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Journal Cover Biocybernetics and Biological Engineering
  [SJR: 0.279]   [H-I: 8]   [4 followers]  Follow
   Full-text available via subscription Subscription journal
   ISSN (Print) 0208-5216
   Published by Elsevier Homepage  [3031 journals]
  • A multi-layered incremental feature selection algorithm for adjuvant
           chemotherapy effectiveness/futileness assessment in non-small cell lung
    • Authors: Roghayeh Esmaeili Naftchali; Mohammad Saniee Abadeh
      Abstract: Publication date: Available online 24 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Roghayeh Esmaeili Naftchali, Mohammad Saniee Abadeh
      Non-small cell lung cancer (NSCLC) is the most common type of lung cancer; and is one of the leading causes of death in the world. Surgery combined with chemotherapy is the recommended treatment for NSCLC. Since chemotherapy is an expensive treatment for either medical staff or patients suffering from pain, this study attempts to construct an intelligent predictive model to predict the adjuvant chemotherapy (ACT) effectiveness/futileness in the patients, in order to help futile cases for unnecessary applications. There is a 2-step method: preprocessing and predicting. First a purposefully preprocessing technique: chi-square test, SVM-RFE and correlation matrix, were employed in NSCLC gene expression dataset as a novel multi-layered feature selection method to defeat the curse of dimension and detect the chemotherapy target genes from tens of thousands features, based on which the patients can be classified into two groups, with NB classifier at second step. 10-Fold cross-validation was found with accuracy of 68.93% for 2 genes, TGFA (205015_s_at) and SEMA6C (208100_x_at), which is preferable compared to earlier studies, even though more than 2 input features are employed for the prediction. According to the results found in this study, one can concludes that the multi-layered feature selection approach has increased the classification accuracy in terms of finding the fitted patient for receiving ACT by reducing the number of features and has significant power to be used in medical datasets with small train samples and large number of features.

      PubDate: 2017-05-24T14:48:45Z
      DOI: 10.1016/j.bbe.2017.05.002
  • ECG signals reconstruction in subbands for noise suppression
    • Authors: Marian Kotas; Tomasz Moroń
      Abstract: Publication date: Available online 18 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marian Kotas, Tomasz Moroń
      In this study, we propose a combination of two methods for ECG noise suppression. The first one is the robust principal component analysis, applied to QRS complexes reconstruction. The second is the method of weighted averaging of nonlinearly aligned signal cycles. The novelty of the approach consists in the way these methods are combined. First, a processed ECG signal is decomposed into three spectral subbands, of high, medium and low frequency. Then both methods are applied in such a way that their operation is prevented from the most common unfavorable factors. RPCA reconstructs QRS complexes in a medium and high frequency subbands added. This makes the method more immune to low frequency artifacts that can be caused by electrodes motion. Dynamic time-warping is performed on the medium frequency subband which again prevents the procedure from the unfavorable influence of electrode motion artifacts. After the warping paths have been determined, the weighted addition of nonlinearly aligned signal cycles is executed, separately in the three subbands, with optimal weights estimated in each subband. Finally, by the appropriate addition of the obtained signals, the whole spectrum ECG is reconstructed. In the experimental section, the method was investigated with the use of real and artificially generated signals. In both cases, it allowed for effective suppression of noise, preserving important features of the processed signals. When it was applied to ECG enhancement prior to determination of the QT interval, the measurements appeared to be remarkably immune to different types of noise.

      PubDate: 2017-05-19T14:16:29Z
      DOI: 10.1016/j.bbe.2017.03.002
  • A new computer-based approach for fully automated segmentation of knee
           meniscus from magnetic resonance images
    • Authors: Ahmet Saygili; Songül Albayrak
      Abstract: Publication date: Available online 18 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Ahmet Saygili, Songül Albayrak
      Menisci are tissues that enable mobility and absorb excess loads on the knee. Problems in meniscus can trigger the disorder of osteoarthritis (OA). OA is one of the most common causes of disability, especially among young athlethes and elderly people. Therefore, the early diagnosis and treatment of abnormalities that occur in the meniscus are of significant importance. This study proposes a new computer-based and fully automated approach to support radiologists by: (i) the segmentation of medial menisci, (ii) enabling early diagnosis and treatment, and (iii) reducing the errors caused by MR intra-reader variability. In this study, 88 different MR images provided by the Osteoarthritis Initiative (OAI) are used. The histogram of oriented gradients (HOG) and local binary patterns (LBP) methods are used for feature extraction from these MR images along with the extreme learning machine (ELM) and random forests (RF) methods which are used for model learning (regression). As the first step of the pipeline, the most compact rectangular patches bounding the menisci are located. After this, meniscus boundaries are revealed by the morphological processes. Then, the similarities between these boundaries and the ground truth images are measured and compared with each other. The highest score is acquired with Dice similarity measurement with a success rate of 82%. A successful segmentation is performed on the diseased knee MR images. The proposed approach can be implemented as a decision support system for radiologists, while the segmented menisci can be used in classification of meniscal tear in future studies.

      PubDate: 2017-05-19T14:16:29Z
      DOI: 10.1016/j.bbe.2017.04.008
  • The autonomic nervous system and cancer
    • Authors: Milan T. Makale; Santosh Kesari; Wolfgang Wrasidlo
      Abstract: Publication date: Available online 17 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Milan T. Makale, Santosh Kesari, Wolfgang Wrasidlo
      Recent data have demonstrated extensive autonomic nervous system (ANS) neural participation in malignant tumors and infiltration of nerve fibers in and around malignant tumors. ANS cybernetic imbalances deriving from central nervous system (CNS) stress are associated with poorer patient outcome and may play a key role in tumor expansion. The ANS modulates and can destabilize tissue stem cells, and it drives the expression of neurotransmitter receptors on tumor cells. Disruption of tumor innervation and pharmacological ANS blockade have abrogated cancer growth in preclinical models. The present review interprets recent key findings with respect to the ANS and cancer. We highlight new data from animal models addressing specific cancers suggesting that unbalanced autonomic cybernetic control loops are associated with tissue instability which in turn promotes, (1) cancer stem cell based tumor initiation and growth, and (2) metastasis. We posit that identifying the sources of neural control loop dysregulation in specific tumors may reveal potential targets for antitumor therapy. Given the striking tumor regression results obtained with gastric vagotomy in gastric cancer models, and the effects of β-adrenergic blockade in pancreatic tumor models, it may be feasible to improve cancer outcomes with therapeutics targeted to the nervous system.

      PubDate: 2017-05-19T14:16:29Z
      DOI: 10.1016/j.bbe.2017.05.001
  • Characterization of cardiac arrhythmias by variational mode decomposition
    • Authors: Uday Maji; Madhuchhanda Mitra; Saurabh Pal
      Abstract: Publication date: Available online 4 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Uday Maji, Madhuchhanda Mitra, Saurabh Pal
      Automatic detection of cardiac abnormalities in early stage is a popular area of research for decades. In this work a novel algorithm for detection of cardiac arrhythmia is proposed using variational mode decomposition (VMD). Arrhythmia is a crucial abnormality of heart in which the rhythmic disorder may lead to sudden cardiac arrest. Existing algorithms for arrhythmia detection are based on accuracy of detection of fiducial points, parameter selection and extraction, quality of classifier and other factors. Unlike other works, proposed method tries to characterize both atrial and ventricular arrhythmias simultaneously and independently from the segmented sections of the signal. VMD, being able to separate closely spaced frequencies, has a good potential to be useful to provide significant features in transformed domain. Unique feature combinations are also proposed to characterize different arrhythmic events.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.007
  • In-silico evaluation of left ventricular unloading under varying speed
           continuous flow left ventricular assist device support
    • Authors: Selim Bozkurt; Surhan Bozkurt
      Abstract: Publication date: Available online 4 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Selim Bozkurt, Surhan Bozkurt
      Continuous flow left ventricular assist device (cf-LVAD) operating speed modulation techniques are proposed to achieve different purposes such as improving arterial pulsatility, aortic valve function or ventricular unloading etc. Although it is possible to improve the left ventricular unloading by modulating the operating speed of a cf-LVAD, it is still unclear what type of pump operating mode should be applied to generate a better left ventricular unloading. This study presents a comparison of different heart pump support modes including constant speed support, copulsative and counterpulsative direct cf-LVAD speed modulation and pump flow rate control to regulate the cf-LVAD operating speed. The simulations were performed using a cardiovascular system model, which consists of active left atrium and ventricle, mitral and aortic valve leaflets, circulatory loop and a cf-LVAD. The cf-LVAD was operated between 7500rpm and 12,500rpm with 1000rpm intervals to simulate constant speed support. The same mean pump operating speeds over a cardiac cycle were applied in the direct operating speed modulation for the copulsative and counterpulsative direct speed modulation cf-LVAD support as in the constant speed support while the same pump-output over a cardiac cycle was applied to drive the pump in flow rate controlled copulsative and counterpulsative cf-LVAD support modes as in the constant speed support. Simulation results show that flow rate controlled counterpulsative pump support mode generates lower end-diastolic left ventricular volume and pressure–volume area while generating more physiological left ventricular volume signals over a cardiac cycle with respect to the other pump operating modes.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.03.003
  • Artifacts removal from EEG signal: FLM optimization-based learning
           algorithm for neural network-enhanced adaptive filtering
    • Authors: M.H. Quazi; S.G. Kahalekar
      Abstract: Publication date: Available online 4 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): M.H. Quazi, S.G. Kahalekar
      Electroencephalogram (EEG) denotes a neurophysiologic measurement, which observes the electrical activity of the brain through making a record of the EEG signal from the electrodes positioned on the scalp. The EEG signal gets mixed with other biological signals, called artifacts. Few artifacts include electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). Removal of artifacts from the EEG signal poses a great challenge in the medical field. Hence, the FLM (Firefly+Levenberg Marquardt) optimization-based learning algorithm for neural network-enhanced adaptive filtering model is introduced to eliminate the artifacts from the EEG. Initially, the EEG signal was provided to the adaptive filter for yielding the optimal weights using the renowned optimization algorithms, called firefly algorithm and LM. These two algorithms are effectively hybridized and applied to the neural network to find the optimal weights for adaptive filtering. Then, the designed filtering process renders an improved system for artifacts removal from the EEG signal. Finally, the performance of the proposed model and the existing models regarding SNR, computation time, MSE and RMSE are analyzed. The results conclude that the proposed method achieves a high SNR of 42.042dB.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.003
  • Physical activity recognition by smartphones, a survey
    • Authors: Jafet Morales; David Akopian
      Abstract: Publication date: Available online 4 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Jafet Morales, David Akopian
      Human activity recognition (HAR) from wearable motion sensor data is a promising research field due to its applications in healthcare, athletics, lifestyle monitoring, and computer–human interaction. Smartphones are an obvious platform for the deployment of HAR algorithms. This paper provides an overview of the state-of-the-art when it comes to the following aspects: relevant signals, data capture and preprocessing, ways to deal with unknown on-body locations and orientations, selecting the right features, activity models and classifiers, metrics for quantifying activity execution, and ways to evaluate usability of a HAR system. The survey covers detection of repetitive activities, postures, falls, and inactivity.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.004
  • Ensemble of classifiers and wavelet transformation for improved
           recognition of Fuhrman grading in clear-cell renal carcinoma
    • Authors: Michal Kruk; Jaroslaw Kurek; Stanislaw Osowski; Robert Koktysz; Bartosz Swiderski; Tomasz Markiewicz
      Abstract: Publication date: Available online 1 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Michal Kruk, Jaroslaw Kurek, Stanislaw Osowski, Robert Koktysz, Bartosz Swiderski, Tomasz Markiewicz
      The paper presents an improved system to recognition of Fuhrman grading in clear-cell renal carcinoma using an ensemble of classifiers. The novelty of solution includes the segmentation applying wavelet transformation in preprocessing stage, application of few selection methods for feature generation and using the ensemble of classifiers in final recognition step. The wavelet transformation is a very efficient tool for image de-noising and enhancing the edges of cell nuclei. The important distinction to other approaches is that diagnostic features of nuclei, based on the texture, geometry, color and histogram, are selected by using few methods, each relying on different mechanism of selection. These different sets of features have enabled creating the ensemble of classifiers based on the support vector machine and random forest, both cooperating with them. Such approach has led to the significant increase of the quality factors in comparison to the best existing results: sensitivity (the average of this solution 94.3% compared to 91.5%) and specificity (the average 98.6% compared to 97.5%.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.005
  • Multi-objective binary DE algorithm for optimizing the performance of
           Devanagari script-based P300 speller
    • Authors: Rahul Kumar Chaurasiya; Narendra D. Londhe; Subhojit Ghosh
      Abstract: Publication date: Available online 1 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Rahul Kumar Chaurasiya, Narendra D. Londhe, Subhojit Ghosh
      P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliability in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.006
  • Magnetic navigation and tracking of multiple ferromagnetic microrobots
           inside an arterial phantom setup for MRI guided drug therapy
    • Authors: Nitesh Kumar; Vivek Verma; Laxmidhar Behera
      Abstract: Publication date: Available online 21 April 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Nitesh Kumar, Vivek Verma, Laxmidhar Behera
      Magnetic steering of ferromagnetic microrobots facilitates active drug targeting and minimally invasive treatment of deep seated tumour cells. Several techniques for magnetic steering of nanostructured single microrobot in human vasculature exist but literatures on multirobot navigation are scarce. In the current work, preliminary experimental validation of a novel magnetic navigation model for multiple ferromagnetic microrobots is performed inside a bifurcated arterial phantom apparatus. The proposed model includes the formation of a single linear assembly of ferromagnetic microrobots inside the arterial setup placed under a magnetic field. This field is intended to mimic the magnetic field generated by a Magnetic Resonance Imaging (MRI) device which finds application in targeted drug therapy. The linear assembly process was studied with the help of Newtonian dynamics simulation including dipole–dipole and near field forces. As, the assembly was found to be structurally intact in a pulsatile flow, its simulated trajectory was controlled by manipulating a single microrobot present in the middle of the assembly. The trajectory convergence algorithm used for this purpose includes a fuzzy logic based nonlinear “Proportional-Integral-Derivative” (PID) control scheme with magnetic field gradient as its control input. Since most of the modern MRI devices are based on PID controller for manipulation of magnetic gradients, the proposed fuzzy PID based control scheme can easily be interfaced with these devices for the intended application.

      PubDate: 2017-04-28T07:06:38Z
      DOI: 10.1016/j.bbe.2017.04.002
  • Fast, accurate and robust retinal vessel segmentation system
    • Authors: Zhexin Jiang; Juan Yepez; Sen An; Seokbum Ko
      Abstract: Publication date: Available online 19 April 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Zhexin Jiang, Juan Yepez, Sen An, Seokbum Ko
      The accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic ophthalmological and cardiovascular diagnosis systems. Aside from accuracy, robustness and processing speed are also considered crucial for medical purposes. In order to meet those requirements, this work presents a novel approach to extract blood vessels from the retinal fundus, by using morphology-based global thresholding to draw the retinal venule structure and centerline detection method for capillaries. The proposed system is tested on DRIVE and STARE databases and has an average accuracy of 95.88% for single-database test and 95.27% for the cross-database test. Meanwhile, the system is designed to minimize the computing complexity and processes multiple independent procedures in parallel, thus having an execution time of 1.677s per image on CPU platform.

      PubDate: 2017-04-21T05:06:23Z
      DOI: 10.1016/j.bbe.2017.04.001
  • Comparative evaluation of EMG signal features for myoelectric controlled
           human arm prosthetics
    • Authors: Derya Karabulut; Faruk Ortes; Yunus Ziya Arslan; Mehmet Arif Adli
      Abstract: Publication date: Available online 31 March 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Derya Karabulut, Faruk Ortes, Yunus Ziya Arslan, Mehmet Arif Adli
      Myoelectric controlled human arm prosthetics have shown a promising performance with regards to the supplementation of the basic manipulation requirements for amputated people over recent years. However these assistive devices still have important restrictions in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyography (EMG) is used as the control signal to command such prosthetic devices to ensure the amputated people to compensate their fundamental movement patterns. The ability of extraction of clear and certain neural information from EMG signals is a critical issue in fine control of hand prosthesis movements. Various signal processing methods have been employed for feature extraction from EMG signals. In this study, it was aimed to comparatively evaluate the widely used time domain EMG signal features, i.e., integrated EMG (IEMG), root mean square (RMS), and waveform length (WL) in estimation of externally applied forces to human hands. Once the signal features were extracted, classification process was applied to predict the external forces using artificial neural networks (ANN). EMG signals were recorded during two types of muscle contraction: (i) isometric and isotonic, and (ii) anisotonic and anisometric contractions. Experiments were implemented by six healthy subjects from the muscles that are proximal to the upper body, i.e., biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically evaluated and, merits and shortcomings of the features were discussed. Findings of the study are expected to provide better insight regarding control structure of the EMG-based motion assistive devices.

      PubDate: 2017-04-07T01:33:44Z
      DOI: 10.1016/j.bbe.2017.03.001
  • Classification of falling asleep states using HRV analysis
    • Authors: Zbigniew Piotrowski; Małgorzata Szypulska
      Abstract: Publication date: Available online 6 March 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Zbigniew Piotrowski, Małgorzata Szypulska
      The article presents the results of studies on drowsiness and drowsiness detection performed using heart rate variability analysis (HRV). The results of those studies indicate that the most significant parameters, from the standpoint of classification of drowsiness are the following parameters of the HRV analysis: the low and high frequency band the ratio of the tachogram power in the LF and HF bands, and the total power distribution. The best detection results were obtained for the following methods, in the following order: the nearest neighborhood with metrics: standardized Euclides and Mahalanobis, the square discriminant analysis, and the Bayesian classifier. In order to classify drowsiness periods, a neural network was also used; it consisted of four inputs, six neurons in the hidden layer, and three outputs, one of which was assigned to one of the accepted classes. In order to obtain the most effective learning, a linear feed forward network was designed using back propagation of errors and the RPROP algorithm. In the case of this type of networks, the achieved accuracy of the individual classes was on the level of 98.7%.

      PubDate: 2017-03-11T11:29:06Z
      DOI: 10.1016/j.bbe.2017.02.003
  • A two dimensional approach for modelling of pennate muscle behaviour
    • Authors: Wiktoria Wojnicz; Bartlomiej Zagrodny; Michal Ludwicki; Jan Awrejcewicz; Edmund Wittbrodt
      Abstract: Publication date: Available online 24 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Wiktoria Wojnicz, Bartlomiej Zagrodny, Michal Ludwicki, Jan Awrejcewicz, Edmund Wittbrodt

      PubDate: 2017-02-27T07:45:10Z
      DOI: 10.1016/j.bbe.2016.12.004
  • Analyzing effects of ELF electromagnetic fields on removing bacterial
    • Authors: Turhan Karaguler; Hasan Kahraman; Melek Tuter
      Abstract: Publication date: Available online 22 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Turhan Karaguler, Hasan Kahraman, Melek Tuter

      PubDate: 2017-02-27T07:45:10Z
      DOI: 10.1016/j.bbe.2016.11.005
  • Full-automatic computer aided system for stem cell clustering using
           Content-based Microscopic Image Analysis
    • Authors: Chen Li; Xinyu Huang; Tao Jiang; Ning Xu
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Chen Li, Xinyu Huang, Tao Jiang, Ning Xu
      Stem cells are very original cells that can differentiate into other cells, tissues and organs, which play a very important role in biomedical treatments. Because of the importance of stem cells, in this paper we propose a full-automatic computer aided clustering system to assist scientists to explore potential co-occurrence relations between the cell differentiation and their morphological information in phenotype. In this proposed system, a multi-stage Content-based Microscopic Image Analysis (CBMIA) framework is applied, including image segmentation, feature extraction, feature selection, feature fusion and clustering techniques. First, an Improved Supervised Normalized Cuts (ISNC) segmentation algorithm is newly introduced to partition multiple stem cells into individual regions in an original microscopic image, which is the most important contribution in this paper. Then, based on the segmented stem cells, 11 different feature extraction approaches are applied to represent the morphological characteristics of them. Thirdly, by analysing the robustness and stability of the extracted features, Hu and Zernike moments are selected. Fourthly, these two selected features are combined by an early fusion approach to further enhance the properties of the feature representation of stem cells. Finally, k-means clustering algorithm is chosen to classify stem cells into different categories using the fused feature. Furthermore, in order to prove the effectiveness and usefulness of this proposed system, we carry out a series of experiments to evaluate our methods. Especially, our ISNC segmentation obtains 92.4% similarity, 96.0% specificity and 107.8% ration of accuracy, showing the potential of our work.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.01.004
  • Non-uniform viscosity caused by red blood cell aggregation may affect NO
           concentration in the microvasculature
    • Authors: Huiting Qiao; Hongjun Zhao; Dov Jaron
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Huiting Qiao, Hongjun Zhao, Dov Jaron
      Aggregation of red blood cells in the micro vasculature may affect blood viscosity in the vessel. The purpose of this study was to investigate the potential effect of non-uniform viscosity caused by red blood cell (RBC) aggregation on nitric oxide (NO) concentration and distribution. A 3-D multi-physics model was established to simulate the production, transport and consumption of NO. Two non-uniform viscosity models caused by RBC aggregation were investigated: one assuming a linear and the other a step hematocrit distribution. In addition, the effect of the thickness of the plasma layer was tested. Simulation results demonstrate that non-uniform viscosity caused by RBCs aggregation influences NO concentration distribution. Compared with the uniform viscosity model, NO concentration using non-uniform viscosity is lower than that using uniform viscosity. Moreover, NO concentration calculated from the step hematocrit model is higher than that calculated from the linear hematocrit model. NO concentrations in the endothelium and the vascular wall decrease with the decline of the thickness of the plasma layer. The relative decrease differs between the linear and the step model. Our results suggest that non-uniform viscosity caused by red blood cell aggregation affects nitric oxide distribution in the micro vasculature. If uniform viscosity is assumed when performing numerical simulations, NO concentration values may be overestimated.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2016.10.004
  • An efficient wavelet-based automated R-peaks detection method using
           Hilbert transform
    • Authors: Manas Rakshit; Susmita Das
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Manas Rakshit, Susmita Das
      Machine-aided detection of R-peaks is becoming a vital task to automate the diagnosis of critical cardiovascular ailments. R-peaks in Electrocardiogram (ECG) is one of the key segments for diagnosis of the cardiac disorder. By recognizing R-peaks, heart rate of the patient can be computed and from that point onwards heart rate variability (HRV), tachycardia, and bradycardia can also be determined. Most of the R-peaks detectors suffer due to non-stationary behaviors of the ECG signal. In this work, a wavelet transform based automated R-peaks detection method has been proposed. A wavelet-based multiresolution approach along with Shannon energy envelope estimator is utilized to eliminate the noises in ECG signal and enhance the QRS complexes. Then a Hilbert transform based peak finding logic is used to detect the R-peaks without employing any amplitude threshold. The efficiency of the proposed work is validated using all the ECG signals of MIT-BIH arrhythmia database, and it attains an average accuracy of 99.83%, sensitivity of 99.93%, positive predictivity of 99.91%, error rate of 0.17% and an average F-score of 0.9992. A close observation of the simulation and validation indicates that the suggested technique achieves superior performance indices compared to the existing methods for real ECG signal.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.02.002
  • Investigation of opacity development in the human eye for estimation of
           the postmortem interval
    • Authors: İsmail Cantürk; Safa Çelik; M. Feyzi Şahin; Fatih Yağmur; Sadık Kara; Fethullah Karabiber
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): İsmail Cantürk, Safa Çelik, M. Feyzi Şahin, Fatih Yağmur, Sadık Kara, Fethullah Karabiber
      Estimation of the postmortem interval (PMI) has attracted the attention of many researchers. It is generally accepted as a challenging task in forensic medicine. Due to its difficulty, researchers have tried to estimate the PMI using different physical and chemical techniques. Since the PMI estimation accuracies of previous studies are not at the desired level, new methods should be developed to more accurately estimate the PMI. The development of opacity in the eye in the PMI might be an important breakthrough in this field. After death, corneal hydration occurs due to degenerated endothelial cells. The degenerated endothelial barrier of the cornea cannot prevent the flow of aqueous humor to the cornea, which results in opacity. The amount of aqueous humor in the cornea determines the level of opacity. Since the flow of aqueous humor to the cornea will continue for a while, opacity is expected to increase with the PMI. In this study, images of human eyes were investigated using computer-based image analysis. The corneal and non-corneal opacities of the recorded eye images increase during the experiment. The experimental results prove that there is a correlation between the elapsed time after death and the development of opacity in the corneal and non-corneal regions in human cases. Exponential curve fitting is employed to observe the decay of the opacity over time. A repeated ANOVA test is also used to show that the opacity development is statistically significant.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.02.001
  • A hierarchical classification method for automatic sleep scoring using
           multiscale entropy features and proportion information of sleep
    • Authors: Pan Tian; Jie Hu; Jin Qi; Xian Ye; Datian Che; Ying Ding; Yinghong Peng
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Pan Tian, Jie Hu, Jin Qi, Xian Ye, Datian Che, Ying Ding, Yinghong Peng
      Background Sleep scoring is a critical step in medical researches and clinical applications. Traditional visual scoring method is based on the processing of physiological signals, such as electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG), which is a time consuming and subjective procedure. It is an urgent task to develop an effective method for automatic sleep scoring. Method This paper presents a hierarchical classification method for automatic sleep scoring by combining multiscale entropy features with the proportion information of the sleep architecture. Based on a three-layer classification scheme, sleep is categorized into five stages (Awake, S1, S2, SWS and REM). Specifically, the first layer is a standard SVM which performs classification between Awake and Sleep, while the second and third layers are implemented by combining probabilistic output SVM with proportion-based clustering. Multiscale entropy (MSE) from electroencephalogram (EEG) is extracted to represent signal characteristics in multiple temporal scales. Results The proposed method is evaluated with 20 sleep recordings, including 10 subjects with mild difficulty falling asleep and 10 healthy subjects. The overall accuracy of the proposed method is 91.4%. Compared with traditional methods, the classification accuracy of the proposed method is more balanced and the global performance is much better. The dataset includes both healthy subjects and subjects with sleep disorders, which means the presented method has good generalization capacity. Experimental results demonstrate the feasibility of the attempt to introduce proportion information into automatic sleep scoring.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.01.005
  • Stress–strain characteristic of human trabecular bone based on depth
           sensing indentation measurements
    • Authors: Marek Pawlikowski; Konstanty Skalski; Jakub Bańczerowski; Anna Makuch; Krzysztof Jankowski
      Abstract: Publication date: Available online 15 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marek Pawlikowski, Konstanty Skalski, Jakub Bańczerowski, Anna Makuch, Krzysztof Jankowski
      In the paper a relation between stress and strain for trabecular bone is presented. The relation is based on the results of depth sensing indentation (DSI) tests which were performed with a spherical indenter. The DSI technique allowed also to determine three measures of hardness, i.e. Martens hardness (H M), nanohardness (H IT), Vickers hardness (H V) and Young modulus E IT of the trabecular bone tissue. The bone samples were harvested from human femoral heads during orthopaedical procedures of hip joint implantation. In the research the Hertzian approach is undertaken. The constitutive relation is then formulated in the elastic domain. The values of hardness and the Young modulus obtained from the DSI tests are in good agreement with those found in literature. The stress–strain relation is formulated to implement it in the future in finite element analyses of trabecular bone. Such simulations allow to take into account the microstructural mechanical properties of the trabecular tissue as well as remodelling phenomenon. This will make it possible to analyse the stress and strain states in bone for engineering and medical purposes.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.01.002
  • Stress response of patellofemoral joint subjected to femoral retroversion
           with various patellar kinematics and flexions – An FEA study
    • Authors: Marlon Jones Louis; R. Malayalamurthi
      Abstract: Publication date: Available online 10 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marlon Jones Louis, R. Malayalamurthi
      The purpose of this study is to observe the stress response of the patellofemoral joint associated with three patellar kinematics: shift, spin and tilt under femoral retroversion conditions. By assigning various flexions and different loads, the stresses were quantified in the bones, tendons, cartilages and cartilage–bone interface. Four different loads of 600, 657, 706 and 753N were applied on 12 models representing each of the various kinematics of shifts, spins and tilts of the patella with femoral flexions of 30°, 60°, 90° and 120° which gave results for 48 analyses. The ‘Q’ angle of the femur bone was maintained at 14° with femoral retroversion of 21°. Based on the patellar kinematics, three different cases were modeled as (a) 5mm shift 10° spin 4° tilt, (b) 10mm shift 13° spin 8° tilt, and (c) 15mm shift 16° spin 12° tilt. Medial shift, spin and tilt with femoral retroversion were limited in this study. The femoral displacement for 30° flexion at 600N was found to be same in all the (a), (b), and (c) cases. Similarly, respective same displacements were achieved in all three cases when subjected to 60° flex at 657N, 90° flex at 706N and 120° flex at 753N. From the simulated results it is inferred that femoral retroversion with case (b) kinematics susceptibly dominated by the cartilages causes patellofemoral joint pain, arthritis and instability due to the larger contact areas between the patella and femur bone at flexions 60° and 90°.

      PubDate: 2017-02-13T23:08:38Z
      DOI: 10.1016/j.bbe.2016.12.006
  • Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy
           regression model
    • Authors: Narges Shafaei Bajestani; Ali Vahidian Kamyad; Ensieh Nasli Esfahani; Assef Zare
      Abstract: Publication date: Available online 5 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Narges Shafaei Bajestani, Ali Vahidian Kamyad, Ensieh Nasli Esfahani, Assef Zare
      Choosing a proper method to predict and timely prevent the complications of diabetes could be considered a significant step toward optimally controlling the disease. Since in medical research only small sample sizes of data are available and medical data always includes high levels of uncertainty and ambiguity, a type-2 fuzzy regression model seems to be an appropriate procedure for finding the relationship between outcome and explanatory variables in medical decision-making. In this paper, a new type-2 fuzzy regression model based on type-2 fuzzy time series concepts is used to forecast nephropathy in diabetic patients. Results in two examples show model efficiency. The use of such models in diabetes clinics is proposed.

      PubDate: 2017-02-08T20:51:04Z
      DOI: 10.1016/j.bbe.2017.01.003
  • Finite element analysis of stresses generated in cortical bone during
           implantation of a novel Limb Prosthesis Osseointegrated Fixation System
    • Authors: Piotr Prochor
      Abstract: Publication date: Available online 4 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Piotr Prochor
      The aim of this study was a biomechanical evaluation of the stresses generated in bone during implantation of the implant designed for direct skeletal attachment of limb prosthesis and a typical interference-fit implant of the reference. Using the finite element method implantation processes of both implants were modelled. The influence of two factors on stresses generated in bone was analysed: first – the radial interference between the implant and reamed marrow cavity (0.05mm up to 0.25mm) and second – the three types of implant's surfaces: polished, beaded and flaked. Obtained results show that in the case of the smallest value of radial interference (0.05mm), stresses generated in cortical bone are more appropriate for the reference implant than for the designed one. With the increase of both analysed factors generated stresses are in favour of the designed implant especially in longitudinal direction for both, implant-adjacent and deep cortical tissue (even 18 times lower) alike. Stresses patterns also present that stresses values are lower in overall volume of analysed bone's part, during implantation of the designed implant. Presented characteristics and patterns confirm that the implantation method of presented implant is safer than a method for typical interference-fit implants for direct skeletal attachment of limb prosthesis.

      PubDate: 2017-02-08T20:51:04Z
      DOI: 10.1016/j.bbe.2016.12.001
  • A generalized method for the detection of vascular structure in
           pathological retinal images
    • Authors: Jaskirat Kaur; Deepti Mittal
      Abstract: Publication date: Available online 28 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Jaskirat Kaur, Deepti Mittal
      Variations in blood vasculature morphology of retinal fundus images is one of the dominant characteristic for the early detection and analysis of retinal abnormalities. Therefore the accurate interpretation of blood vasculature is useful for ophthalmologists to diagnose patients that suffer from retinal abnormalities. A generalized method to detect and segment blood vasculature using retinal fundus images has been proposed in this work using (i) preprocessing for quality improvement of retinal fundus images, (ii) initial segmentation of vasculature map to find vascular and non vascular structures, (iii) extraction of relevant set of geometrical based features from the vasculature map and intensity based features from original retinal fundus image that differentiate vascular and non vascular structures efficiently, (iv) supervised classification of vascular and non vascular structures using the extracted features, and (v) joining of candidate vascular structures to create connectivity. The proposed method is evaluated on clinically acquired dataset and different publically available standard datasets such as DRIVE, STARE, ARIA and HRF. The clinically acquired dataset consists of 468 retinal fundus images comprising of healthy images, images with mild, intermediate and severe pathologies. Test results of the proposed method shows average sensitivity/specificity/accuracy of 85.43/97.94/95.45 on the 785 retinal fundus images. The proposed method shows an improvement of 14.01% in sensitivity without degrading specificity and accuracy in comparison to the recently published methods.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2016.09.002
  • Trends and perspectives in modification of zirconium oxide for a dental
           prosthetic applications – A review
    • Authors: D.S. Nakonieczny; A. Ziębowicz; Z.K. Paszenda; C. Krawczyk
      Abstract: Publication date: Available online 28 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): D.S. Nakonieczny, A. Ziębowicz, Z.K. Paszenda, C. Krawczyk
      Full-ceramic dental restorations made from ZrO2 have become increasingly popular due to their aesthetics and mechanical strength, and are gradually replacing prostheses made of porcelain fused to metal. Nevertheless, due to the variability in the physicochemical properties in a wet environment at elevated temperature, zirconia is quite a controversial material, the use of which in the environment of the mouth is questionable and raises many concerns. The reason for the variability in the physicochemical changes is the martensitic transformation in which metastable phases (β, γ) change into the stable phase (α). For biomedical applications, the most desired is the β-phase. A very unfavourable phenomenon accompanying the martensitic transformation in a wet environment is low temperature degradation, which is an autocatalytic process accelerating negative changes in ZrO2. The aim of this review is a comprehensive study of the degradation phenomenon problems according to prosthetic treatment with a fixed prosthesis and ways to reduce it.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2016.10.005
  • Development of the deterministic and stochastic Markovian model of a
           dendritic neuron
    • Authors: Aleksandra Świetlicka; Karol Gugała; Witold Pedrycz; Andrzej Rybarczyk
      Abstract: Publication date: Available online 28 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Aleksandra Świetlicka, Karol Gugała, Witold Pedrycz, Andrzej Rybarczyk
      In this study, we propose a model of the dendritic structure of the neuron (referred to as a neural network – NN), which can be viewed as an extension of the models that are currently used in the description of the potential on the neuron's membrane. The proposed extensions augment the generic model and offer a fuller description of the neuron's nature. The common assumption being used in most of the previous models stating a single channel (forming component of the neuron's membrane) can be positioned in only one of the two states (permissive – open and non-permissive – closed), is now relaxed by allowing the channel to be positioned in more states (five or eight states). The relationship between these states is expressed in terms of Markov kinetic schemes. In the paper, we demonstrate that the new approach is more suitable for a larger number of applications than the conventional Hodgkin–Huxley model. The study, by providing the mathematical background of the new extended model, forms a significant step towards a hardware implementation of the biologically realistic neural network (NN) of this type. To reduce the number of components required in such implementation, we propose a new optimization technique that significantly reduces the computational complexity of a single neuron.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2016.10.002
  • A classification framework for prediction of breast density using an
           ensemble of neural network classifiers
    • Authors: Indrajeet Kumar; H.S. Bhadauria; Jitendra Virmani; Shruti Thakur
      Abstract: Publication date: Available online 27 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Indrajeet Kumar, H.S. Bhadauria, Jitendra Virmani, Shruti Thakur
      The present work proposes a classification framework for the prediction of breast density using an ensemble of neural network classifiers. Expert radiologists, visualize the textural characteristics of center region of a breast to distinguish between different breast density classes. Accordingly, ROIs of fixed size are cropped from the center location of the breast tissue and GLCM mean features are computed for each ROI by varying inter-pixel distance ‘d’ from 1 to 15. The proposed classification framework consists of two stages, (a) first stage: this stage consists of a single 4-class neural network classifier NN0 (B-I/B-II/B-III/B-IV) which yields the output probability vector [P B-I PB-II P B-III P B-IV] indicating the probability values with which a test ROI belongs to a particular breast density class. (b) second stage: this stage consists of an ensemble of six binary neural network classifiers NN1 (B-I/B-II), NN2 (B-I/B-III), NN3 (B-I/B-IV), NN4 (B-II/B-III), NN5 (B-II/B-IV) and NN6 (B-III/B-IV). The output of the first stage of the classification framework, i.e. output on NN0 is used to obtain the two most probable classes for a test ROI. In the second stage this test ROI is passed through one of the binary neural networks, i.e. NN1 to NN6 corresponding to the two most probable classes predicted by NN0. After passing the entire test ROIs through the second stage, the overall accuracy increases from 79.5% to 90.8%. The promising results achieved by the proposed classification framework indicate that it can be used in clinical environment for differentiation between breast density patterns.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2017.01.001
  • In vitro test method for the development of intelligent lower limb
           prosthetic devices
    • Authors: Cristiano Marinelli; Hermes Giberti; Ferruccio Resta
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Cristiano Marinelli, Hermes Giberti, Ferruccio Resta
      In recent decades, the technological progress has contributed to the development of appliances that significantly improve human life. The biomedical field has benefited more than others from this innovation process. In particular, robotics advances have led to the development of prostheses that allow who suffered the amputation of a lower limb to walk almost like a healthy person. Although sophisticated, the current solutions are not yet able to completely reestablish the function of their biological counterpart. According to authors’ opinion this deficiency is principally due to the lack of suitable development and verification methods rather than of appropriate technology resources. Therefore, an innovative bench for testing lower limb prostheses considering working conditions more realistic than those defined by the legislation in force is presented in this paper. The mechanical setup is composed of a 6-axis industrial robot and a custom 2-axis active force plate. The first one is used to replicate the movements of the limb residual segment in space. The second one to load the prosthetic foot both in longitudinal and vertical direction, that is, in the sagittal plane. Both the design choices and the operation procedure are illustrated. Then, a numerical model of the bench is developed in order to assess the merits and the limits of the proposed solution.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.003
  • Ictal EEG classification based on amplitude and frequency contours of IMFs
    • Authors: K.S. Biju; Hara Abdul Hakkim; M.G. Jibukumar
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): K.S. Biju, Hara Abdul Hakkim, M.G. Jibukumar
      Electroencephalogram (EEG) signal serves is a powerful tool in epilepsy detection. This study decomposes intrinsic mode functions (IMFs) into amplitude envelope and frequency functions on a time-scale basis using the analytic function of Hilbert transform. IMFs results from the empirical mode decomposition of EEG signals. Features such as energy and entropy parameters were calculated from the amplitude contour of each IMF. Other features, such as interquartile range, mean absolute deviation and standard deviation are also computed for their instantaneous frequencies. Discriminative features were extracted using a large database to classify healthy and ictal EEG signals. Normal EEG segments were differentiated from the seizure attack in individual IMF features, multiple features with individual IMF, and individual features with multiple IMFs. Discriminating capability of three Cases was tested. (i) Artificial neural network and (ii) adaptive neuro-fuzzy inference system classification were used to identify EEG segments with seizure attacks. ANOVA was used to analyze statistical performance. Energy and entropy-based features of instantaneous amplitude and standard deviation of instantaneous frequency of IMF2 and IMF1 have 100% accuracy, sensitivity, and specificity. Good performance with a single feature that represents information of the whole data was obtained. The result involved less complicated computation than other time–frequency analysis techniques.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.12.005
  • Automatic segmentation of infant brain MR images: With special reference
           to myelinated white matter
    • Authors: Chelli N. Devi; Anupama Chandrasekharan; V.K. Sundararaman; Zachariah C. Alex
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Chelli N. Devi, Anupama Chandrasekharan, V.K. Sundararaman, Zachariah C. Alex
      Automatic segmentation of infant brain images is faced with numerous challenges like poor image contrast, motion artifacts, and changes caused by progressive myelination of the infant brain. Since timely myelination points to normal brain maturity, monitoring the progress and degree of myelination is clinically significant. However, most of the existing segmentation methods do not segment myelinated portions of the infant brain. In this paper, we propose a segmentation approach focused on segmenting the myelinated white matter tissue in T1-weighted magnetic resonance images of the infant brain. The novelty of the algorithm lies in the introduction of a weighted localized Tsallis entropy based thresholding method. The proposed method is also tested on older babies beyond the one-year age mark to verify its utility and robustness. It is seen that the mean Dice coefficients obtained for myelin segmentation by the proposed weighted localized method are higher than that of the other methods, namely, the conventional Tsallis entropy thresholding and modified localized method.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.11.004
  • Automatic contrast enhancement of brain MR images using Average Intensity
           Replacement based on Adaptive Histogram Equalization (AIR-AHE)
    • Authors: Iza Sazanita Isa; Siti Noraini Sulaiman; Muzaimi Mustapha; Noor Khairiah A. Karim
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Iza Sazanita Isa, Siti Noraini Sulaiman, Muzaimi Mustapha, Noor Khairiah A. Karim
      Medical imaging is the most established technique of visualizing the interior of the human body without the risk of the non-invasive effect. This technology is designed to produce images, and it is also capable of representing information about the screening location. In MRI imaging, the poor image quality particularly the low contrast image may provide insufficient data for the visual interpretation of such affected locations. Therefore, the need of image enhancement arises to improve image visions and also to computationally support the image processing technique. In general, conventional contrast enhancement methods may work well for some images. However, in MRI brain image, there are often more complex situations where the WMH signal is high but it may mistakenly be considered as other brain tissues such as CSF. With the motivation to classify the most possible WMH regions, this paper proposes a novel image contrast algorithm of WMH enhancement for MRI image. This algorithm is also known as the Average Intensity Replacement – Adaptive Histogram Equalization (AIR-AHE). The proposed algorithm is applied to the FLAIR image based on the intensity adjustment and contrast mapping techniques. The proposed algorithm for the image enhancement is superior to the existing methods by using image evaluation quantitative methods of PSNR, average gradient values and MSE. Furthermore, the edge information pertaining to the potential WMH regions can effectively increase the accuracy of the results.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.12.003
  • 3D vascular tree segmentation using a multiscale vesselness function and a
           level set approach
    • Authors: Tomasz Woźniak; Michał Strzelecki; Agata Majos; Ludomir Stefańczyk
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Tomasz Woźniak, Michał Strzelecki, Agata Majos, Ludomir Stefańczyk
      The paper presents a method aimed at segmentation of a vascular network in 3D medical data. The method implements an extended version of a vesselness function that considers multiscale image filtering to emphasize vessels of different diameters. This function is combined with a level set approach based on a Chan–Vese model. The proposed method was evaluated on medical images of the brain and hand vasculature. These images were obtained by different modalities, including angio-CT and two MR acquisition protocols. The proposed technique was quantitatively validated for the tree phantom image by assessing segmentation accuracy and for the angio-CT images by estimating diameters of vessel fragments. Two radiologists provided also qualitative evaluation of this approach. It was demonstrated that this method ensures correct segmentation of a vessel tree in the analyzed images. Moreover, it enables detection of thinner vessel branches when compared to single scale vesselness function approaches.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.11.003
  • Optimization of multi-slot coaxial antennas for microwave thermotherapy
           based on the S11-parameter analysis
    • Authors: Piotr Gas
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Piotr Gas
      The underlying aim of presented article is to determine the optimal location and sizes of the air gaps inside a multi-slot coaxial antenna with 50-ohm feed based on the S 11-parameter characteristics of microwave applicator to get the best antenna impedance matching to the treated tissue. The next step is the selection of the levels limits of the antenna input power, for which temperature of the tissue do not exceed known therapeutic elevations for microwave therapies at hyperthermic and ablation temperatures. The proposed approach provides a relatively simple method for optimization of the location and size of slots in the antenna structure. The proper choice of limit values of total antenna input power enables appropriate adjustment of temperature of the target tissue to preserve optimal cancer treatment.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.001
  • Reliability of stiffness measurement device during passive isokinetic
           spastic wrist movements of healthy subjects and hemiplegics
    • Authors: Abbas Orand; Hiroyuki Miyasaka; Kotaro Takeda; Genichi Tanino; Takeshi Chihara; Hidehiko Beppu; Shigeru Sonoda
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Abbas Orand, Hiroyuki Miyasaka, Kotaro Takeda, Genichi Tanino, Takeshi Chihara, Hidehiko Beppu, Shigeru Sonoda
      The consistency of torque measurements during repetitive moving arm movements and also during passive wrist movements at two angular velocities of slow (∼6°/s) and moderate (∼120°/s) was investigated. The designed and developed device was applied to 3 cases, to a spring, to 8 able-bodied subjects and to 2 hemiplegic patients. While the mean of the intra-class correlation coefficient of subjects were 0.65 and 0.75 for slow and moderate angular velocities, those of the hemiplegic patients and the spring respectively ranged between excellent values of 0.93–1 and 0.91–1. The Pearson's product-moment correlation coefficients of the 3 cases for the 2 slow and moderate angular velocities ranged between 0.80 and 1. We could verify that the device can be used in our future researches and it can (1) reliably rotate a moving arm horizontally with angular velocities between 3 and 350°/s constantly in a range of motion between −60 and 60° and (2) simultaneously capture the data of angular displacement, torque, and two electromyogram activities. For the standardization of our future studies with the device, we could verify the stability of the last two repeated passive wrist movements in case of patients. The results of the study with the able-bodied subjects are also important as a reference for our studies with the hemiplegic.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.001
  • Grafting of oxidized carboxymethyl cellulose with hydrogen peroxide in
           presence of Cu(II) to chitosan and biological elucidation
    • Authors: Saida Benghanem; Asma Chetouani; Meriem Elkolli; Mahmoud Bounekhel; Djafar Benachour
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Saida Benghanem, Asma Chetouani, Meriem Elkolli, Mahmoud Bounekhel, Djafar Benachour
      The chemical interaction of chitosan (CS) is performed in the presence of sodium carboxymethyl cellulose (CMC) and/or oxidized CMC. The latter is obtained by the action of H2O2/CuSO4 to generate carbonyl and carboxyl groups which were increased with CuSO4 concentration. The characterization of these new materials is made by FTIR, TGA, XRD and SEM. Examination of the hemolytic potential showed that the hydrogels were non hemolytic in nature. The hydrogels were non-toxic and blood-compatible. The antibacterial and antioxidant activities of samples were investigated.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.003
  • Electromyography and mechanomyography signal recognition: Experimental
           analysis using multi-way array decomposition methods
    • Authors: Andrzej Wołczowski; Rafał Zdunek
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Andrzej Wołczowski, Rafał Zdunek
      In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the highest classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.004
  • Verification of the new ‘all ages’ spirometric reference values for
           use in young Polish children of Caucasian origin
    • Authors: Waldemar Tomalak; Jakub Radliński; Halina Bańka; Bożena Myszkal; Krzysztof Żarnowski; Zbigniew Doniec; Jaroslav Fabry
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Waldemar Tomalak, Jakub Radliński, Halina Bańka, Bożena Myszkal, Krzysztof Żarnowski, Zbigniew Doniec, Jaroslav Fabry
      Interpretation of the spirometric results in young children aged 3 years and onward was a difficult task, because existing reference values usually covered age range of 7–18 years. Recently two big studies concerning ‘all ages’ reference equations were published: the study of The Asthma UK Initiative (Stanojevic et al. AJRCCM 2009) and the so called GLI2012 values (Quanjer et al. ERJ 2012); both providing equations with LMS approach for spirometric reference values for age range of 3–95 years. The aim of the study was to test the applicability of the new sets of equations in a group of healthy Polish children of Caucasian descent. The analysis was performed on a data gathered from children admitted to outpatient department for diagnostic reasons. Children performed impulse oscillometry (IOS) measurements and spirometry. Elevated value of oscillometric resistance at 5Hz (R5) eliminated children from analysis as well as forced expiratory time less than 1.5s. Final analysis was performed on results obtained from 142 children aged 4–10 years. Z-scores and percent of predicted values were calculated for FEV1, FVC and FEV1/FVC using both sets; additionally z-score and percent predicted was also calculated for FEV0.75/FVC using Stanojevic's equation. The distribution of all calculated z-scores was normal. For FEV1/FVC mean±SD of z-score was 0.01±0.80 using GLI2012 and −0.15±0.79 using Stanojevic's set. Mean value of percent predicted values using GLI2012 was 100.2±5.5% for FEV1/FVC, 107.4±9.4% for FEV1 and 106.6±10.1% for FVC. Our results confirm applicability of the new sets of reference values in young Caucasian children from Poland and point out the potential diagnostic value of FEV0.75/FVC.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.006
  • Bat optimization based neuron model of stochastic resonance for the
           enhancement of MR images
    • Authors: Munendra Singh; Ashish Verma; Neeraj Sharma
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Munendra Singh, Ashish Verma, Neeraj Sharma
      Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.006
  • The intima with early atherosclerotic lesions is load-bearing component of
           human thoracic aorta
    • Authors: Magdalena Kobielarz; Marta Kozuń; Aleksandra Kuzan; Krzysztof Maksymowicz; Wojciech Witkiewicz; Celina Pezowicz
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Magdalena Kobielarz, Marta Kozuń, Aleksandra Kuzan, Krzysztof Maksymowicz, Wojciech Witkiewicz, Celina Pezowicz
      The aim of the study was to evaluate the mechanical properties of the adventitia, media, and especially intima of the human thoracic aortic wall in the early stages of atherosclerosis (stage I to III according to the Stary's classification). Histological and immunohistochemical techniques were used to evaluate the severity of atherosclerosis and the correctness of separation of the respective layers. Circumferential specimens of the adventitia, media, and intima (n =193) were prepared from 27 arteries. The mechanical properties, i.e. the ultimate tensile strength, the maximum strain, and the maximum tangential elastic moduli, were determined in uniaxial tensile test and presented as a median (Me). The tensile strength of the intima (Me=105kPa) is comparable to the media (Me=123kPa) and lower than for the adventitia (Me=808.5kPa). The intima also undergoes the lowest maximum strain (Me=0.008), and its elastic modulus (Me=11,510kPa) is significantly higher compared to the media (Me=5280kPa). Therefore, presented results indicate that even in the early stages of atherosclerotic development the intima takes part in the process of mechanical loads bearing.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.008
  • An objective method to identify optimum clip-limit and histogram
           specification of contrast limited adaptive histogram equalization for MR
    • Authors: Justin Joseph; Sivaraman Jayaraman; R. Periyasamy; V.R. Simi
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Justin Joseph, Sivaraman Jayaraman, R. Periyasamy, V.R. Simi
      In contrast limited adaptive histogram equalization (CLAHE), the selection of tile size, clip-limit and the distribution which specify desired shape of the histogram of image tiles is paramount, as it critically influences the quality of the enhanced image. The optimal value of these parameters devolves on the generic of the image to be enhanced and usually they are selected empirically. In this paper, the degradation of intensity, textural and geometric features of the medical image with respect to the variation in clip-limit and specified histogram shape is analyzed. The statistical indices used to quantify the feature degradation are Absolute Mean Brightness Error (AMBE), Absolute Deviation in Entropy (ADE), Peak Signal to Noise Ratio (PSNR), Variance Ratio (VR), Structural Similarity Index Matrix (SSIM) and Saturation Evaluation Index (SEI). The images used for the analysis are axial plane MR images of magnetic resonance spectroscopy (MRS), under gradient recalled echo (GRE), diffusion weighted imaging (DWI) 1000b Array Spatial Sensitivity Encoding Technique (ASSET), T2 Fluid Attenuation Inversion Recovery (FLAIR) and T1 Fast Spin-Echo Contrast Enhanced (FS-ECE) series of pre-operative Glioblastoma-edema complex. The experimental analysis was performed using Matlab®. Results show that for MR images the exponential histogram specification with a clip-limit of 0.01 is found to be optimum. At optimum clip-limit, the mean of SSIM exhibited by the Rayleigh, uniform and exponential histogram specification were found to be 0.7477, 0.7946 and 0.8457, for ten sets of MR images and mean of variance ratio are 1.242, 2.0316 and 1.7711, respectively.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.11.006
  • A full reference Morphological Edge Similarity Index to account processing
           induced edge artefacts in magnetic resonance images
    • Authors: P.G. Kuppusamy; Justin Joseph; J. Sivaraman
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): P.G. Kuppusamy, Justin Joseph, J. Sivaraman
      An objective measure of edge similarity between the original and processed images to quantify the processing induced artefacts in medical image computing is proposed in this article. Globally accepted Image Quality Analysis (IQA) indices such as Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) measure the structural similarity between the original and processed image and do not specifically reflect the resemblance of the edge content. Most of the IQA indices either do not comply with the subjective quality ratings or they are prone to noise level. In the proposed Morphological Edge Similarity Index (MESI), the binary edge maps of the reference and processed images are generated via gradient based threshold and these edge maps are objectively compared to yield a reliable edge quality metric. The index is found superior to Edge Preservation Index (EPI), Edge Strength Similarity based Image quality Metric (ESSIM) and SSIM in terms of dynamic variability, correlation with subjective quality ratings, robustness to noise and sensitivity to degradation in edge quality caused by blockiness artefacts in image compression. MESI exhibits a correlation of 0.9985, very close to unity, with the subjective quality ratings. It is useful for objectively evaluating the performance of denoising, sharpening and enhancement schemes and for the selection of optimum value of the arbitrary parameters used in them.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.12.008
  • Classification of tactile event-related potential elicited by Braille
           display for brain–computer interface
    • Authors: Junichi Hori; Naoto Okada
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Junichi Hori, Naoto Okada
      To construct brain–computer interface (BCI), an event-related potential (ERP) induced by a tactile stimulus is investigated in this paper. For ERP-based BCI, visual or auditory information is frequently used as the stimulus. In the present study, we focus on tactile sensations to reserve their visual and auditory senses for other activities. Several patterns of mechanical tactile stimulation were applied to the index fingers of both hands using two piezo actuators that were used as a braille display. Human experiments based on the oddball paradigm were carried out. Subjects were instructed to pay attention to unusual target stimuli while avoiding other frequent non-target stimuli. The extracted features were classified by applying stepwise linear discriminant analysis. As a result, an accuracy of 85% and 60% were obtained for 2- and 4-class classification, respectively. The accuracy was improved by increasing the number of electrodes even when short stimulus intervals were used.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.007
  • Bayesian network aided grasp and grip efficiency estimation using a smart
           data glove for post-stroke diagnosis
    • Authors: Debeshi Dutta; Satyanarayan Modak; Anirudh Kumar; Joydeb Roychowdhury; Soumen Mandal
      Abstract: Publication date: Available online 19 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Debeshi Dutta, Satyanarayan Modak, Anirudh Kumar, Joydeb Roychowdhury, Soumen Mandal
      Stroke is one of the major causes behind the increased mortality rate throughout the world and disability among the survivors. Such disabilities include several grasp and grip related impairment in daily activities like holding a glass of water, counting currency notes, producing correct signature in bank, etc., that seek serious attention. Present therapeutic facilities, being expensive and time-consuming, fail to cater the poverty stricken rural class of the society. In this paper, on the basis of an investigation, we developed a smart data glove based diagnostic device for better treatment of such patients by providing timely estimation of their grasp quality. Data collected from a VMG30 motion capture glove for six patients who survived stroke and two other healthy subjects was fused with suitable hypothesis obtained from a domain expert to reflect the required outcome on a Bayesian network. The end result could be made available to a doctor at a remote location through a smart phone for further advice or treatment. Results obtained clearly distinguished a patient from a healthy subject along with supporting estimates to study and compare different grasping gestures. The improvement in mobility could be assessed after physiotherapeutic treatments using the proposed method.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.005
  • Spatial and spatio-temporal filtering based on common spatial patterns and
           Max-SNR for detection of P300 component
    • Authors: Fereshteh Salimian Rizi; Vahid Abootalebi; Mohamad Taghi Sadeghi
      Abstract: Publication date: Available online 18 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Fereshteh Salimian Rizi, Vahid Abootalebi, Mohamad Taghi Sadeghi
      Recent advances in brain-computer interfaces (BCIs) have developed a new arena for designing systems to help disabled persons to communicate with the surrounding environment. P300 speller is one of the most famous BCI systems choosing the characters from a virtual keyboard through the analysis of EEG signals. P300 detection is an important processing step of these systems. The accuracy of P300 detection highly depends on the feature extraction method. In this study, the maximum signal to noise ratio (Max-SNR) has been used for feature extraction, which rarely applied in this area. This study presents a novel feature extraction technique, named spatio-temporal Max-SNR (ST.Max-SNR). Unlike the standard Max-SNR which only uses spatial patterns of a signal, the proposed method, separately consider the spatial and temporal patterns of the signal to enhance the accuracy of feature extraction. Due to the similarity of the common spatial pattern (CSP) and the Max-SNR algorithms, the performance of this technique and its extension, common Spatio-temporal pattern (CSTP), has been compared with the proposed method. Then, the LDA and SWLDA classifiers are used for classification of the features. Our experimental results show that the Max-SNR based spatio-temporal features lead to an average classification accuracy of 94.4 percent suggesting the best performance.

      PubDate: 2017-01-18T18:16:37Z
      DOI: 10.1016/j.bbe.2016.11.001
  • An analytical method for the adaptive computation of threshold of gradient
           modulus in 2D anisotropic diffusion filter
    • Authors: Justin Joseph; R. Periyasamy
      Abstract: Publication date: Available online 18 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Justin Joseph, R. Periyasamy
      In spite of the extensive application of Anisotropic Diffusion (AD) filter in software packages for medical image analysis, denoising and edge preservation offered by it depends exclusively on the selection of the value of Threshold of Gradient Modulus (TGM). Tuning the TGM to its optimum value through trial and error is subjective and tiring. An analytical model to compute the optimum value of TGM adaptively from the mean gradient of the image itself is proposed in this article. The qualitative examination of the gradient and true edge maps of the original and restored Magnetic Resonance images revealed that analytically computed TGM ensures best trade-off between noise suppression and edge preservation.

      PubDate: 2017-01-18T18:16:37Z
      DOI: 10.1016/j.bbe.2016.12.002
  • Use of siliconised infant endotracheal tubes reduces work of breathing
           under turbulent flow
    • Authors: Barbara Stankiewicz; Marcin Rawicz; Marek Darowski; Krzysztof Zielinski; Maciej Kozarski; Andrzej Chwojnowski
      Abstract: Publication date: Available online 17 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Barbara Stankiewicz, Marcin Rawicz, Marek Darowski, Krzysztof Zielinski, Maciej Kozarski, Andrzej Chwojnowski
      The high resistance of an infant endotracheal tube (ETT) can markedly impair ventilation and gas exchange. Since some manufacturers cover the inner surface of their ETTs with a silicon layer in order to diminish deposition and ease mucous evacuation from airway, via surface roughness decrease, we assessed whether the silicon layer may affect tube resistance, work of breathing and other parameters of ventilation. We compared SUMI (Poland) non-siliconised and siliconised polyvinyl chloride ETTs (2.5, 3.0 and 4.0mm ID), twenty of each type and size combination. Simulating volume-controlled ventilation with the hybrid (numerical–physical) lung models of a premature infant and a 3-month-old baby peak inspiratory pressure (PIP), peak inspiratory and expiratory flow (PIF, PEF), (patient+ETT) inspiratory and expiratory airway resistance (R ins, R exp) and work of breathing by ventilator (WOBvt) were measured. Additionally, images of the both type surfaces were taken using Hitachi TM-1000 electron microscope. When 2.5 and 3.0mm ID ETTs were examined, laminar flow (Re <2300) across the tube was observed, and there were no clinically significant differences in the ventilation parameters between non-siliconised and siliconised tubes. Whereas, when 4mm ID ETTs were tested, turbulent flow was observed, and PIP, R ins, R exp and WOBvt were significantly lower (5%, 17%, 17%, and 7%, respectively) (P <0.05), but PIF and PEF were significantly higher (8%, 14%) (P <0.05). Thus, the silicone inner surface of ETT offers less resistance and WOBvt in presence of turbulent flow. However, artifacts observed on the surface of non-siliconised and siliconised ETTs can potentially impair ventilation.

      PubDate: 2017-01-18T18:16:37Z
      DOI: 10.1016/j.bbe.2016.11.002
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