basis of summarising the current single channel ECG R-wave peak detection methods, the paper proposed a specific fusion algorithm of detected multi-channel maternal ECG R-wave peak locations. The proposed entire algorithm was then tested using two databases; one database, created by us, was composed of 343 groups of 8-channel data collected from 78 pregnant women, and the other one, called the challenge database, was from the Physionet/Computing in Cardiology Challenge 2013, including 175 groups of 4-channel data. When using these databases, each group of data was classified into two parts, called the training part and the validation test part respectively; the training part was the first 8.192 s of each group of data and the validation test part was the next 8.192 s. Results: To show the results, three evaluation parameters-sensitivity (Se), positive predictive value (PPV) and F1-are used. The validation test results for the database we collected are Se = 99.93 %, PPV = 99.98 %, and F1 = 99.95 %, while the results for the challenge database are Se = 99.91 %, PPV = 99.86 %, and F1 = 99.88 %. Conclusion: The results of the test show that the robustness of our proposed whole fusion algorithm was superior to that of other outstanding algorithms for maternal R-wave detection, and is much better than that of single channel maternal R-wave detection algorithms.
    Preview · Article · Jan 2016 · BioMedical Engineering OnLine
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    ABSTRACT: Background: CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. Methods: The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. Results: The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. Conclusions: From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.
    Preview · Article · Jan 2016 · BioMedical Engineering OnLine
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    ABSTRACT: Background: Extremely low frequency pulsed magnetic fields (ELFPMF) have been shown to induce Faraday currents and measurable effects on biological systems. A kind of very high frequency electromagnetic field was reported that it improved the symptoms of diabetic nephropathy (DN) which is a major complication of diabetes. However, few studies have examined the effects of ELFPMF DN at the present. The present study was designed to investigate the effects of ELFPMF on DN in streptozotocin (STZ)-induced type 1 diabetic rats. Methods: Adult male SD rats were randomly divided into three weight-matched groups: Control (non-diabetic rats without DN), DN + ELFPMF (diabetic rats with DN exposed to ELFPMF, 8 h/days, 6 weeks) and DN (diabetic rats with DN exposed to sham ELFPMF). Renal morphology was examined by light and electron microscopy, vascular endothelial growth factor (VEGF)-A and connective tissue growth factor (CTGF) were measured by enzyme linked immune sorbent assay. Results: After 6 weeks' ELFPMF exposure, alterations of hyperglycemia and weight loss in STZ-treated rats with DN were not found, while both positive and negative effects of ELFPMF on the development of DN in diabetic rats were observed. The positive one was that ELFPMF exposure attenuated the pathological alterations in renal structure observed in STZ-treated rats with DN, which were demonstrated by slighter glomerular and tubule-interstitial lesions examined by light microscopy and slighter damage to glomerular basement membrane and podocyte foot processes examined by electron microscopy. And then, the negative one was that ELFPMF stimulation statistically significantly decreased renal expression of VEGF-A and statistically significantly increased renal expression of CTGF in diabetic rats with DN, which might partially aggravate the symptoms of DN. Conclusion: Both positive and negative effects of ELFPMF on the development of DN in diabetic rats were observed. The positive effect induced by ELFPMF might play a dominant role in the procession of DN in diabetic rats, and it is suggested that the positive effect should be derived from the correction of pathogenic diabetes-induced mediators.
    Preview · Article · Jan 2016 · BioMedical Engineering OnLine
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    ABSTRACT: The low quality of diffusion tensor image (DTI) could affect the accuracy of oncology diagnosis. We present a novel sparse representation based denoising method for three dimensional DTI by learning adaptive dictionary with the context redundancy between neighbor slices. In this study, the context redundancy among the adjacent slices of the diffusion weighted imaging volumes is utilized to train sparsifying dictionaries. Therefore, higher redundancy could be achieved for better description of image with lower computation complexity. The optimization problem is solved efficiently using an iterative block-coordinate relaxation method. The effectiveness of our proposed method has been assessed on both simulated and real experimental DTI datasets. Qualitative and quantitative evaluations demonstrate the performance of the proposed method on the simulated data. The experiments on real datasets with different b-values also show the effectiveness of the proposed method for noise reduction of DTI. The proposed approach well removes the noise in the DTI, which has high potential to be applied for clinical oncology applications.
    Preview · Article · Jan 2016 · BioMedical Engineering OnLine
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    Preview · Article · Jan 2016 · BioMedical Engineering OnLine