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Journal Cover Biocybernetics and Biological Engineering
  [SJR: 0.279]   [H-I: 8]   [5 followers]  Follow
   Full-text available via subscription Subscription journal
   ISSN (Print) 0208-5216
   Published by Elsevier Homepage  [3044 journals]
  • Electroencephalography (EEG) signal processing for epilepsy and autism
           spectrum disorder diagnosis
    • Authors: Sutrisno Ibrahim; Ridha Djemal; Abdullah Alsuwailem
      Abstract: Publication date: Available online 14 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Sutrisno Ibrahim, Ridha Djemal, Abdullah Alsuwailem
      Quantification of abnormality in brain signals may reveal brain conditions and pathologies. In this study, we investigate different electroencephalography (EEG) feature extraction and classification techniques to assist in the diagnosis of both epilepsy and autism spectrum disorder (ASD). First, the EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). Two nonlinear methods were studied, namely, Shannon entropy and largest Lyapunov exponent, which measure complexity and chaoticity in the EEG recording, in addition to the two conventional methods (namely, standard deviation and band power). We also study the use of a cross-correlation approach to measure synchronization between EEG channels, which may reveal abnormality in communication between brain regions. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design exploration techniques: the University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and our own EEG recordings (46 subjects). The combination of DWT, Shannon entropy, and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem. The proposed method obtained better classification accuracy compared to the existing methods and tested using larger and more comprehensive EEG dataset. The proposed method could potentially be used to assist epilepsy and ASD diagnosis therefore improving the speed and the accuracy.

      PubDate: 2017-09-18T21:06:35Z
      DOI: 10.1016/j.bbe.2017.08.006
  • Human gait pattern changes detection system: A multimodal vision-based and
           novelty detection learning approach
    • Authors: João Paulo; Alireza Asvadi; Paulo Peixoto; Paula Amorim
      Abstract: Publication date: Available online 14 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): João Paulo, Alireza Asvadi, Paulo Peixoto, Paula Amorim
      This paper proposes a novel gait rehabilitation analysis system, based on an innovative multimodal vision-based sensor setup, focused on detecting gait pattern changes over time. The proposed setup is based on inexpensive technologies, without compromising performance, and was designed to be deployed on walkers, which are a typical assistive aid used in gait rehabilitation. In the medical field the evaluation of a patient's rehabilitation progress is typically performed by a medical professional through subjective techniques based on the professional's visual perception and experience. In this context, we are proposing an automatic system to detect the progress of patients undergoing rehabilitation therapy. Our approach is able to perform novelty detection for gait pattern classification based on One-Class Support Vector Machines (OC-SVM). Using point-cloud and RGB-D data, we detect the lower limbs (waist, legs and feet) by using Weighted Kernel-Density Estimation and Weighted Least-Squares to segment the legs into several parts (thighs and shins), and by fitting 3D ellipsoids to model them. Feet are detected using k-means clustering and a Circular Hough Transform. A temporal analysis of the feet's depth is also performed to detect heel strike events. Spatiotemporal and kinematic features are extracted from the lower limbs’ model and fed to a classifier based on the fusion of several OC-SVMs. Experiments with volunteers using the robotic walker platform ISR-AIWALKER, where the proposed system was deployed, revealed a lower limbs tracking accuracy of 3° and that our novelty detection approach successfully identified novel gait patterns, evidencing an overall 97.89% sensitivity.

      PubDate: 2017-09-18T21:06:35Z
      DOI: 10.1016/j.bbe.2017.08.002
  • Cardiac arrhythmia classification using the phase space sorted by Poincare
    • Authors: Reza Yaghoobi Karimui; Sassan Azadi
      Abstract: Publication date: Available online 8 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Reza Yaghoobi Karimui, Sassan Azadi
      Many methods for automatic heartbeat classification have been applied and reported in literature, but methods, which used the basin geometry of quasi-periodic oscillations of electrocardiogram (ECG) signal in the phase space for classifying cardiac arrhythmias, frequently extracted a limited amount of information of this geometry. Therefore, in this study, we proposed a novel technique based on Poincare section to quantify the basin of quasi-periodic oscillations, which can fill the mentioned gap to some extent. For this purpose, we first reconstructed the two-dimensional phase space of ECG signal. Then, we sorted this space using the Poincare sections in different angles. Finally, we evaluated the geometric features extracted from the sorted spaces of five heartbeat groups recommend by the association for the advancement of medical instrumentation (AAMI) by using the sequential forward selection (SFS) algorithm. The results of this algorithm indicated that a combination of nine features extracted from the sorted phase space along with per and post instantaneous heart rate could significantly separate the five heartbeat groups (99.23% and 96.07% for training and testing sets, respectively). Comparing these results with the results of earlier work also indicated that our proposed method had a figure of merit (FOM) about 32.12%. Therefore, this new technique not only can quantify the basin geometry of quasi-periodic oscillations of ECG signal in the phase space, but also its output can improve the performance of detection systems developed for the cardiac arrhythmias, especially in the five heartbeat groups recommend by the AAMI.

      PubDate: 2017-09-12T20:15:43Z
      DOI: 10.1016/j.bbe.2017.08.005
  • Application of MODWT and log-normal distribution model for automatic
           epilepsy identification
    • Authors: Mingyang Li; Wanzhong Chen; Tao Zhang
      Abstract: Publication date: Available online 5 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Mingyang Li, Wanzhong Chen, Tao Zhang
      In this paper, a novel approach based on the maximal overlap discrete wavelet transform (MODWT) and log-normal distribution (LND) model was proposed for identifying epileptic seizures from electroencephalogram (EEG) signals. To carry out this study, we explored the potentials of MODWT to decompose the signals into time-frequency sub-bands till sixth level. And demodulation analysis (DA) was investigated to reveal the subtle envelope information from the sub-bands. All obtained coefficients are then used to calculate LDN features, scale parameter (σ) and shape parameter (μ), which were fed to a random forest classifier (RFC) for classification. Besides, some experiments have been conducted to evaluate the performance of proposed model in the consideration of various wavelet functions as well as feature extractors. The implementation results demonstrated that our proposed technique has yielded remarkable classification performance for all the concerned problems that outperformed the reported methods in terms of the universality. The major finding of this research is that the proposed technique was capable of classifying EEG segments with satisfied accuracy and clinically acceptable computational time. These advantages have make our technique an attractive diagnostic and monitoring tool, which helps doctors in providing better and timely care for the patients.

      PubDate: 2017-09-06T18:17:47Z
      DOI: 10.1016/j.bbe.2017.08.003
  • Fractal analysis of the grey and binary images in diagnosis of Hashimoto's
    • Authors: Zbigniew Omiotek
      Abstract: Publication date: Available online 3 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Zbigniew Omiotek
      In the study, a fractal analysis of thyroid ultrasound images was applied. This method has not been too often used for testing such kind of images so far. Its advantage is a tool in a form of a fractal dimension, which easily quantifies a complexity of an image texture surface. There is a close relationship between the lesions and an ultrasound image texture in a case of a diffuse form of the Hashimoto's disease. As a result of the analysis, a set of nine fractal descriptors was obtained which made it possible to distinguish healthy cases from sick ones that suffer from the diffuse form of the Hashimoto's thyroiditis. The Hellwig's method for feature selection was utilised. It found the combinations of features of the highest value of the information capacity index. These combinations were applied to build and test five popular classifiers. The following methods were implemented: decision tree, random forests, K-nearest neighbours, linear and quadratic discriminant analysis. The best results were achieved with a combination of three descriptors – fractal dimension and intercept obtained by the power spectral density method and fractal dimension estimated by the box counting method. The LDA (linear discriminant analysis) classifier based on them was characterised by a sensitivity of 96.88%, a specificity at a level of 98.44%, and its overall classification accuracy was equal to 97.66%. These results are similar to the best results of other authors cited in the work where the greyscale image analysis was used.

      PubDate: 2017-09-06T18:17:47Z
      DOI: 10.1016/j.bbe.2017.08.004
  • Lumped models of the cardiovascular system of various complexity
    • Authors: Filip Ježek; Tomáš Kulhánek; Karel Kalecký; Jiri Kofránek
      Abstract: Publication date: Available online 30 August 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Filip Ježek, Tomáš Kulhánek, Karel Kalecký, Jiri Kofránek
      Purpose The main objective is to accelerate the mathematical modeling of complex systems and offer the researchers an accessible and standardized platform for model sharing and reusing. Methods We describe a methodology for creating mathematical lumped models, decomposing a system into basic components represented by elementary physical laws and relationships expressed as equations. Our approach is based on Modelica, an object-oriented, equation-based, visual, non-proprietary modeling language, together with Physiolibrary, an open-source library for the domain of physiology. Results We demonstrate this methodology on an open implementation of a range of simple to complex cardiovascular models, with great complexity variance (simulation time from several seconds to hours). The parts of different complexity could be combined together. Conclusions Thanks to the equation-based nature of Modelica, a hierarchy of subsystems can be built with an appropriate connecting component. Such a structural model follows the concept of the system rather than the computational order. Such a model representation retains structural knowledge, which is important for e.g., model maintainability and reusability of the components and multidisciplinary cooperation with domain experts not familiar with modeling methods.

      PubDate: 2017-08-31T16:56:41Z
      DOI: 10.1016/j.bbe.2017.08.001
  • Fully automatic ROI extraction and edge-based segmentation of radius and
           ulna bones from hand radiographs
    • Authors: Shreyas Simu; Shyam Lal; Pranav Nagarsekar; Amrish Naik
      Abstract: Publication date: Available online 12 August 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Shreyas Simu, Shyam Lal, Pranav Nagarsekar, Amrish Naik
      Bone age is a reliable measure of person's growth and maturation of skeleton. The difference between chronological age and bone age indicates presence of endocrinological problems. The automated bone age assessment system (ABAA) based on Tanner and Whitehouse method (TW3) requires monitoring the growth of radius, ulna and short bones (phalanges) of left hand. In this paper, a detailed analysis of two bones in the bone age assessment system namely, radius and ulna is presented. We propose an automatic extraction method for the region of interest (ROI) of radius and ulna bones from a left hand radiograph (RUROI). We also propose an improved edge-based segmentation technique for those bones. Quantitative and qualitative results of the proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation techniques. Medical experts have also validated the qualitative results of proposed segmentation technique. Experimental results reveal that these proposed techniques provide better segmentation accuracy as compared to the other state-of-the-art segmentation techniques.

      PubDate: 2017-08-21T14:40:18Z
      DOI: 10.1016/j.bbe.2017.07.004
  • Computer assisted classification framework for prediction of acute
           lymphoblastic and acute myeloblastic leukemia
    • Authors: Jyoti Rawat; Annapurna Singh; H.S. Bhadauria; Jitendra Virmani; Jagtar Singh Devgun
      Abstract: Publication date: Available online 25 July 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Jyoti Rawat, Annapurna Singh, H.S. Bhadauria, Jitendra Virmani, Jagtar Singh Devgun
      Hematological malignancies i.e. acute lymphoid leukemia and acute myeloid leukemia are the types of blood cancer that can affect blood, bone marrow, lymphatic system and are the major contributors to cancer deaths. In present work, an attempt has been made to design a CAC (computer aided classification system) for diagnosis of myeloid and lymphoid cells and their FAB (French, American, and British) characterization. The proposed technique improves the AML and ALL diagnostic accuracy by analyzing color, morphological and textural features from the blood image using image processing and to assist in the development of a computer-aided screening of AML and ALL. This paper endeavors at proposing a quantitative microscopic approach toward the discrimination of malignant from normal in stained blood smear. The proposed technique firstly segments the nucleus from the leukocyte cell background and then computes features for each segmented nucleus. A total of 331 geometrical, chromatic and texture features are computed. A genetic algorithm using support vector machine (SVM) classifier is used to optimize the feature space. Based on optimized feature space, an SVM classifier with various kernel functions is used to eradicate noisy objects like overlapped cells, stain fragments, and other kinds of background noises. The significance of the proposed method is tested using 331 features on 420 microscopic blood images acquired from the online repository provided by the American society of hematology. The results confirmed the viability or potential of using a computer aided classification method to reinstate the monotonous and the reader-dependent diagnostic methods.

      PubDate: 2017-07-31T11:49:05Z
      DOI: 10.1016/j.bbe.2017.07.003
  • Determining the shift of a bronchoscope catheter from the analysis of a
           video sequence of a bronchoscope video camera
    • Authors: Dariusz Michalski; Zbisław Tabor; Bartosz Zieliński
      Abstract: Publication date: Available online 24 July 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Dariusz Michalski, Zbisław Tabor, Bartosz Zieliński
      In this study we have proposed an algorithm for automated monitoring of the movements of a catheter used in peripheral bronchoscopy examination. We have shown that the shift of the catheter can be controlled in an automated way with quite a good accuracy by the means of analysis of video sequence recorded by a video camera of a bronchoscope. For a catheter moving between successive frames by no more than 1/3 of the distance between successive markers associated with a catheter the accuracy of a catheter shift measurement was equal to 1% and for a catheter moving between successive frames by no more than 1/2 of the distance between successive markers associated with a catheter the accuracy of a catheter shift measurement was equal to 5%. Visual inspection proved that the observed measurement errors were associated with faster movements of a catheter. Bronchoscope redesign option is proposed to improve catheter shift measurement accuracy. The results of this study demonstrate that application of image analysis techniques to data recorded during bronchoscopy examination can at least support the existing navigation methods for peripheral bronchoscopy with respect to the determination of the location of the catheter distal tip within the lumen of the pulmonary airways.

      PubDate: 2017-07-31T11:49:05Z
      DOI: 10.1016/j.bbe.2017.07.002
  • A rapid algebraic 3D volume image reconstruction technique for Cone Beam
           Computed Tomography
    • Authors: Mohammed A. Al-masni; Mugahed A. Al-antari; Mohamed K. Metwally; Yasser M. Kadah; Seung-Moo Han; Tae-Seong Kim
      Abstract: Publication date: Available online 11 July 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Mohammed A. Al-masni, Mugahed A. Al-antari, Mohamed K. Metwally, Yasser M. Kadah, Seung-Moo Han, Tae-Seong Kim
      Computed Tomography (CT) is a widely used imaging technique in medical diagnosis. Among the latest advances in CT imaging techniques, the use of cone-beam X-ray projections, instead of the usual planar fan beam, promises faster yet safer 3D imaging in comparison to the previous CT imaging methodologies. This technique is called Cone Beam CT (CBCT). However, these advantages come at the expense of a more challenging 3D reconstruction problem that is still an active research area to improve the speed and quality of image reconstruction. In this paper, we present an implementation of rapid parallel Multiplicative Algebraic Reconstruction Technique (rpMART) for CBCT which gives more accurate and faster reconstruction even with a lower number of projections via parallel computing. We have compared rpMART with the parallel version of Algebraic Reconstruction Technique (pART) and the conventional non-parallel versions of npART, npMART and Feldkamp, Davis, and Kress (npFDK) techniques. The results indicate that the reconstructed volume images from rpMART provide a higher image quality index of 0.99 than the indices of pART and npFDK of 0.80 and 0.39, respectively. Also the proposed implementation of rpMART and pART via parallel computing significantly reduce the reconstruction time from more than 6h with npART and npMART to 580 and 560s with the full 360° projections data, respectively. We consider that rpMART could be a better image reconstruction technique for CBCT in clinical applications instead of the widely used FDK method.

      PubDate: 2017-07-14T15:31:25Z
      DOI: 10.1016/j.bbe.2017.07.001
  • Denoising of ECG signal by non-local estimation of approximation
           coefficients in DWT
    • Authors: Pratik; Gayadhar Pradhan; S. Shahnawazuddin
      Abstract: Publication date: Available online 27 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Pratik, Gayadhar Pradhan, S. Shahnawazuddin
      This paper presents an ECG denoising technique using merits of discrete wavelet transform (DWT) and non-local means (NLM) estimation. The NLM-based approach is quite effective in removing low frequency noises but it suffers from the issue of under-averaging in the high-frequency QRS-complex region. In addition to that, the computational cost of NLM estimation is also high. The DWT, on the other hand, is effective in removing high-frequency noise but needs larger decomposition levels in order to denoise the low-frequency components. Thresholding lower-frequency components in the DWT domain often results in a loss of critical information. To overcome these drawbacks, in the proposed method, two-level DWT decomposition is first performed in order to decompose the noisy ECG signal into low- and high-frequency approximation and detail coefficients, respectively, at each level. The high frequency noise is removed by thresholding the detail coefficients at both the levels. The noise in the lower-frequency region is then removed by performing NLM estimation of Level 2 approximation coefficient. The Level 2 approximation coefficients actually represent the low-frequency envelope of the ECG. Thus, the proposed technique effectively combines the power of both NLM and DWT. At the same time, the computational cost of whole process is not more than the earlier existing techniques since NLM estimation is performed only on Level 2 approximation coefficients instead of the complete ECG signal. The proposed method is found to be superior to the existing state-of-the-art techniques when tested on the MIT-BIH arrhythmia database.

      PubDate: 2017-07-05T13:57:00Z
      DOI: 10.1016/j.bbe.2017.06.001
  • A description of hand matrices to extract various characteristics of human
           hand in three-dimensional space
    • Authors: Tsutomu Sekine; Shun Hibino; Yuya Nakamura
      Abstract: Publication date: Available online 20 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Tsutomu Sekine, Shun Hibino, Yuya Nakamura
      This study focuses on a description of hand matrices to extract various characteristics of human hand in three-dimensional space. A mathematical expression for human hand has scarcely been proposed so far, and the practical, versatile description has been required to analyze a gesture behavior in detail. In this study, the bones and joints of human hand were explained supplementarily. After that, a CG model of human hand was created according to the anatomical structure. With reference to the model's structure, hand matrices were proposed to investigate poses, positions, and postural orientations of human hand in a uniform manner. The several examples were also discussed with appropriate illustrations. As a result, the characteristics of hand matrices were revealed in practically-possible cases; moreover, the mathematical treatments were theoretically versatile and simple to find a difference or common feature of hand motion in three-dimensional space.

      PubDate: 2017-06-23T15:52:38Z
      DOI: 10.1016/j.bbe.2017.05.003
  • Pure intrusion of a mandibular canine with segmented arch in lingual
           orthodontics: A numerical study with 3-dimensional finite element analysis
    • Authors: Abhishek Thote; Krishna Sharma Rashmi Uddanwadiker Sunita Shrivastava
      Abstract: Publication date: Available online 16 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Abhishek M. Thote, Krishna Sharma, Rashmi V. Uddanwadiker, Sunita Shrivastava
      Objective Approximately 50% patients with a deep bite possess anatomically extruded mandibular canines. The objective of this study was to specify the required toe (θ) of the vertical segment of a cantilever from the distal aspect to achieve pure intrusion of a mandibular canine with a segmented arch in lingual orthodontics. Additionally, the optimum magnitude of the required intrusive force by a cantilever was determined assuming non-linear, hyper-elastic behaviour of periodontal ligament (PDL). Methods The geometrical model of a mandibular canine tooth was developed and the mathematical equation was devised to evaluate θ (positive value: toe-in, negative value: toe-out) based on certain input parameters. To verify this numerical study by finite element analysis (FEA), total eight different positions of point of force application (P f) on bracket top (occlusal) surface were considered based on different values of input parameters. Results The results were displayed in terms of nature of tooth movement and Von-Mises (equivalent) stresses generated in the PDL. Additionally, the optimum magnitude of the required intrusive force within the biological limit of a mandibular canine was determined from FEA considering the strength of PDL and factor of safety. Conclusions The numerical study was developed to compute the value of required toe angle (θ) of the vertical segment of a cantilever for different morphologies of a mandibular canine as well as different positions of Pf. From FEA, the optimum range of an intrusive force within the biological limit of a mandibular canine was found to be 20–30g.

      PubDate: 2017-06-18T15:30:08Z
  • Improving the accuracy of detecting steroid abuse in cattle by pairwise
           learning of serum samples
    • Authors: Xiao-Lei Xia
      Abstract: Publication date: Available online 7 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Xiao-Lei Xia

      PubDate: 2017-06-08T16:31:01Z
      DOI: 10.1016/j.bbe.2017.05.009
  • Development of a real time emotion classifier based on evoked EEG
    • Authors: Moon Inder Singh; Mandeep Singh
      Abstract: Publication date: Available online 1 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Moon Inder Singh, Mandeep Singh
      Our quality of life is more dependent on our emotions than on physical comforts alone. This is motivation enough to classify emotions using Electroencephalogram (EEG) signals. This paper describes the acquisition of evoked EEG signals for classification of emotions into four quadrants. The EEG signals have been collected from 24 subjects on three electrodes (Fz, Cz and Pz) along the central line. The absolute and differential attributes of single trial ERPs have been used to classify emotions. The single trial ERP attributes collected from each electrode have been used for developing an emotion classifier for each subject. The accuracy of classification of emotions into four classes lies between 62.5–83.3% for single trials. The subject independent analysis has been done using absolute and differential attributes of single trial signals of ERP. An overall accuracy of 55% has been obtained on Fz electrode for multi subject trials. The methodology used to classify emotions by fixing the attributes for classification of emotions brings us a step closer to developing a real time emotion recognition system with benefits including applications like Brain-Computer Interface for locked-in subjects, emotion classification for highly sensitive jobs like fighter pilots etc.

      PubDate: 2017-06-04T15:49:15Z
      DOI: 10.1016/j.bbe.2017.05.004
  • Robust and accurate optic disk localization using vessel symmetry line
           measure in fundus images
    • Authors: Rashmi Panda; N.B. Puhan; Ganapati Panda
      Abstract: Publication date: Available online 31 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Rashmi Panda, N.B. Puhan, Ganapati Panda
      Accurate optic disk (OD) localization is an important step in fundus image based computer-aided diagnosis of glaucoma and diabetic retinopathy. Robust OD localization becomes more challenging with the presence of common pathological variations which could alter its overall appearance. This paper presents a novel OD localization method by incorporating salient visual cues of retinal vasculature: (1) global vessel symmetry, (2) vessel component count and (3) local vessel symmetry inside OD region. In the proposed method, a new vessel symmetry line (VSL) measure is designed to demarcate the lines that divide the retinal vasculature into approximately similar halves. The initial OD center location is computed using the highest number of major blood vessel components in the skeleton image. The final OD center involves an iterative center of mass computation to exploit the local vessel symmetry in the OD region of interest. The proposed method shows effectiveness in diseased retinas having diverse symptoms like bright lesions, hemorrhages, and tortuous vessels that create potential ambiguity for OD localization. A total of ten publicly available retinal image databases are considered for extensive evaluation of the proposed method. The experimental results demonstrate high average OD detection accuracy of 99.49%, while achieving state-of-the-art OD localization error in all databases.

      PubDate: 2017-06-04T15:49:15Z
      DOI: 10.1016/j.bbe.2017.05.008
  • Blood flows in end-to-end arteriovenous fistulas: Unsteady and steady
           state numerical investigations of three patient-specific cases
    • Authors: Daniel Jodko; Damian Obidowski; Piotr Reorowicz; Krzysztof Jóźwik
      Abstract: Publication date: Available online 30 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Daniel Jodko, Damian Obidowski, Piotr Reorowicz, Krzysztof Jóźwik

      PubDate: 2017-06-04T15:49:15Z
      DOI: 10.1016/j.bbe.2017.05.006
  • Construction of a bilirubin biosensor based on an albumin-immobilized
           quartz crystal microbalance
    • Authors: Mustafa Kocakulak; Tuncay Bayrak; Sinan Saglam
      Abstract: Publication date: Available online 28 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Mustafa Kocakulak, Tuncay Bayrak, Sinan Saglam
      Bilirubin is a bile pigment that is produced by hemoglobin catabolism in old erythrocytes in mammals. Bilirubin accumulates in the brain tissue, where it is toxic. Bilirubin is associated with several diseases such as Gilbert's syndrome, Dubin–Johnson syndrome and other systemic pathologies. For this reason, it is very important to measure the bilirubin levels in the human body. To treat hyperbilirubinemia patients, who have high bilirubin levels, extracorporeal bilirubin removal columns are applied. The measurement of bilirubin is important for diagnosis and therapy, and it is usually measured by nonchemical photometric devices, skin test devices and laboratory analyzers. In this study, a new bilirubin biosensor using quartz crystal microbalances immobilized with albumin is proposed. To measure the effectiveness of the biosensor, a series of experiments was realized with various concentrations of bilirubin, including 1mg/ml (1.71mmol/L), 2mg/ml (3.42mmol/L), 5mg/ml (8.55mmol/L) and 10mg/ml(17.1mmol/L). Comparing gas analyzers, laboratory analyzers, skin test devices and nonchemical photometric devices, skin test devices could be used up to 200μmol/L and nonchemical photometric devices could be evaluated as reliable up to 250μmol/L. The low limit range of the bilirubin detection is between 1.7μmol/L and 2.5μmol/L for costly commercial bilirubin measurement devices. Nevertheless, this study presents measurements with a high sensitivity and includes the advantage of reusability by using cheaper materials. In the light of this study, more sensitive biosensor could be developed to detection bilirubin level in the human blood instead of current commercial products. In addition, atomic force microscopy (AFM) was used to prove albumin immobilization and the bilirubin-albumin interaction, and a good correlation was obtained from AFM images. As a result, a low cost and more sensitive bilirubin measurement device is implemented. In conclusion, an effective and reusable bilirubin biosensor could be developed with albumin immobilization.

      PubDate: 2017-05-29T15:03:41Z
      DOI: 10.1016/j.bbe.2017.05.007
  • 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
  • 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
  • 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
  • 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
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