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BIOTECHNOLOGY (237 journals)                  1 2 | Last

Showing 1 - 200 of 237 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 8)
Advanced Biomedical Research     Open Access  
Advances in Bioscience and Biotechnology     Open Access   (Followers: 14)
Advances in Genetic Engineering & Biotechnology     Hybrid Journal   (Followers: 8)
African Journal of Biotechnology     Open Access   (Followers: 6)
Algal Research     Partially Free   (Followers: 10)
American Journal of Biochemistry and Biotechnology     Open Access   (Followers: 64)
American Journal of Bioinformatics Research     Open Access   (Followers: 7)
American Journal of Polymer Science     Open Access   (Followers: 31)
Anadolu University Journal of Science and Technology : C Life Sciences and Biotechnology     Open Access  
Animal Biotechnology     Hybrid Journal   (Followers: 8)
Annales des Sciences Agronomiques     Full-text available via subscription  
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 43)
Applied Bioenergy     Open Access  
Applied Biosafety     Hybrid Journal  
Applied Food Biotechnology     Open Access   (Followers: 3)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 63)
Applied Mycology and Biotechnology     Full-text available via subscription   (Followers: 4)
Arthroplasty Today     Open Access   (Followers: 1)
Artificial Cells, Nanomedicine and Biotechnology     Hybrid Journal   (Followers: 1)
Asia Pacific Biotech News     Hybrid Journal   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Pacific Journal of Tropical Biomedicine     Open Access   (Followers: 2)
Australasian Biotechnology     Full-text available via subscription   (Followers: 1)
Banat's Journal of Biotechnology     Open Access  
BBR : Biochemistry and Biotechnology Reports     Open Access   (Followers: 5)
Bio-Algorithms and Med-Systems     Hybrid Journal   (Followers: 2)
Bio-Research     Full-text available via subscription   (Followers: 2)
Bioactive Materials     Open Access   (Followers: 1)
Biocatalysis and Agricultural Biotechnology     Hybrid Journal   (Followers: 4)
Biocybernetics and Biological Engineering     Full-text available via subscription   (Followers: 5)
Bioethics UPdate     Hybrid Journal  
Biofuels     Hybrid Journal   (Followers: 11)
Biofuels Engineering     Open Access   (Followers: 1)
Biological & Pharmaceutical Bulletin     Full-text available via subscription   (Followers: 4)
Biological Cybernetics     Hybrid Journal   (Followers: 10)
Biomarkers and Genomic Medicine     Open Access   (Followers: 3)
Biomarkers in Drug Development     Partially Free   (Followers: 1)
Biomaterials Research     Open Access   (Followers: 4)
BioMed Research International     Open Access   (Followers: 4)
Biomédica     Open Access  
Biomedical and Biotechnology Research Journal     Open Access  
Biomedical Engineering Research     Open Access   (Followers: 6)
Biomedical glasses     Open Access  
Biomedical Reports     Full-text available via subscription  
BioMedicine     Open Access  
Biomedika     Open Access  
Bioprinting     Hybrid Journal   (Followers: 1)
Bioresource Technology Reports     Hybrid Journal   (Followers: 1)
Bioscience, Biotechnology, and Biochemistry     Hybrid Journal   (Followers: 21)
Biosimilars     Open Access   (Followers: 1)
Biosurface and Biotribology     Open Access  
Biotechnic and Histochemistry     Hybrid Journal   (Followers: 2)
BioTechniques : The International Journal of Life Science Methods     Full-text available via subscription   (Followers: 28)
Biotechnologia Acta     Open Access   (Followers: 1)
Biotechnologie, Agronomie, Société et Environnement     Open Access   (Followers: 2)
Biotechnology     Open Access   (Followers: 5)
Biotechnology & Biotechnological Equipment     Open Access   (Followers: 4)
Biotechnology Advances     Hybrid Journal   (Followers: 33)
Biotechnology and Applied Biochemistry     Hybrid Journal   (Followers: 44)
Biotechnology and Bioengineering     Hybrid Journal   (Followers: 155)
Biotechnology and Bioprocess Engineering     Hybrid Journal   (Followers: 5)
Biotechnology and Genetic Engineering Reviews     Hybrid Journal   (Followers: 13)
Biotechnology and Health Sciences     Open Access   (Followers: 1)
Biotechnology and Molecular Biology Reviews     Open Access   (Followers: 1)
Biotechnology Annual Review     Full-text available via subscription   (Followers: 5)
Biotechnology for Biofuels     Open Access   (Followers: 10)
Biotechnology Frontier     Open Access   (Followers: 2)
Biotechnology Journal     Hybrid Journal   (Followers: 16)
Biotechnology Law Report     Hybrid Journal   (Followers: 4)
Biotechnology Letters     Hybrid Journal   (Followers: 34)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Biotechnology Reports     Open Access  
Biotechnology Research International     Open Access   (Followers: 1)
Biotechnology Techniques     Hybrid Journal   (Followers: 10)
Biotecnología Aplicada     Open Access  
Bioteknologi (Biotechnological Studies)     Open Access  
Biotribology     Hybrid Journal   (Followers: 1)
BMC Biotechnology     Open Access   (Followers: 16)
Cell Biology and Development     Open Access  
Chinese Journal of Agricultural Biotechnology     Full-text available via subscription   (Followers: 4)
Communications in Mathematical Biology and Neuroscience     Open Access  
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computer Methods and Programs in Biomedicine     Hybrid Journal   (Followers: 8)
Contributions to Tobacco Research     Open Access   (Followers: 2)
Copernican Letters     Open Access   (Followers: 1)
Critical Reviews in Biotechnology     Hybrid Journal   (Followers: 20)
Crop Breeding and Applied Biotechnology     Open Access   (Followers: 3)
Current Bionanotechnology     Hybrid Journal  
Current Biotechnology     Hybrid Journal   (Followers: 4)
Current Opinion in Biomedical Engineering     Hybrid Journal   (Followers: 1)
Current Opinion in Biotechnology     Hybrid Journal   (Followers: 56)
Current Pharmaceutical Biotechnology     Hybrid Journal   (Followers: 9)
Current Research in Bioinformatics     Open Access   (Followers: 12)
Current Trends in Biotechnology and Chemical Research     Open Access   (Followers: 3)
Current trends in Biotechnology and Pharmacy     Open Access   (Followers: 8)
EBioMedicine     Open Access  
Electronic Journal of Biotechnology     Open Access  
Entomologia Generalis     Full-text available via subscription  
Environmental Science : Processes & Impacts     Full-text available via subscription   (Followers: 4)
Experimental Biology and Medicine     Hybrid Journal   (Followers: 3)
Folia Medica Indonesiana     Open Access  
Food Bioscience     Hybrid Journal  
Food Biotechnology     Hybrid Journal   (Followers: 9)
Food Science and Biotechnology     Hybrid Journal   (Followers: 8)
Frontiers in Bioengineering and Biotechnology     Open Access   (Followers: 6)
Frontiers in Systems Biology     Open Access   (Followers: 2)
Fungal Biology and Biotechnology     Open Access   (Followers: 2)
GM Crops and Food: Biotechnology in Agriculture and the Food Chain     Full-text available via subscription   (Followers: 1)
GSTF Journal of BioSciences     Open Access  
HAYATI Journal of Biosciences     Open Access  
Horticulture, Environment, and Biotechnology     Hybrid Journal   (Followers: 11)
IEEE Transactions on Molecular, Biological and Multi-Scale Communications     Hybrid Journal   (Followers: 1)
IET Nanobiotechnology     Hybrid Journal   (Followers: 2)
IIOAB Letters     Open Access  
IN VIVO     Full-text available via subscription   (Followers: 4)
Indian Journal of Biotechnology (IJBT)     Open Access   (Followers: 2)
Indonesia Journal of Biomedical Science     Open Access   (Followers: 2)
Indonesian Journal of Biotechnology     Open Access   (Followers: 1)
Industrial Biotechnology     Hybrid Journal   (Followers: 18)
International Biomechanics     Open Access  
International Journal of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 13)
International Journal of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 4)
International Journal of Biomedical Research     Open Access   (Followers: 2)
International Journal of Biotechnology     Hybrid Journal   (Followers: 5)
International Journal of Biotechnology and Molecular Biology Research     Open Access   (Followers: 2)
International Journal of Biotechnology for Wellness Industries     Partially Free   (Followers: 1)
International Journal of Environment, Agriculture and Biotechnology     Open Access   (Followers: 5)
International Journal of Functional Informatics and Personalised Medicine     Hybrid Journal   (Followers: 4)
International Journal of Medicine and Biomedical Research     Open Access   (Followers: 1)
International Journal of Nanotechnology and Molecular Computation     Full-text available via subscription   (Followers: 3)
International Journal of Radiation Biology     Hybrid Journal   (Followers: 4)
Iranian Journal of Biotechnology     Open Access  
ISABB Journal of Biotechnology and Bioinformatics     Open Access  
Italian Journal of Food Science     Open Access   (Followers: 1)
Journal of Biometrics & Biostatistics     Open Access   (Followers: 3)
Journal of Bioterrorism & Biodefense     Open Access   (Followers: 6)
Journal of Petroleum & Environmental Biotechnology     Open Access   (Followers: 1)
Journal of Advanced Therapies and Medical Innovation Sciences     Open Access  
Journal of Advances in Biotechnology     Open Access   (Followers: 5)
Journal Of Agrobiotechnology     Open Access  
Journal of Analytical & Bioanalytical Techniques     Open Access   (Followers: 7)
Journal of Animal Science and Biotechnology     Open Access   (Followers: 4)
Journal of Applied Biomedicine     Open Access   (Followers: 2)
Journal of Applied Biotechnology     Open Access   (Followers: 2)
Journal of Applied Biotechnology Reports     Open Access   (Followers: 2)
Journal of Applied Mathematics & Bioinformatics     Open Access   (Followers: 5)
Journal of Biologically Active Products from Nature     Hybrid Journal   (Followers: 1)
Journal of Biomaterials and Nanobiotechnology     Open Access   (Followers: 6)
Journal of Biomedical Photonics & Engineering     Open Access  
Journal of Biomedical Practitioners     Open Access  
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription  
Journal of Bioprocessing & Biotechniques     Open Access  
Journal of Biosecurity, Biosafety and Biodefense Law     Hybrid Journal   (Followers: 3)
Journal of Biotechnology     Hybrid Journal   (Followers: 68)
Journal of Biotechnology and Strategic Health Research     Open Access  
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 9)
Journal of Chitin and Chitosan Science     Full-text available via subscription  
Journal of Colloid Science and Biotechnology     Full-text available via subscription  
Journal of Commercial Biotechnology     Full-text available via subscription   (Followers: 6)
Journal of Crop Science and Biotechnology     Hybrid Journal   (Followers: 3)
Journal of Essential Oil Research     Hybrid Journal   (Followers: 2)
Journal of Experimental Biology     Full-text available via subscription   (Followers: 24)
Journal of Genetic Engineering and Biotechnology     Open Access   (Followers: 5)
Journal of Ginseng Research     Open Access  
Journal of Industrial Microbiology and Biotechnology     Hybrid Journal   (Followers: 16)
Journal of Integrative Bioinformatics     Open Access  
Journal of International Biotechnology Law     Hybrid Journal   (Followers: 3)
Journal of Medical Imaging and Health Informatics     Full-text available via subscription  
Journal of Molecular Biology and Biotechnology     Open Access  
Journal of Molecular Microbiology and Biotechnology     Full-text available via subscription   (Followers: 11)
Journal of Nano Education     Full-text available via subscription  
Journal of Nanobiotechnology     Open Access   (Followers: 4)
Journal of Nanofluids     Full-text available via subscription   (Followers: 1)
Journal of Organic and Biomolecular Simulations     Open Access  
Journal of Plant Biochemistry and Biotechnology     Hybrid Journal   (Followers: 4)
Journal of Science and Applications : Biomedicine     Open Access  
Journal of the Mechanical Behavior of Biomedical Materials     Hybrid Journal   (Followers: 11)
Journal of Trace Elements in Medicine and Biology     Hybrid Journal   (Followers: 1)
Journal of Tropical Microbiology and Biotechnology     Full-text available via subscription  
Journal of Yeast and Fungal Research     Open Access   (Followers: 1)
Marine Biotechnology     Hybrid Journal   (Followers: 4)
Messenger     Full-text available via subscription  
Metabolic Engineering Communications     Open Access   (Followers: 4)
Metalloproteinases In Medicine     Open Access  
Microalgae Biotechnology     Open Access   (Followers: 2)
Microbial Biotechnology     Open Access   (Followers: 9)
MicroMedicine     Open Access   (Followers: 3)
Molecular and Cellular Biomedical Sciences     Open Access  
Molecular Biotechnology     Hybrid Journal   (Followers: 13)
Molecular Genetics and Metabolism Reports     Open Access   (Followers: 3)
Nanobiomedicine     Open Access  
Nanobiotechnology     Hybrid Journal   (Followers: 2)
Nanomaterials and Nanotechnology     Open Access  
Nanomaterials and Tissue Regeneration     Open Access  
Nanomedicine and Nanobiology     Full-text available via subscription  
Nanomedicine Research Journal     Open Access  
Nanotechnology Reviews     Hybrid Journal   (Followers: 5)
Nature Biotechnology     Full-text available via subscription   (Followers: 535)

        1 2 | Last

Journal Cover Biocybernetics and Biological Engineering
  Journal Prestige (SJR): 0.279
  Citation Impact (citeScore): 8
  Number of Followers: 5  
   Full-text available via subscription Subscription journal
   ISSN (Print) 0208-5216
   Published by Elsevier Homepage  [3162 journals]
  • A segment-wise reconstruction method based on bidirectional long short
           term memory for Power Line Interference suppression
    • Authors: Yue Qiu; Kejie Huang; Feng Xiao; Haibin Shen
      Pages: 217 - 227
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 2
      Author(s): Yue Qiu, Kejie Huang, Feng Xiao, Haibin Shen
      The overlap between the signal components of Power Line Interference (PLI) and biomedical signals in the frequency domain makes the filtered results prone to severe distortion. Electrocardiogram (ECG) is a type of biomedical electronic signal used for cardiac diagnosis. The objective of this work is to suppress the PLI components from biomedical signals with minimal distortion, and the object of study is mainly the ECG signals. In this study, we propose a novel segment-wise reconstruction method to suppress the PLI in biomedical signals based on the Bidirectional Recurrent Neural Networks with Long Short Term Memory (Bi-LSTM). Experiments are conducted on both synthetic and real signals, and quantitative comparisons are made with a traditional IIR notch filter and two state-of-the-art methods in the literature. The results show that by our method, the output Signal-to-Noise Ratio (SNR) is improved by more than 7dB and the settling time for step response is reduced to 0.09s on average. The results also demonstrate that our method has enough generalization ability for unforeseen signals without retraining.

      PubDate: 2018-02-26T21:34:15Z
      DOI: 10.1016/j.bbe.2018.01.003
  • Bayesian HCS-based multi-SVNN: A classification approach for brain tumor
           segmentation and classification using Bayesian fuzzy clustering
    • Abstract: Publication date: Available online 1 June 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): A. Ratna Raju, P. Suresh, R. Rajeswara Rao
      Brain tumor segmentation and classification is the interesting area for differentiating the tumerous and the non-tumerous cells in the brain and to classify the tumerous cells for identifying its level. The conventional methods lack the automatic classification and they consumed huge time and are ineffective in decision-making. To overcome the challenges faced by the conventional methods, this paper proposes the automatic method of classification using the Harmony-Crow Search (HCS) Optimization algorithm to train the multi-SVNN classifier. The brain tumor segmentation is performed using the Bayesian fuzzy clustering approach, whereas the tumor classification is done using the proposed HCS Optimization algorithm-based multi-SVNN classifier. The proposed method of classification determines the level of the brain tumor using the features of the segments generated based on Bayesian fuzzy clustering. The robust features are obtained using the information theoretic measures, scattering transform, and wavelet transform. The experimentation performed using the BRATS database conveys proves the effectiveness of the proposed method and the proposed HCS-based tumor segmentation and classification achieves the classification accuracy of 0.93 and outperforms the existing segmentation methods.

      PubDate: 2018-06-04T08:03:27Z
  • Representation learning-based unsupervised domain adaptation for
           classification of breast cancer histopathology images
    • Abstract: Publication date: Available online 30 May 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Pendar Alirezazadeh, Behzad Hejrati, Alireza Monsef-Esfehani, Abdolhossein Fathi
      Breast cancer has high incidence rate compared to the other cancers among women. This disease leads to die if it does not diagnosis early. Fortunately, by means of modern imaging procedure such as MRI, mammography, thermography, etc., and computer systems, it is possible to diagnose all kind of breast cancers in a short time. One type of BC images is histology images. They are obtained from the entire cut-off texture by use of digital cameras and contain invaluable information to diagnose malignant and benign lesions. Recently by requesting to use the digital workflow in surgical pathology, the diagnosis based on whole slide microscopy image analysis has attracted the attention of many researchers in medical image processing. Computer aided diagnosis (CAD) systems are developed to help pathologist make a better decision. There are some weaknesses in histology images based CAD systems in compared with radiology images based CAD systems. As these images are collected in different laboratory stages and from different samples, they have different distributions leading to mismatch of training (source) domain and test (target) domain. On the other hand, there is the great similarity between images of benign tumors with those of malignant. So if these images are analyzed undiscriminating, this leads to decrease classifier performance and recognition rate. In this research, a new representation learning-based unsupervised domain adaptation method is proposed to overcome these problems. This method attempts to distinguish benign extracted feature vectors from those of malignant ones by learning a domain invariant space as much as possible. This method achieved the average classification rate of 88.5% on BreaKHis dataset and increased 5.1% classification rate compared with basic methods and 1.25% with state-of-art methods.

      PubDate: 2018-06-01T07:30:12Z
  • Object detection based on deep learning for urine sediment examination
    • Abstract: Publication date: Available online 24 May 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Yixiong Liang, Zhihong Tang, Meng Yan, Jianfeng Liu
      Urine sediment examination (USE) is an important topic in kidney disease analysis and it is often the prerequisite for subsequent diagnostic procedures. We propose DFPN(Feature Pyramid Network with DenseNet) method to overcome the problem of class confusion in the USE images that it is hard to be solved by baseline model which is the state-of-the-art object detection model FPN with RoIAlign pooling. We explored the importance of two parts of baseline model for the USE cell detection. First, adding attention module in the network head, and the class-specific attention module has improved mAP by 0.7 points with pre-trained ImageNet model and 1.4 points with pre-trained COCO model. Next, we introduced DenseNet to the baseline model(DFPN) for cell detection in USE, so that the input of the network's head own multiple levels of semantic information, compared to the baseline model only has high-level semantic information. DFPN achieves top result with a mAP of 86.9% on USE test set after balancing between the classification loss and bounding-box regression loss, which improve 5.6 points compared to baseline model, and especially erythrocyte's AP is greatly improved from 65.4% to 93.8%, indicating class confusion has been basically resolved. And we also explore the impacts of training schedule and pre-trained model. Our method is promising for the development of automated USE.

      PubDate: 2018-05-29T07:24:47Z
  • Time–frequency analysis in infant cry classification using quadratic
           time frequency distributions
    • Abstract: Publication date: Available online 18 May 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): J. Saraswathy, M. Hariharan, Wan Khairunizam, J. Sarojini, N. Thiyagar, Y. Sazali, Shafriza Nisha
      This paper presents a new investigation of time–frequency (t–f) based signal processing approach using quadratic time–frequency distributions (QTFDs) namely spectrogram (SPEC), Wigner–Ville distribution (WVD), Smoothed–Wigner Ville distribution (SWVD), Choi–William distribution (CWD) and modified B-distribution (MBD) for classification of infant cry signals. t–f approaches have proved as an efficient approach for applications involving the non stationary signals. In feature extraction, a cluster of t–f based features were extracted by extending the time-domain and frequency-domain features to the joint t–f domain from the generated t–f representation. Conventional features such as mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs) were also extracted in order to compare the effectiveness of the t–f methods. The efficacy of the extracted feature vectors was validated using probabilistic neural network (PNN) and general regression neural network (GRNN). The proposed methodology was implemented to classify different sets of binary classification problems of infant cry signals from different native. The best empirical result of above 90% was reported and revealed the good potential of t–f methods in the context of infant cry classification.

      PubDate: 2018-05-29T07:24:47Z
  • Control of speed and direction of electric wheelchair using seat pressure
    • Abstract: Publication date: Available online 5 May 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Junichi Hori, Hiroki Ohara, Seiya Inayoshi
      An electric wheelchair controlled through seat pressure mapping was developed to accomplish hands-free operation. The seat pressure mapping resulting from a change in posture was measured using a pressure sensor array seated on the wheelchair in real time. The movements of the upper body were discriminated using template matching. The speed and direction can be controlled based on the similarities between the measured pressure distribution and five templates of neutral, forward, backward, left, and right movements. The developed interface was built into a commercial electric wheelchair. As the results of an experiment show, the proposed wheelchair can be controlled in any direction and velocity.

      PubDate: 2018-05-29T07:24:47Z
  • A robust pre-processing of BeadChip microarray images
    • Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 3
      Author(s): Jan Kalina
      Microarray images commonly used in gene expression studies are heavily contaminated by noise and/or outlying values (outliers). Unfortunately, standard methodology for the analysis of Illumina BeadChip microarray images turns out to be too vulnerable to data contamination by outliers. In this paper, an alternative approach to low-level pre-processing of images obtained by the BeadChip microarray technology is proposed. The novel approach robustifies the standard methodology in a complex way and thus ensures a sufficient robustness (resistance) to outliers. A gene expression data set from a cardiovascular genetic study is analyzed and the performance of the novel robust approach is compared with the standard methodology. The robust approach is able to detect and delete a larger percentage of outliers. More importantly, gene expressions are estimated more precisely. As a consequence, also the performance of a subsequently performed classification task to two groups (patients vs. control persons) is improved over the cardiovascular gene expression data set. A further improvement was obtained when considering weighted gene expression values, where the weights correspond to a robust estimate of variability of the measurements for each individual gene transcript.

      PubDate: 2018-05-29T07:24:47Z
  • Automated diagnosis of atrial fibrillation ECG signals using entropy
           features extracted from flexible analytic wavelet transform
    • Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 3
      Author(s): Mohit Kumar, Ram Bilas Pachori, U. Rajendra Acharya
      Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The electrocardiogram (ECG) signals are widely used to diagnose the AF. Automated diagnosis of AF can aid the clinicians to make a more accurate diagnosis. Hence, in this work, we have proposed a decision support system for AF using a novel nonlinear approach based on flexible analytic wavelet transform (FAWT). First, we have extracted 1000 ECG samples from the long duration ECG signals. Then, log energy entropy (LEE), and permutation entropy (PEn) are computed from the sub-band signals obtained using FAWT. The LEE and PEn features are extracted from different frequency bands of FAWT. We have found that LEE features showed better classification results as compared to PEn. The LEE features obtained maximum accuracy, sensitivity, and specificity of 96.84%, 95.8%, and 97.6% respectively with random forest (RF) classifier. Our system can be deployed in hospitals to assist cardiac physicians in their diagnosis.

      PubDate: 2018-05-29T07:24:47Z
  • Virus–human protein–protein interaction prediction using Bayesian
           matrix factorization and projection techniques
    • Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 3
      Author(s): Esmaeil Nourani, Farshad Khunjush, F. Erdoğan Sevilgen
      Pathogens infect host organisms by exploiting host cellular mechanisms and evading host defence mechanisms through molecular pathogen–host interactions (PHIs). Discovering new interactions between pathogen and human proteins is very crucial in understanding the infection mechanisms. By analysing interaction networks, the interactions responsible for infectious diseases can be detected and new drugs disabling these interactions can be delivered. In this paper, we propose a method based on Bayesian matrix factorization for predicting PHIs along with a projection-based technique and combine the results by employing an ensemble method. Furthermore, two features, target similarity and attacker similarity, are utilized for the first time in the literature for PHI prediction. The advantages of the proposed methods are two folds. Firstly, they relieve the need for negative samples which is significant since there is no available dataset providing negative samples for most of the pathogenic systems. Secondly, the experiments demonstrate that the proposed approach outperforms state-of-the-art methods; roughly 20% of top 50 predictions are among recently validated interactions. So, the search space for wet-lab experiments to obtain validated interactions can be considerably narrowed down from a huge number of possible interactions.

      PubDate: 2018-05-29T07:24:47Z
  • A hybrid approach for the delineation of brain lesion from CT images
    • Authors: Anjali Gautam; Balasubramanian Raman; Shailendra Raghuvanshi
      Abstract: Publication date: Available online 22 April 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Anjali Gautam, Balasubramanian Raman, Shailendra Raghuvanshi
      Brain lesion segmentation from radiological images is the most important task in accurate diagnosis of patients. This paper presents a hybrid approach for the segmentation of brain lesion from computed tomography (CT) images based on the combination of fuzzy clustering using hyper tangent function as the robust kernel and distance regularized level set evolution (DRLSE) function as the edge based active contour method. Kernel based fuzzy clustering method divides the image into different regions. These regions can be used to find region of interest by using DRLSE algorithm to generate the optimal region boundary. The proposed method results in smooth boundary of the required regions with high accuracy of segmentation. In this paper, results are compared with standard fuzzy c-means (FCM) clustering, spatial FCM, robust kernel based fuzzy clustering (RFCM) and DRLSE algorithms. The performance of the proposed method is evaluated on CT scan images of hemorrhagic lesion, which shows that our method can segment brain lesion more accurately than the other conventional methods.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.04.003
  • A fast and robust level set motion-assisted deformable registration method
           for volumetric CT guided lung intervention
    • Authors: Dae Gwan Kim; Namkug Kim; Sangmin Lee; Joon Beom Seo
      Abstract: Publication date: Available online 18 April 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Dae Gwan Kim, Namkug Kim, Sangmin Lee, Joon Beom Seo
      This paper describes the accurate deformable registration method for image-guided lung interventions, including lung nodule biopsy and radiofrequency ablation of lung tumours. A level set motion assisted deformable registration method for computed tomography (CT) images was proposed and its accuracy and speed were compared with those of other conventional methods. Fifteen 3D CT images obtained from lung biopsy patients were scanned. Each scan consisted of diagnostic and preoperative CT images. Each deformable registration method was initially evaluated with a landmark-based affine registration algorithm. Various deformable registration methods such as level set motion, demons, diffeomorphic demons, and b-spline were compared. Visual assessment by two expert thoracic radiologists using five scales showed an average visual score of 3.2 for level set motion deformable registration, whereas scores were below 3 for other deformable registration methods. In the qualitative assessment, the level set motion algorithm showed better results than those obtained with other deformable registration methods. A level set motion based deformable registration algorithm was effective for registering diagnostic and preoperative volumetric CT images for image-guided lung intervention.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.04.002
  • Geometric verification of the validity of Finite Element Method analysis
           of Abdominal Aortic Aneurysms based on Magnetic Resonance Imaging
    • Authors: Zuzanna Domagała; Hubert Stępak; Paweł Drapikowski; Anna Kociemba; Małgorzata Pyda; Katarzyna Karmelita-Katulska; Łukasz Dzieciuchowicz; Grzegorz Oszkinis
      Abstract: Publication date: Available online 12 April 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Zuzanna Domagała, Hubert Stępak, Paweł Drapikowski, Anna Kociemba, Małgorzata Pyda, Katarzyna Karmelita-Katulska, Łukasz Dzieciuchowicz, Grzegorz Oszkinis
      The currently used criterion of maximum transverse diameter for the Abdominal Aortic Aneurysm treatment has some limitations. Therefore, studies aiming at creating an individualized, therapeutic strategies are being conducted. Those include biomechanical assessment of rupture risk of an aneurysm based on the Finite Element Analysis of the geometric models of the aneurysm. The usual approach is to use the results of the computed tomography imaging to build a three-dimensional model of the aneurysm. The FEA is then performed and the resulting stress is analyzed to estimate the risk of rupture. Although such an approach brings significant improvements over the traditional maximum diameter method, it is difficult to ensure the validity of the assumptions (e.g. the material model) made. This paper presents a method to evaluate the correctness of such a modeling approach. The emergence of gated Magnetic Resonance Imaging (MRI) provides an opportunity to register aneurysm in both the systolic and diastolic phase of cardiac cycle. The corresponding geometric models are built and the results of the FEA applied to the diastolic model are compared with the actual deformation of the aneurysm observed in the patient's body – the systolic model. As a result, it is possible to verify whether the individualized diagnostic approach applied to a specific patient was correct. The geometry of the reference data and the analyzed models were compared using the Differential Surface Area Method to obtain geometry error for each case. The average geometry error equals 1.65%. In the best case the error amounts to 1.04%, in the worst to 3.00%. The obtained results are satisfactory and provide significant evidence that the Finite Element Analysis is a reliable method and can be potentially used for individualized diagnostics and treatment.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.04.001
  • Temperature controlled dual hypoxic chamber design for in vitro ischemia
    • Authors: Marcell Bagó; Dénes B. Horváthy; Melinda Simon; Bence Marschall; Ana Pinto; Olga Kuten; Dora Polsek; István Hornyák; Stefan Nehrer; Zsombor Lacza
      Abstract: Publication date: Available online 7 April 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marcell Bagó, Dénes B. Horváthy, Melinda Simon, Bence Marschall, Ana Pinto, Olga Kuten, Dora Polsek, István Hornyák, Stefan Nehrer, Zsombor Lacza
      In vitro ischemia models are designed to study various aspects of hypo-perfusion, focusing on the consequences of acute events under body temperature. Cold ischemia, on the other hand, is less investigated even though the beneficial effects of cooling is expected. The aim of the present work was to develop a device modeling cold and warm ischemia in vitro. We designed a dual hypoxic chamber suitable for cell culture plates. Oxygen-glucose deprivation was applied with continuous nitrogen flow and glucose-free cell culture media to mimic ischemia. Using Peltier units the temperature in both chambers were independently set between 4 and 37°C. Once the chambers reached the target temperature, samples were placed inside for the ischemic period, followed by a reperfusion stage under standard cell culture conditions. We tested rat calvaria bone pieces undergoing 1, 7, 12 and 24h of ischemia at 4 and 37°C. After 24h of reperfusion, cell number was measured with a tetrazolium cell viability assay. The shortest 1h period of ischemia paradoxically increased the post-reperfusion cell count, while cold-ischemia had an opposite effect. After 7h of warm ischemia the cells were already unable to recover, while under cold ischemia 60% of the cells were still functioning. After 12h of cold ischemia 50% of the cells were still be able to recover, while at 24h even the low temperature was unable to keep the cells alive. The markedly different effect of warm and cold ischemia suggests that this newly designed system is capable of reliable and reproducible modeling of ischemic conditions. Moreover, it also enables deeper investigations in the pathophysiology of cold ischemia at the cellular and tissue level.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.03.010
  • Thermal modelling and screening method for skin pathologies using active
    • Authors: M. Strąkowska; R. Strąkowski; M. Strzelecki; G. De Mey; B. Więcek
      Abstract: Publication date: Available online 5 April 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): M. Strąkowska, R. Strąkowski, M. Strzelecki, G. De Mey, B. Więcek
      This paper presents a novel screening approach of human skin pathologies using Active IR Thermography. The input of the proposed algorithm is the values of the physical parameters of the skin. Parameters are estimated based on dynamic thermographic measurements of human skin and the developed thermal model of the tissue. The calculations were based on the inverse thermal modelling. Classification was done using Support Vector Machine, Linear Discriminant Analysis and k-Nearest Neighbours classifiers. As an example, one presented the results of screening for psoriasis.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.03.009
  • Generalized Stockwell transform and SVD-based epileptic seizure detection
           in EEG using random forest
    • Authors: Tao Zhang; Wanzhong Chen; Mingyang Li
      Abstract: Publication date: Available online 5 April 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Tao Zhang, Wanzhong Chen, Mingyang Li
      Purpose Visual inspection of electroencephalogram (EEG) records by neurologist is the main diagnostic method of epilepsy but it is particularly time-consuming and expensive. Hence, it is of great significance to develop automatic seizure detection technique. Methods In this work, a seizure detection approach, synthesizing generalized Stockwell transform (GST), singular value decomposition (SVD) and random forest, was proposed. Utilizing GST, the raw EEG was transformed into a time–frequency matrix, then the global and local singular values were extracted by SVD from the holistic and partitioned matrices of GST, respectively. Subsequently, four local parameters were calculated from each vector of local singular values. Finally, the global singular value vectors and local parameters were respectively fed into two random forest classifiers for classification, and the final category of a testing EEG was voted based on sub-labels obtained from the trained classifiers. Results Four most common but challenging classification tasks of Bonn EEG database were investigated. The highest accuracies of 99.12%, 99.63%, 99.03% and 98.62% were achieved using our presented technique, respectively. Conclusions Our proposed technique is comparable or superior to other up-to-date methods. The presented method is promising and able to handle with kinds of epileptic seizure detection tasks with satisfactory accuracy.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.03.007
  • Multi-modal framework for automatic detection of diagnostically important
           regions in nonalcoholic fatty liver ultrasonic images
    • Authors: R. Bharath; P. Rajalakshmi; Mohammad Abdul Mateen
      Abstract: Publication date: Available online 5 April 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): R. Bharath, P. Rajalakshmi, Mohammad Abdul Mateen
      The severity of fat in ultrasonic liver images is quantified based on characteristics of three regions in the image namely diaphragm, periportal veins and texture of liver parenchyma. The characteristics of these regions vary with the severity of fat in the liver, and is subjected to low signal to noise ratio, low contrast, poorly defined organ boundaries, etc., hence locating these regions in ultrasound images is challenging task for the sonographers. Automated detection of these regions will help the sonographers to do accurate diagnosis in shorter time, and also acts as a fundamental step to develop automated diagnostic algorithms. In this paper, we propose a novel multi-modal framework for detecting diaphragm, periportal veins and texture of liver parenchyma in ultrasonic liver ultrasound images. Since the characteristics of these regions differ from each other, we propose a specific algorithm for detecting each region. Diaphragm and periportal veins are detected with the combination of Viola Jones and GIST descriptor based classifier, while homogeneous texture regions are detected with the combination of histogram features based classifier and connected components algorithm. The proposed algorithm when tested on 180 ultrasound liver images, detected the diaphragm, periportal veins and texture regions with an accuracy of 97%, 91% and 100% respectively.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.03.008
  • Improvement in the diagnosis of melanoma and dysplastic lesions by
           introducing ABCD-PDT features and a hybrid classifier
    • Authors: Arezoo Zakeri; Alireza Hokmabadi
      Abstract: Publication date: Available online 31 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Arezoo Zakeri, Alireza Hokmabadi
      Melanoma and dysplastic lesions are pigmented skin lesions whose accurate classification is of great importance. In this paper, we have proposed a computer-aided diagnosis (CAD) system to improve the diagnostic ability of the conventional ABCD (asymmetry, border irregularity, color, and diameter) analysis. We introduced features extracted by local analysis of range of intensity variations within the lesion that describe pigment distribution and texture (PDT) features. The statistical distribution of pigmentation at a specified direction and distance was analyzed through grey level co-occurrence matrix (GLCM). Some other quantitative features were also extracted by computing neighborhood grey-tone difference matrix. These were correlated with human perception of texture. A hybrid classifier was designed for classification of melanoma, dysplastic, and benign lesions. Log-linearized Gaussian mixture neural network (LLGMNN), K-nearest neighborhood (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) construct the hybrid classifier. The proposed system was evaluated on a set of 792 dermoscopy images and the diagnostic accuracies of 96.8%, 97.3%, and 98.8% for melanoma, dysplastic, and benign lesions were achieved, respectively. The results indicate that PDT features are promising features which in combination with the conventional ABCD features are capable of enhancing the classification performance of the pigmented skin lesions.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.03.005
  • Enhancement of graphene quantum dots based applications via optimum
           physical chemistry: A review
    • Authors: Pushpa Jegannathan; Amin Termeh Yousefi; Mohd Sayuti Abd Karim; Nahrizul Adib Kadri
      Abstract: Publication date: Available online 24 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Pushpa Jegannathan, Amin Termeh Yousefi, Mohd Sayuti Abd Karim, Nahrizul Adib Kadri
      Graphene quantum dots (GQDs) is a promising new substance from the carbon material family that has been attracting researchers of many fields, such as biomedical sensors, medical imaging, polymer science, solar cells, light emitting diodes, and photoelectrons. Its unique electrical and mechanical properties could encourage its usage due to its low cost, high surface area, safety, stable luminescence, excellent biocompatibility, suitable conductivity, and low toxicity. The dispersibility of GQDs in common solvents depends on hydrophobicity/hydrophilicity, which is particularly important toward its homogeneous incorporation into various polymer layers. This review discusses the global demand for GQDs and explore the main factors encouraging its utilization in various devices. Moreover, different synthesis methods of GQDs were compared, and recent investigation on GQDs based composite applications are analyzed. Finally, the future of GQDs is detailed, focusing on the gaps in its role in future technology.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.03.006
  • Experimental investigation of particle size distribution and morphology of
           alumina-yttria-ceria-zirconia powders obtained via sol–gel route
    • Authors: D.S. Nakonieczny; M. Antonowicz; Z.K. Paszenda; T. Radko; S. Drewniak; W. Bogacz; C. Krawczyk
      Abstract: Publication date: Available online 23 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): D.S. Nakonieczny, M. Antonowicz, Z.K. Paszenda, T. Radko, S. Drewniak, W. Bogacz, C. Krawczyk
      Background Oxide-doped zirconia is currently commonly used ceramics in dental prosthetics. However, its use raises a lot of controversy. This is related to the stability of the zirconia metastable phases in the human mouth environment and it sensitivity for the so-called low-temperature degradation. A key way to avoid this type of negative phenomena is doping ZrO2 with selected metal oxides and choosing appropriate methods for the synthesis of ceramic powders. Objective The aim of this paper is to present investigations of modification and to analyse the influence of chemical composition and volume of parent-solvent for the morphology and thermal properties of ceramic powders prepared in a ZrO2-CeO2-Y2O3-Al2O3 system. Methods The powders were obtained by using the sol–gel method in an inert gas atmosphere and ambient temperature using zirconium n-propoxide for this purpose. Morphology was examined by using scanning electron microscopy (SEM) and particle size distribution (PSD); thermal properties was evaluated using thermogravimetric analysis (TGA/DTA/DTG), and chemical composition was confirmed by using electron probe microanalysis (EPMA) Results Depending from the volume of the CeO2 precursor solution of and regardless of the volume of the second oxide precursor, was observed difference in morphology of the obtained powders. Overall trend is related to reduce the size of agglomerates with an increase in the volume of the precursor of CeO2. Conclusions The influence of various chemical compositions for morphology and thermal properties is negligible. In contrast, a clear correlation is observed between the volume of parent alcohol for both morphology and thermal properties. Use of sol–gel method to further research in view of these results appears to be appropriate.

      PubDate: 2018-04-25T11:15:29Z
      DOI: 10.1016/j.bbe.2018.02.010
  • An improved feature based image fusion technique for enhancement of liver
    • Authors: P. Sreeja; S. Hariharan
      Abstract: Publication date: Available online 19 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): P. Sreeja, S. Hariharan
      This paper describes two methods for enhancement of edge and texture of medical images. In the first method optimal kernel size of range filter suitable for enhancement of liver and lesions is deduced. The results have been compared with conventional edge detection algorithms. In the second method the feasibility of feature based pixel wise image fusion for enhancing abdominal images is investigated. Among the different algorithms developed in the medical image fusion pixel level fusion is capable of retaining the maximum relevant information with better implementation and computational efficiency. Conventional image fusion includes multi-modal fusion and multi-resolution fusion. The present work attempts to fuse together, texture enhanced and edge enhanced images of the input image in order to obtain significant enhancement in the output image. The algorithm is tested in low contrast medical images. The result shows an improvement in contrast and sharpness of output image which will provide a basis for a better visual interpretation leading to more accurate diagnosis. Qualitative and quantitative performance evaluation is done by calculating information entropy, MSE, PSNR, SSIM and Tenengrad values.

      PubDate: 2018-03-20T12:25:18Z
      DOI: 10.1016/j.bbe.2018.03.004
  • Automatic identifying of maternal ECG source when applying ICA in fetal
           ECG extraction
    • Authors: Yu Qiong; Yan Huawen; Song Lin; Guo Wenya; Liu Hongxing; Si Junfeng; Zhao Ying
      Abstract: Publication date: Available online 19 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Yu Qiong, Yan Huawen, Song Lin, Guo Wenya, Liu Hongxing, Si Junfeng, Zhao Ying
      Independent component analysis (ICA) is usually used as a preliminary step for maternal electrocardiogram (ECG) QRS detection in fetal ECG extraction. When applying ICA to do this, a troublesome problem arises from how to automatically identify the separated maternal ECG component. In this paper we proposed a method called PRCH (short for Peak to peak entropy, R-R interval entropy, Correlation coefficient and Heart rate) for the automatic identifying. In the method, we defined four kinds of features, including amplitude, instantaneous heart rate, morphology and average heart rate, to characterize a signal, and determined some decision parameters through machine learning. Experiments and comparison with other three existed methods were given. Through taking metric F1 for evaluation, it showed that the proposed PRCH method has the highest identifying accuracy and generalization capability.

      PubDate: 2018-03-20T12:25:18Z
      DOI: 10.1016/j.bbe.2018.03.003
  • Peripheral blood smear analysis using image processing approach for
           diagnostic purposes: A review
    • Authors: Roopa B. Hegde; Keerthana Prasad; Harishchandra Hebbar; I. Sandhya
      Abstract: Publication date: Available online 15 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Roopa B. Hegde, Keerthana Prasad, Harishchandra Hebbar, I. Sandhya
      Peripheral blood smear analysis is a common practice to evaluate health status of a person. Many disorders such as malaria, anemia, leukemia, thrombocytopenia, sickle cell anemia etc., can be diagnosed by evaluating blood cells. Many groups have reported methods to automate blood smear analysis for detection of specific disorders for diagnostic purposes. In this paper, we have summarized the methods used to analyze peripheral blood smears using image processing techniques. We have categorized these methods into three groups based on approaches such as WBC analysis, RBC analysis and platelet analysis. We conclude that there is a need for a method of automation to match with human evaluation process and rule out any abnormality present in the blood smear. It is desirable for studies on automation of peripheral blood smear analysis to focus on development of robust method to handle illumination and color shade variations. Also, it is desirable to design a method which could collect all the abnormal regions from all views of a specimen to limit the manual evaluation to those regions making it more feasible for telemedicine applications.

      PubDate: 2018-03-20T12:25:18Z
      DOI: 10.1016/j.bbe.2018.03.002
  • Entropies for automated detection of coronary artery disease using ECG
           signals: A review
    • Authors: U. Rajendra Acharya; Yuki Hagiwara; Joel En Wei Koh; Shu Lih Oh; Jen Hong Tan; Muhammad Adam; Ru San Tan
      Abstract: Publication date: Available online 10 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): U. Rajendra Acharya, Yuki Hagiwara, Joel En Wei Koh, Shu Lih Oh, Jen Hong Tan, Muhammad Adam, Ru San Tan
      Coronary artery disease (CAD) develops when coronary arteries are unable to supply oxygen-rich blood to the heart due to the accumulation of cholesterol plaque on the inner walls of the arteries. Chronic insufficient blood flow leads to the complications, including angina and heart failure. In addition, acute plaque rupture may lead to vessel occlusion, causing a heart attack. Thus, it is encouraged to have regular check-ups to diagnose CAD early and avert complications. The electrocardiogram (ECG) is a widely used diagnostic tool to study the electrical activity of the heart. However, ECG signals are highly chaotic, complex, and non-stationary in their behaviour. It is laborious, and requires expertise, to visually interpret these signals. Hence, the computer-aided detection system (CADS) is developed to assist clinicians to interpret the ECG signals fast and reliably. In this work, we have employed sixteen entropies to extract the various hidden signatures from ECG signals of normal healthy persons as well as patients with CAD. We observed that the majority of extracted entropy features showed lower values for CAD patients compared to normal subjects. We believe that there is one possible reason which could be the decreased in the variability of ECG signals is associated with reduced heart pump function.

      PubDate: 2018-03-20T12:25:18Z
      DOI: 10.1016/j.bbe.2018.03.001
  • In silico testing of optimized Fuzzy P+D controller for artificial
    • Authors: Selim Soylu; Kenan Danisman
      Abstract: Publication date: Available online 5 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Selim Soylu, Kenan Danisman
      Background and objectives Despite therapeutic advances, a complete cure has not been found yet for patients with type 1 diabetes (T1D). Artificial pancreas (AP) is a promising approach to cope with this disease. The controller part of the AP can compute the insulin infusion rate that keeps blood glucose concentration (BGC) in normoglycemic ranges. Most controllers rely on model-based controllers and use manual meal announcements or meal detection algorithms. For a fully automated AP, a controller only using the patient's BGC data is needed. Methods An optimized Mamdani-type hybrid Fuzzy P+D controller was proposed. Using the University of Virginia/Padova Simulator, a 36h scenario was tested in nine virtual adult patients. To take into account the effect of continuous glucose monitor noise, the scenario was repeated 25 times for each adult. The main outcomes were the percentage of time BGC levels are in the euglycemic range and blood glucose risk index (BGRI), respectively. Results The obtained BGC values were found to be in the euglycemic range for 82.6% of the time. Moreover, the BGC values were below 50mg/dl, below 70mg/dl and above 250mg/dl for 0%, 0.35% and 0.74% of the time, respectively. The BGRI, low blood glucose index (LBGI), and high blood glucose index (HBGI) were also found as 3.75, 0.34 and 3.41, respectively. The proposed controller both increases the time the BGC levels are in the euglycemic range and causes less hypoglycemia and hyperglycemia relative to the published techniques studied in a similar scenario and population.

      PubDate: 2018-03-07T23:02:06Z
      DOI: 10.1016/j.bbe.2018.02.009
  • The ADHD effect on the actions obtained from the EEG signals
    • Authors: Reza Yaghoobi Karimu; Sassan Azadi; Parviz Keshavarzi
      Abstract: Publication date: Available online 2 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Reza Yaghoobi Karimu, Sassan Azadi, Parviz Keshavarzi
      Attention-deficit/hyperactivity disorder (ADHD) is an important challenge in studies of children's ethology that unbalances the opposite behaviors for creating inattention along with or without hyperactivity. Nevertheless, most studies on the ADHD children, which employed the EEG signals for analyzing the ADHD influence on the brain activities, considered the EEG signals as a random or chaotic process without considering the role of these opposites in the brain activities. In this study, we considered the EEG signals as a biotic process according to these opposites and examined the ADHD effect on the brain activity by defining the dual sets of transitions between states in the complement plots of quantized EEG segments. The results of this study generally indicated that the complement plots of quantized EEG signal have a surprising regularity similar to the Mandala patterns compared to the chaotic processes. These results also indicated that the probability of occurrence of dual sets in the complement plots of ADHD children was averagely different (p <0.01) from that of healthy children, so that the SVM classifier developed by these probabilities could significantly separate the ADHD from healthy children (99.37% and 98.25% for training and testing sets, respectively). Therefore, the complement plots of quantized EEG signals relevant to the ADHD children not only can quantify informational opposition caused from inattention, hyperactivity and impulsivity, but also these plots can provide remarkable information for developing new diagnostic and therapeutic techniques.

      PubDate: 2018-03-07T23:02:06Z
      DOI: 10.1016/j.bbe.2018.02.007
  • An optimal spectroscopic feature fusion strategy for MR brain tumor
           classification using Fisher Criteria and Parameter-Free BAT optimization
    • Authors: Taranjit Kaur; Barjinder Singh Saini; Savita Gupta
      Abstract: Publication date: Available online 28 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Taranjit Kaur, Barjinder Singh Saini, Savita Gupta
      In the present work, a fused metabolite ratio is proposed that integrates the conventional metabolite ratios in a weighted manner to improve the diagnostic accuracy of glioma brain tumor categorization. Each metabolite ratio is weighted by the value generated by the Fisher and the Parameter-Free BAT (PFree BAT) optimization algorithm. Here, feature fusion is formulated as an optimization problem with PFree BAT optimization as its underlying search strategy and Fisher Criterion serving as a fitness function. Experiments were conducted on the magnetic resonance spectroscopy (MRS) data of 50 subjects out of which 27 showed low-grade glioma and rest presented high-grade. The MRS data was analyzed for the peaks. The conventional metabolite ratios, i.e., Choline/N-acetyl aspartate (Cho/NAA), Cho/Creatine (Cho/Cr), were quantitated using peak integration that exhibited difference among the tumor grades. The difference in the conventional metabolite ratios was enhanced by the proposed fused metabolite ratio that was duly validated by metrics of sensitivity, specificity, and the classification accuracy. Typically, the fused metabolite ratio characterized low-grade and high-grade with a sensitivity of 96%, specificity of 91%, and an accuracy of 93.72% when fed to the K-nearest neighbor classifier following a fivefold cross-validation data partitioning scheme. The results are significantly better than that obtained by the conventional metabolites where an accuracy equal to 80%, 87%, and 89% was attained. Prominently, the results using the fused metabolite ratio show a surge of 4.7% in comparison to Cho/Cr+Cho/NAA+NAA/Cr. Moreover, the obtained results are better than the similar works reported in the literature.

      PubDate: 2018-03-07T23:02:06Z
      DOI: 10.1016/j.bbe.2018.02.008
  • Efficient compression of bio-signals by using Tchebichef moments and
           Artificial Bee Colony
    • Authors: Khalid M. Hosny; Asmaa K. Mohamed; Ehab R. Mohamed
      Abstract: Publication date: Available online 24 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Khalid M. Hosny, Asmaa K. Mohamed, Ehab R. Mohamed
      In this paper, an algorithm is proposed for efficient compression of bio-signals based on discrete Tchebichef moments and Artificial Bee Colony (ABC). The Tchebichef moments are used to extract features of the bio-signals, then, the ABC algorithm is used to select of the optimum features which achieve the best bio-signal quality for a specific compression ratio (CR). The proposed algorithm has been tested by using different datasets of Electrocardiogram (ECG), Electroencephalogram (EEG), and Electromyogram (EMG). The optimum feature selection using ABC significantly improve the quality of the reconstructed bio-signals. Different numerical experiments are performed where to compress different records of ECG, EEG and EMG bio-signals by using the proposed algorithm and the most recent existing methods. The performance of the proposed algorithm and the other existing methods are evaluated using different metrics such as CR, PRD, and peak signal to noise ratio (PSNR). The comparison shown that, at the same CR, the proposed compression algorithm yields the best quality of the reconstructed signals over the other existing methods.

      PubDate: 2018-02-26T21:34:15Z
      DOI: 10.1016/j.bbe.2018.02.006
  • Accurate prediction of continuous blood glucose based on support vector
           regression and differential evolution algorithm
    • Authors: Takoua Hamdi; Jaouher Ben Ali; Véronique Di Costanzo; Farhat Fnaiech; Eric Moreau; Jean-Marc Ginoux
      Abstract: Publication date: Available online 23 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Takoua Hamdi, Jaouher Ben Ali, Véronique Di Costanzo, Farhat Fnaiech, Eric Moreau, Jean-Marc Ginoux
      Type 1 diabetes (T1D) is a chronic disease requiring patients to know their blood glucose values in order to ensure blood glucose levels as close to normal as possible. Hence, the ability to predict blood glucose levels is of a great interest for clinical researchers. In this sense, the literature is rich with several solutions that can predict blood glucose levels. Unfortunately, these methods require the patient to specific their daily activities: meal intake, insulin injection and emotional factors, which can be error prone. To reduce this burden on the patent, this work proposes to use only continuous glucose monitoring (CGM) data to predict blood glucose levels independently of other factors. To support this, support vector regression (SVR) and differential evolution (DE) algorithms were investigated. The proposed method is validated using real CGM data of 12 patients. The obtained average of root mean square error (RMSE) was 9.44, 10.78, 11.82 and 12.95mg/dL for prediction horizon (PH) respectively equal to 15, 30, 45 and 60min. The results of the present study and comparison with some previous works show that the proposed method holds promise. The SVR based on DE algorithm achieved high prediction accuracy while being robustness, automatic, and requiring no human intervention.

      PubDate: 2018-02-26T21:34:15Z
      DOI: 10.1016/j.bbe.2018.02.005
  • Review on plantar data analysis for disease diagnosis
    • Authors: Julian Andres Ramirez Bautista; Silvia Liliana Chaparro Cárdenas; Antonio Hernández Zavala; Jorge Adalberto Huerta-Ruelas
      Abstract: Publication date: Available online 19 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Julian Andres Ramirez Bautista, Silvia Liliana Chaparro Cárdenas, Antonio Hernández Zavala, Jorge Adalberto Huerta-Ruelas
      Force distribution on foot surface allows to understand the human mechanical behavior, providing detailed information for the evaluation of foot alterations. In diagnosis for diseases related to plantar pathologies, there are many devices for plantar pressure measurement, and corresponding algorithms for data analyzing, providing medical tools for assisting in treatment, early detection, and the development of preventive strategies. In medicine, use of computational intelligence is increasing, making the diagnostic processes faster and more accurate. Clinical Decision Support Systems (CDSS) can handle large amounts of data to improve decision-making, helping to prevent the deterioration of people's health. Numerous approaches have been applied over the past few decades to solve medical problems such as hepatitis, diabetes, liver disease, pathological gait, and plantar diseases, among others. This paper presents the developments reported in the literature for detecting diseases through plantar pressure data and the corresponding algorithms for its analysis and diagnosis, using different electronic measurements systems. Finally, we present a discussion about the future work required to improve in the field of plantar pressure diagnosis algorithms using different approaches suggested by the authors as potential candidates. In this sense, hybrid systems which include fuzzy concepts are the most promising methodology.

      PubDate: 2018-02-26T21:34:15Z
      DOI: 10.1016/j.bbe.2018.02.004
  • Gene selection from large-scale gene expression data based on fuzzy
           interactive multi-objective binary optimization for medical diagnosis
    • Authors: Saleh Shahbeig; Akbar Rahideh; Mohammad Sadegh Helfroush; Kamran Kazemi
      Abstract: Publication date: Available online 18 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Saleh Shahbeig, Akbar Rahideh, Mohammad Sadegh Helfroush, Kamran Kazemi
      An efficient fuzzy interactive multi-objective optimization method is proposed to select the sub-optimal subset of genes from large-scale gene expression data. It is based on the binary particle swarm optimization (BPSO) algorithm tuned by a chaotic method. The proposed method is able to select the sub-optimal subset of genes with the least number of features that can accurately distinguish between the two classes, e.g. the normal and cancerous samples. The proposed method is evaluated on several publicly available microarray and RNA-sequencing gene expression datasets such as leukemia, colon cancer, central nervous system, lung cancer, ovarian cancer, prostate cancer and RNA-seq lung disease. The results indicate that the proposed method can identify the minimum number of genes to achieve the most accuracy, sensitivity and specificity in the classification process. Achieving 100% accuracy in six out of the seven datasets investigated in this study, demonstrates the high capacity of the proposed algorithm to find the sub-optimal subset of genes. This approach is useful in clinical applications to extract the most influential genes on a disease and to find the treatment procedure for the disease.

      PubDate: 2018-02-18T20:50:29Z
      DOI: 10.1016/j.bbe.2018.02.002
  • Combination of clinical and multiresolution features for glaucoma
           detection and its classification using fundus images
    • Authors: T.R. Kausu; Varun P. Gopi; Khan A. Wahid; Wangchuk Doma; Swamidoss Issac Niwas
      Abstract: Publication date: Available online 17 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): T.R. Kausu, Varun P. Gopi, Khan A. Wahid, Wangchuk Doma, Swamidoss Issac Niwas
      Glaucoma is a neuro-degenerative disorder of the eye and it leads to permanent blindness when untreated or detected in the later stage. The main cause of glaucoma is the damage of the optic nerve, which occurs due to the increase of eye pressure. Hence the early detection of this disease is critical in time and which can help to prevent further vision loss. The assessment of optic nerve head using fundus images is more beneficial than the raised intra ocular pressure assessment in population-based glaucoma screening. This work proposed a novel method for glaucoma identification based on time-invariant feature cup to disk ratio and anisotropic dual-tree complex wavelet transform features. Optic disk segmentation is done by using Fuzzy C-Means clustering method and Otsu's thresholding is used for optic cup segmentation. The results show the proposed method achieved an accuracy rate of 97.67% with 98% sensitivity using a multilayer perceptron model that is considered as clinically significant when compared to the existing works.

      PubDate: 2018-02-18T20:50:29Z
      DOI: 10.1016/j.bbe.2018.02.003
  • Use of the surface electromyography for a quantitative trend validation of
           estimated muscle forces
    • Authors: Magdalena Żuk; Malgorzata Syczewska; Celina Pezowicz
      Abstract: Publication date: Available online 16 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Magdalena Żuk, Malgorzata Syczewska, Celina Pezowicz
      Surface EMG is a non-invasive measurement of an individual muscle activity and it can be used as the indirect form of a simulated muscle forces validation. The quantitative curves comparison has some potential, which has not been fully exploited yet [13]. The purpose of current study was to quantitatively compare muscle forces predicted using musculoskeletal models to measured surface electromyography signals. A metrics based on correlation and an electromechanical delay correction for a quantitative trend validation has been proposed. Kinematics of a normal gait was collected for three healthy subjects together with ground reaction forces and EMG signals of eight different muscles of both legs. Dynamic simulations have been performed for two models of differing complexity from OpenSim library (Gait2392 and Gait2354) [2,5,6], static optimization method and computed muscle control algorithm [20] have been used. It has been shown, that the level of force-EMG trend compliance, obtained for applied models and simulation techniques, is related rather to the selected muscle than to applied optimization criteria or technique. The contribution of analyzed muscles during gait has been predicted better by complex model than by simplified model. Moreover relationship between the body proportion of subject and the degree of correlation has been observed. Proposed metrics and obtained results can be the basis for further identification of cost functions, which could most closely describe motor control strategy.

      PubDate: 2018-02-18T20:50:29Z
      DOI: 10.1016/j.bbe.2018.02.001
  • Denoising of Electrocardiogram (ECG) signal by using empirical mode
           decomposition (EMD) with non-local mean (NLM) technique
    • Authors: Shailesh Kumar; Damodar Panigrahy; P.K. Sahu
      Abstract: Publication date: Available online 14 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Shailesh Kumar, Damodar Panigrahy, P.K. Sahu
      In this paper, the investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework for preservation of the edges and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser mean of percent root mean square difference (PRD), mean square error (MSE), and better mean SNR improvement compared to other well-known methods at different input SNR. The proposed methodology also shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR.

      PubDate: 2018-02-18T20:50:29Z
      DOI: 10.1016/j.bbe.2018.01.005
  • An epileptic seizure detection system based on cepstral analysis and
           generalized regression neural network
    • Authors: Erdem Yavuz; Mustafa Cem Kasapbaşı; Can Eyüpoğlu; Rıfat Yazıcı
      Abstract: Publication date: Available online 3 February 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Erdem Yavuz, Mustafa Cem Kasapbaşı, Can Eyüpoğlu, Rıfat Yazıcı
      This study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroencephalogram (EEG) recordings. The EEG recordings are obtained from an open database which has been widely studied with many different combinations of feature extraction and classification techniques. Cepstral analysis technique is mainly used for speech recognition, seismological problems, mechanical part tests, etc. Utility of cepstral analysis based features in EEG signal classification is explored in the paper. In the proposed study, mel frequency cepstral coefficients (MFCCs) are computed in the feature extraction stage and used in neural network based classification stage. MFCCs are calculated based on a frequency analysis depending on filter bank of approximately critical bandwidths. The experimental results have shown that the proposed method is superior to most of the previous studies using the same dataset in classification accuracy, sensitivity and specificity. This achieved success is the result of applying cepstral analysis technique to extract features. The system is promising to be used in real time seizure detection systems as the neural network adopted in the proposed method is inherently of non-iterative nature.

      PubDate: 2018-02-18T20:50:29Z
      DOI: 10.1016/j.bbe.2018.01.002
  • Comparative assessment of texture features for the identification of
           cancer in ultrasound images: A review
    • Authors: Oliver Faust; U. Rajendra Acharya; Kristen M. Meiburger; Filippo Molinari; Joel E.W. Koh; Chai Hong Yeong; Pailin Kongmebhol; Kwan Hoong Ng
      Abstract: Publication date: Available online 17 January 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Oliver Faust, U. Rajendra Acharya, Kristen M. Meiburger, Filippo Molinari, Joel E.W. Koh, Chai Hong Yeong, Pailin Kongmebhol, Kwan Hoong Ng
      In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which deliver real benefits to patients by improving the diagnosis accuracy while reducing health care cost.

      PubDate: 2018-02-18T20:50:29Z
      DOI: 10.1016/j.bbe.2018.01.001
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