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BIOTECHNOLOGY (244 journals)                  1 2 | Last

Showing 1 - 200 of 244 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 8)
Advanced Biomedical Research     Open Access  
Advances in Bioscience and Biotechnology     Open Access   (Followers: 17)
Advances in Genetic Engineering & Biotechnology     Hybrid Journal   (Followers: 9)
Advances in Regenerative Medicine     Open Access   (Followers: 3)
African Journal of Biotechnology     Open Access   (Followers: 6)
Algal Research     Partially Free   (Followers: 11)
American Journal of Biochemistry and Biotechnology     Open Access   (Followers: 69)
American Journal of Bioinformatics Research     Open Access   (Followers: 7)
American Journal of Polymer Science     Open Access   (Followers: 33)
Amylase     Open Access  
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: 45)
Applied Biosafety     Hybrid Journal  
Applied Food Biotechnology     Open Access   (Followers: 3)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 67)
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: 9)
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)
Beitr?ge zur Tabakforschung International/Contributions to Tobacco Research     Open Access   (Followers: 3)
Bio-Algorithms and Med-Systems     Hybrid Journal   (Followers: 2)
Bio-Research     Full-text available via subscription   (Followers: 4)
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   (Followers: 1)
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)
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)
Biosensors Journal     Open Access  
Biosimilars     Open Access   (Followers: 1)
Biosurface and Biotribology     Open Access  
Biotechnic and Histochemistry     Hybrid Journal   (Followers: 1)
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: 8)
Biotechnology & Biotechnological Equipment     Open Access   (Followers: 4)
Biotechnology Advances     Hybrid Journal   (Followers: 34)
Biotechnology and Applied Biochemistry     Hybrid Journal   (Followers: 44)
Biotechnology and Bioengineering     Hybrid Journal   (Followers: 160)
Biotechnology and Bioprocess Engineering     Hybrid Journal   (Followers: 6)
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: 2)
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: 17)
Biotechnology Law Report     Hybrid Journal   (Followers: 4)
Biotechnology Letters     Hybrid Journal   (Followers: 34)
Biotechnology Progress     Hybrid Journal   (Followers: 41)
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  
BIOTIK : Jurnal Ilmiah Biologi Teknologi dan Kependidikan     Open Access  
Biotribology     Hybrid Journal   (Followers: 1)
BMC Biotechnology     Open Access   (Followers: 17)
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)
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: 55)
Current Pharmaceutical Biotechnology     Hybrid Journal   (Followers: 9)
Current Research in Bioinformatics     Open Access   (Followers: 13)
Current Trends in Biotechnology and Chemical Research     Open Access   (Followers: 3)
Current trends in Biotechnology and Pharmacy     Open Access   (Followers: 8)
DNA and RNA Nanotechnology     Open Access  
EBioMedicine     Open Access  
Electronic Journal of Biotechnology     Open Access  
Entomologia Generalis     Full-text available via subscription   (Followers: 1)
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  
Horticultural Biotechnology Research     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)
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)
Indonesian Journal of Medicine     Open Access  
Industrial Biotechnology     Hybrid Journal   (Followers: 18)
International Biomechanics     Open Access  
International Journal of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 14)
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: 4)
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)
JMIR Biomedical Engineering     Open Access  
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 BioScience and Biotechnology     Open Access  
Journal of Biosecurity Biosafety and Biodefense Law     Hybrid Journal   (Followers: 3)
Journal of Biotechnology     Hybrid Journal   (Followers: 63)
Journal of Biotechnology and Strategic Health Research     Open Access   (Followers: 1)
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   (Followers: 1)
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 Ecobiotechnology     Open Access  
Journal of Essential Oil Research     Hybrid Journal   (Followers: 2)
Journal of Experimental Biology     Full-text available via subscription   (Followers: 25)
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: 18)
Journal of Integrative Bioinformatics     Open Access  
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: 13)
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: 13)
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)
Meat Technology     Open Access  
Messenger     Full-text available via subscription  
Metabolic Engineering Communications     Open Access   (Followers: 4)
Metalloproteinases In Medicine     Open Access  
Microbial Biotechnology     Open Access   (Followers: 10)
MicroMedicine     Open Access   (Followers: 3)
Molecular and Cellular Biomedical Sciences     Open Access   (Followers: 1)
Molecular Biotechnology     Hybrid Journal   (Followers: 13)
Molecular Genetics and Metabolism Reports     Open Access   (Followers: 3)
Nanobiomedicine     Open Access  
Nanobiotechnology     Hybrid Journal   (Followers: 2)

        1 2 | Last

Journal Cover
Biocybernetics and Biological Engineering
Journal Prestige (SJR): 0.384
Citation Impact (citeScore): 2
Number of Followers: 5  
  Full-text available via subscription Subscription journal
ISSN (Print) 0208-5216
Published by Elsevier Homepage  [3155 journals]
  • Multi-channel acoustic analysis of phoneme /s/ mispronunciation for
           lateral sigmatism detection
    • Abstract: Publication date: Available online 12 December 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Michal Krecichwost, Zuzanna Miodonska, Pawel Badura, Joanna Trzaskalik, Natalia Mocko The paper presents a method for computer-aided detection of lateral sigmatism. The aim of the study is to design an automated sigmatism diagnosis tool. For that purpose, a reference speech corpus has been collected. It contains 438 recordings of a phoneme /s/ surrounded by certain vowels with normative and simulated pathological pronunciation. The acoustic signal is recorded with an acoustic mask, which is a set of microphones organised in a semi-cylindrical surface around the subject's face. Frames containing /s/ phoneme are subjected to beamforming and feature extraction. Two different feature vectors containing, e.g., Mel-frequency cepstral coefficients and fricative formants, are defined and evaluated in terms of binary classification involving support vector machines. A single-channel analysis is confronted with multi-channel processing. The experimental results show that the multi-channel speech signal processing supported by beamforming is able to increase the pathology detection capabilities in general.
  • Coupling of inertial measurement units with a virtual world model for
           supporting navigation in bronchoscopy
    • Abstract: Publication date: Available online 7 December 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Dariusz Michalski, Tomasz Nabagło, Zbisław Tabor Background and objectiveThe purpose of this paper is to provide a method for supporting navigation in bronchoscopy based on measurements of absolute orientation of a tip of a bronchoscope and the length a bronchoscope is pushed in the lumen of an examined bronchial structure.MethodsA hardware solution is designed and developed for collecting the data related to the absolute orientation of a tip of a bronchoscope and the length a bronchoscope is pushed in the lumen of an examined structure. A software which processes these data and visualizes in real-time the actual location of a bronchoscope tip in the lumen of a digital model of the examined structure (i.e. virtual bronchoscopy) is also designed and implemented.ResultsA calibration procedure is developed which constitutes a basis for the operation of the proposed system. A phantom of a tree-like structure is build, imitating the anatomy of a bronchial tree, and the proposed method of navigation is tested for the task of navigating in the lumen of the phantom to user-selected target locations.ConclusionA method has been proposed and tested for Inertial Measurement Unit (IMU)-based support of navigation in bronchoscopy.
  • A speech recognition system based on electromyography for the
           rehabilitation of dysarthric patients: A Thai syllable study
    • Abstract: Publication date: Available online 3 December 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Nida Sae Jong, Pornchai Phukpattaranont The objective of this study is to develop a speech recognition system for classifying nine Thai syllables, which is used for the rehabilitation of dysarthric patients, based on five channels of surface electromyography (sEMG) signals from the human articulatory muscles. After the sEMG signal from each channel was collected, it was processed by a band-pass filter from 20–450 Hz for noise removal. Then, six features from three feature categories were determined and analyzed, namely, mean absolute value (MAV) and wavelength (WL) from amplitude based features (ABF), zero crossing (ZC) and mean frequency (MNF) from frequency based features (FBF), and L-kurtosis (L-KURT) and L-skewness (L-SKW) from statistics based features (SBF). Subsequently, a spectral regression extreme learning machine (SRELM) was used as the feature projection technique to reduce the dimension of feature vector from 30 to 8. Finally, the projected features were classified using a feed forward neural network (NN) classifier with 5-fold cross-validation. The proposed system was evaluated with the sEMG signals from seven healthy volunteers and five dysarthric volunteers. The results show that the proposed system can recognize the sEMG signals from both healthy and dysarthric volunteers. The average classification accuracies obtained from all six features in the healthy and dysarthric volunteers were 94.5% and 89.4%, respectively.
  • A CFD investigation of intra-aortic balloon pump assist ratio effects on
           aortic hemodynamics
    • Abstract: Publication date: Available online 3 December 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Maria Vittoria Caruso, Vera Gramigna, Gionata Fragomeni Intra-aortic balloon pump (IABP) is a mechanical circulatory support approach used in case of several cardiac diseases and a challenge of IABP therapy is the weaning process accomplished by decreasing the assist ratio. However, the impact of weaning on aortic hemodynamics on organs perfusions is not well known.Aim of this study was to evaluate and compare the global effects of IABP assistance frequencies on hemodynamics and perfusions in a patient-specific geometry by means of the computational fluid dynamics (CFD). A 3D aorta model was obtained from CT images using segmentation and reverse engineering techniques. The balloon was modeled and positioned in the descending aorta as in clinical practice and its inflation/deflation behavior was realized with a parametric study. Four assist ratios have been investigated: full assistance (1:1), partial assistances (1:2 and 1:3) and weak assistance (1:4). To perform the comparison, same boundary conditions were applied.Our results highlighted that the presence of balloon in aorta modifies significantly its hemodynamics and that the four assist ratios generate different perfusions in the human districts. Data suggested also that the biggest difference occurs between 1:2 and 1:3 frequencies and that 1:4 ratio is more suitable for the weaning of counterpulsation treatment than the 1:3 ratio.This first CFD analysis of IABP weaning increases information and knowledge on hemodynamics and organs perfusions.
  • The use of the Hellwig's method for feature selection in the detection of
           myeloma bone destruction based on radiographic images
    • Abstract: Publication date: Available online 3 December 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Zbigniew Omiotek, Olga Stepanchenko, Waldemar Wójcik, Wojciech Legieć, Małgorzata Szatkowska The radiological test is cost-effective, widely available, allows for the visualisation of large areas of the skeleton and can identify long bones potentially at risk for fractures in osteolysis sites. Therefore, radiology is often used in the early stages of multiple myeloma, in the detection and characterisation of complications, and in the assessment of the patient's response to treatment. The accuracy of this method can be improved through the use of appropriate algorithms of computer image processing and analysis. In the study, the feature vector based on humerus CR images was extracted. As a result of the analysis, 279 image descriptors were obtained. Hellwig's method in the selection process was applied. It found the set of feature combinations of the largest integral index of information capacity. To evaluate these combinations, 11 classifiers were built and tested. As a result, 2 feature sets were identified that provided the highest classification accuracy in combination with the K-NN classifier. The 9-NN classifier for the first combination (2 features) was used and 5-NN for the second one (3 features). The classification accuracy (depending on the quality index used) was as follows: overall classification accuracy – 93%, classification sensitivity – 92%, classification specificity – 96%, positive predictive value – 96% and negative predictive value – 93%. Results show that: (1) the use of humerus CR images may be useful in the detection of bone damages caused by multiple myeloma; (2) the Hellwig's method is effective in the feature selection of the analysed kind of images.
  • Automated detection of the preseizure state in EEG signal using neural
    • Abstract: Publication date: Available online 3 December 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): C. Sudalaimani, N. Sivakumaran, Thomas T. Elizabeth, Valsalam S. Rominus The life-threatening neural syndrome epilepsy is elicited by seizure which affects over 50 million people in the universal. A seizure is a brain condition made by excessive, unusual exoneration by nerve cells of the brain. Contemporary seizure forecast research works exhibited worthy results in both undersized and lengthy electroencephalography (EEG) signal; however it is essential to formulate superior epileptic seizure forecast system; that shall be steady, constant and less resource intensive for effectively employed to heading for evolving a convenient and easily manageable ictal or seizure forewarning prearrangement or devices. Based on our exploration, we have found a novel seizure prediction method which we evaluated by producing ten sub-frequency EEG data from initially recorded signal. Simple, robust and computationally less-intense EEG characteristics are mined using the generated sub-frequency signals and applied the extracted features to computationally less intense generalized regression neural network (GRNN) to segregate EEG signal clips into normal or preseizure files. In this research work, we have engendered 10 sub-frequency bands of signals from original EEG recordings, extracted various meaningful features from those sub-frequency band signals, created 10 GRNN neural networks to categorize feature files as normal or preseizure, and then applied post-processing techniques with 10 thresholding mechanisms to each classifier output. As such, we determined that seizure forewarning may function better in various sub-frequency bands for many patients in a subject-specific manner. We also found that epileptic-seizure forecast performed superior at ‘60 Hz high pass’ filtered sub-frequency band EEG signal for all subjects or canines data.
  • A novel method to design an electro-kinetic platform based on
           complementary metal-oxide semiconductor technology using SKILL scripting
           of cadence
    • Abstract: Publication date: Available online 3 December 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Reda Abdelbaset, Yehya H. Ghallab, Yehea Ismail The dielectrophoresis (DEP) is the motion of polarizable particles which is a result of the interaction between a non-uniform electric field and the induced dipole moment of these particles. The electro-kinetic DEP is a widely used technique for biological cells’ manipulation, characterization and separation. The electro-kinetic DEP consists of three major configurations, they are; traveling wave dielectrophoresis (twDEP), electro-rotation dielectrophoresis (rotDEP), and levitation (levDEP). In this paper, a design of electrokinetic platform that includes the three electrokinetic configurations is presented and discussed. The design of the electrokinetic platform is implemented and simulated using 130 nm complementary metal-oxide-semiconductor (CMOS) technology. Also, this paper presents a developed technique to design the electrokinetic platform's electrodes. This developed technique is the usage of SKILL scripting of cadence (SSC) language. CMOS is a technology which is used to fabricate integrated circuits (IC). SKILL is a scripting language which supports the automation of a specific layout design by commands. The layout of electrokinetic DEP platform is developed using SSC. The performance of the developed electrokinetic platform using SSC versus the platforms based on the other traditional techniques is presented and evaluated using COMSOL Multiphysics®.
  • Primary stenosis progression versus secondary stenosis formation in the
           left coronary bifurcation: A mechanical point of view
    • Abstract: Publication date: Available online 29 November 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Reza Jahromi, Hossein Ali Pakravan, Mohammad Said Saidi, Bahar Firoozabadi Biomechanical forces and hemodynamic factors influence the blood flow and the endothelial cells (ECs) morphology. These factors behave differently beyond the coronary artery stenosis. In the present study, unsteady blood flow in the left coronary artery (LCA) and its atherosclerotic bifurcating vessels, left anterior descending (LAD) and left circumflex (LCX) arteries, were numerically simulated to investigate the risk of plaque length development and secondary plaque formation in the post-stenotic areas. Using fluid–structure interaction (FSI) model, compliance of arterial wall and vessel curvature variations due to cardiac motion were considered. The arteries included plaques at the beginning of the bifurcation. Stenosis degree varied from 40% to 70% based on diameter reduction. Healthy coronary artery was also reconstructed to compare with the atherosclerotic arteries. Circumferential and longitudinal strains of ECs as well as wall shear stress (WSS) were computed in different locations downstream of the stenosis. It was concluded that the most critical regions experiencing low circumferential strain and low WSS were located proximal to the plaque throat, and the effects of these parameters intensified by stenosis degree. The results proposed that primary plaque length progression is more probable than secondary plaque formation distal to the stenosis when the stenosis degree increases.
  • A new framework using deep auto-encoder and energy spectral density for
           medical waveform data classification and processing
    • Abstract: Publication date: Available online 28 November 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Ahmad M. Karim, Mehmet S. Güzel, Mehmet R. Tolun, Hilal Kaya, Fatih V. Çelebi This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks.In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved.
  • Adaptive shrinkage on dual-tree complex wavelet transform for denoising
           real-time MR images
    • Abstract: Publication date: Available online 17 November 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): V.R. Simi, Damodar Reddy Edla Performance of denoising filters which are based on the principle of wavelet thresholding greatly depends upon selection of the threshold value. An objective method is proposed in this paper for computing the optimum value of threshold in DTCWT based denoising. At optimum threshold, annoying intensity transitions of pixels in the homogeneous regions of the images, contributed by noise get completely suppressed and the true edges remain unaffected. For finding optimum value of threshold a newly derived quality metric termed as Optimum Denoising Index (ODI), which quantifies both the edge-preservation and smoothing of homogeneous regions is used. The ODI values corresponding to mean, median, Gaussian, Wiener, Bilateral, Kuwahara filters and wavelet thresholding are 0.1192 ± 0.0118, 0.2196 ± 0.0125, 0.1283 ± 0.0118, 0.2106 ± 0.0145, 0.1590 ± 0.0331, 0.2200 ± 0.0101 and 0.2516 ± 0.0094, respectively. The wavelet thresholding has better edge-preservation and denoising capacity than the said denoising schemes. The ODI is highly correlated with its existing alternatives like Peak Signal to Noise Ratio (PSNR) and Structured Similarity Index Metric (SSIM) with values 0.9165 ± 0.0536 and 0.9050 ± 0.0452 respectively. This shows ODI is a good alternative to PSNR and SSIM.
  • Chemotherapy-induced fatigue estimation using hidden Markov model
    • Abstract: Publication date: Available online 15 November 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Sina Ameli, Fazel Naghdy, David Stirling, Golshah Naghdy, Morteza Aghmesheh Chemotherapy-induced fatigue undermines the physical performance and alter gait behaviour of patients. In routine clinical oncology, there is not a well-established method to objectively assess the effects of chemotherapy-induced fatigue on gait characteristics. Clinical trials commonly use 6-min walking tests (6MWT) to assess the gait of patients. However, these studies only measure the distance that a patient can walk. The distance does not provide comprehensive information about variations in ambulatory motion characteristics and body postural behaviour which can more appropriately describe the fatigue effects on general physical performance. Gait characteristics provide a manifestation of relationships between muscular and cardiovascular fitness status and physical motions. Hence, an assessment of gait characteristics provides more appropriate information about the effects of chemotherapy-induced fatigue on gait behaviour and also general physical performance of patients. A novel approach is proposed to objectively assess the impacts of chemotherapy-induced fatigue on cancer gait by analysing the gait characteristics during 6MWT. The joint angles of the lower body segments are measured by a set of inertial sensors and modelled through a hidden Markov model (HMM) with Gaussian emissions. A Gaussian clustering method classifies the joint angles of first gait cycle to determine the six gait phases of a normal gait as initial training values. A comparison of gait characteristics before and after chemotherapy-induced fatigue determines the gait abnormalities. The method is applied to four cancer patients and outcomes are benchmarked against the gait of a healthy subject before and after running program-induced fatigue. The results indicate a more accurate quantitative-based tool to measure the effects of chemotherapy-induce fatigue on gait and physical performance.
  • Heart rate extraction from PPG signals using variational mode
    • Abstract: Publication date: Available online 14 November 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Hemant Sharma Monitoring of vital signs using the photoplethysmography (PPG) signal is desirable for the development of home-based healthcare systems in the aspect of feasibility, mobility, comfort, and cost-effectiveness of the PPG device. In this paper, a new technique based on the variational mode decomposition (VMD) for estimating heart rate (HR) from the PPG signal is proposed. The VMD decomposes an input PPG signal into a number of modes or sub-signals. Afterward, the modes which are dominantly influenced by the HR information are selected and further processed for extracting HR of the patient. The proposed scheme is validated over a large number of recordings acquired from three independent databases, namely the Capnobase, MIMIC, and University of Queens Vital Sign (UQVS). Experiments are performed over different data length segments of the PPG recordings. Using the data length of 30 s, the proposed technique outperformed the existing techniques by achieving the lower median (1st quartile, 3rd quartile) values of root mean square error (RMSE) as 0.23 (0.19, 0.31) beats per minute (bpm), 0.41 (0.31, 0.56) bpm and 1.1 (0.9, 1.22) bpm for the Capnobase, MIMIC, and UQVS datasets, respectively. Since the shorter data length is more suitable for the clinical applications, the proposed technique also provided satisfactory agreement between the derived and reference HR values for the shorter data length segments. Performance results over three independent datasets suggest that the proposed technique can provide accurate and reliable HR information using the PPG signal recorded from the patients suffering from dissimilar problems.
  • Support vector machine classification of brain states exposed to social
           stress test using EEG-based brain network measures
    • Abstract: Publication date: Available online 12 November 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Saeed Lotfan, Shima Shahyad, Reza Khosrowabadi, Alireza Mohammadi, Boshra Hatef Stress is one of the most significant health problems in the 21st century, and should be dealt with due to the costs of primary and secondary cares of stress-associated psychological and psychiatric problems. In this study, the brain network states exposed to stress were monitored based on electroencephalography (EEG) measures extracted by complex network analysis. To this regard, 23 healthy male participants aged 18–28 were exposed to a stress test. EEG data and salivary cortisol level were recorded for three different conditions including before, right after, and 20 min after exposure to stress. Then, synchronization likelihood (SL) was calculated for the set of EEG data to construct complex networks, which are scale reduced datasets acquired from multi-channel signals. These networks with weighted connectivity matrices were constructed based on original EEG data and also by using four different waves of the recorded signals including δ, θ, α, and β. In addition to these networks with weighted connectivity, networks with binary connectivity matrices were also derived using threshold T. For each constructed network, four measures including transitivity, modularity, characteristic path length, and global efficiency were calculated. To select the sensitive optimal features from the set of the calculated measures, compensation distance evaluation technique (CDET) was applied. Finally, multi-class support vector machine (SVM) was trained in order to classify the brain network states. The results of testing the SVM models showed that the features based on the original EEG, α and β waves have got better performances in monitoring the brain network states.
  • Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for
           epileptic seizure detection
    • Abstract: Publication date: Available online 6 November 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Shamzin Mamli, Hashem Kalbkhani Epilepsy is a brain disorder that many persons of different ages in the world suffer from it. According to the world health organization, epilepsy is characterized by repetitive seizures and more electrical discharge in a group of brain neurons results in sudden physical actions. The aim of this paper is to introduce a new method to classify epileptic phases based on Fourier synchro-squeezed transform (FSST) of electroencephalogram (EEG) signals. FSST is a time-frequency (TF) analysis and provides sharper TF estimates than the conventional short-time Fourier transform (STFT). Absolute of FSST of EEG signal is computed and segmented into five non-overlapping frequency sub-bands as delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ). Each sub-band is considered as a gray-scale image and then we propose to obtain the gray-level co-occurrence matrix (GLCM) of each sub-band as features. We concatenate the features of different sub-bands to obtain the final feature vector. After selecting informative features by infinite latent feature selection (ILFS) method, the support vector machine (SVM) and K-nearest neighbor (KNN) classifiers are used separately to classify EEG signals. We use the EEG signals from Bonn University database and different combinations of its sets are considered. Simulation results show that the proposed method efficiently classifies the EEG signals and can be used to determine the phase of epilepsy.
  • Computer-aided detection of mesial temporal sclerosis based on hippocampus
           and cerebrospinal fluid features in MR images
    • Abstract: Publication date: Available online 28 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Huiquan Wang, S. Nizam Ahmed, Mrinal Mandal Mesial temporal sclerosis (MTS) is the commonest brain abnormalities in patients with intractable epilepsy. Its diagnosis is usually performed by neuroradiologists based on visual inspection of magnetic resonance imaging (MRI) scans, which is a subjective and time-consuming process with inter-observer variability. In order to expedite the identification of MTS, an automated computer-aided method based on brain MRI characteristics is proposed in this paper. It includes brain segmentation and hippocampus extraction followed by calculating features of both hippocampus and its surrounding cerebrospinal fluid. After that, support vector machines are applied to the generated features to identify patients with MTS from those without MTS. The proposed technique is developed and evaluated on a data set comprising 15 normal controls, 18 left and 18 right MTS patients. Experimental results show that subjects are correctly classified using the proposed classifiers with an accuracy of 0.94 for both left and right MTS detection. Overall, the proposed method could identify MTS in brain MR images and show a promising performance, thus showing its potential clinical utility.
  • Accurate automated detection of congestive heart failure using eigenvalue
           decomposition based features extracted from HRV signals
    • Abstract: Publication date: Available online 19 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Rishi Raj Sharma, Ashish Kumar, Ram Bilas Pachori, U. Rajendra Acharya Congestive heart failure (CHF) is a cardiac abnormality in which heart is not able to pump sufficient blood to meet the requirement of all the parts of the body. This study aims to diagnose the CHF accurately using heart rate variability (HRV) signals. The HRV signals are non-stationary and nonlinear in nature. We have used eigenvalue decomposition of Hankel matrix (EVDHM) method to analyze the HRV signals. The lowest frequency component (LFC) and the highest frequency component (HFC) are extracted from the eigenvalue decomposed components of HRV signals. After that, the mean and standard deviation in time domain, mean frequency calculated from Fourier-Bessel series expansion, k-nearest neighbor (k-NN) entropy, and correntropy features are evaluated from the decomposed components. The ranked features based on t-value are fed to least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel for automated diagnosis of CHF HRV signals. The study is performed on three normal datasets and two CHF datasets. Our proposed system has yielded an accuracy of 93.33%, sensitivity of 91.41%, and specificity of 94.90% using 500 HRV samples. The automated toolkit can aid cardiac physicians in the accurate diagnosis of CHF patients to confirm their findings with our system. Hence, it will help to provide timely treatment for CHF patients and save life.
  • Magnetic resonance imaging-based brain tumor grades classification and
           grading via convolutional neural networks and genetic algorithms
    • Abstract: Publication date: Available online 18 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Amin Kabir Anaraki, Moosa Ayati, Foad Kazemi Gliomas are the most common type of primary brain tumors in adults and their early detection is of great importance. In this paper, a method based on convolutional neural networks (CNNs) and genetic algorithm (GA) is proposed in order to noninvasively classify different grades of Glioma using magnetic resonance imaging (MRI). In the proposed method, the architecture (structure) of the CNN is evolved using GA, unlike existing methods of selecting a deep neural network architecture which are usually based on trial and error or by adopting predefined common structures. Furthermore, to decrease the variance of prediction error, bagging as an ensemble algorithm is utilized on the best model evolved by the GA. To briefly mention the results, in one case study, 90.9 percent accuracy for classifying three Glioma grades was obtained. In another case study, Glioma, Meningioma, and Pituitary tumor types were classified with 94.2 percent accuracy. The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images. Due to the flexible nature of the method, it can be readily used in practice for assisting the doctor to diagnose brain tumors in an early stage.
  • Design factors of lumbar pedicle screws under bending load: A finite
           element analysis
    • Abstract: Publication date: Available online 12 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Jayanta Kumar Biswas, Tikeshwar Prasad Sahu, Masud Rana, Sandipan Roy, Santanu Kumar Karmakar, Santanu Majumder, Amit Roychowdhury Loosening and breakage of lumbar pedicle screw are the most common complications affecting the spinal stability. The design factors of the pedicle screw that may affect the fixation strength under bending load are pitch length, major diameter, thread profiles and geometry.In this study, 84 finite element (FE) models of the pedicle screw were generated having 7 pitch lengths, 3 major diameters, 2 thread profiles and 2 geometries. The assembly of pedicle screw and CT scan based half section FE model of 4th lumbar vertebra was loaded with a 200 N force on the screw head which is equivalent to a bending moment of 11 Nm.With triangular thread profile and cylindrical geometry, for 300% increase in pitch length (1 mm to 4 mm), von Mises stress in screw and von Mises strain in bone increased by 65% and 117% respectively, for a 26% decrease in major diameter (7.6 mm to 5.6 mm) and correlations were proposed among screw stress (r2 = 0.992) or bone strain (r2 = 0.986), pitch length and major diameter. Similar correlations were also proposed for trapezoidal thread profile and tapered geometry (r2 = 0.994 for screw stress and r2 = 0.986 for bone strain).Hence, a combination of tapered pedicle screw with lower pitch length, higher diameter and trapezoidal thread profile may serve better under bending load for lumbar vertebral implant.
  • Detection of type-2 diabetes using characteristics of toe
           photoplethysmogram by applying support vector machine
    • Abstract: Publication date: Available online 12 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Neelamshobha Nirala, R. Periyasamy, B.K. Singh, Awanish Kumar Diabetes mellitus (DM) is one of the most widespread and rapidly growing diseases. With its advancement, DM-related complications are also increasing. We used characteristic features of toe photoplethysmogram for the detection of type-2 DM using support vector machine (SVM). We collected toe PPG signal, from 58 healthy and 83 type-2 DM subjects. From each PPG signal 37 different features were extracted for further classification. To improve the performance of SVM and reduce the noisy data we employed hybrid feature selection technique that reduces the feature set of 37 to 10 on the basis of majority voting. Using 10 selected features set, we gained an accuracy of 97.87%, sensitivity of 98.78% and specificity of 96.61%. Further for the validation of our method we need to do random population test, so that it can be used as a non-invasive screening tool. Photoplethysmogram is an economic, technically easy and completely non-invasive method for both physician and subject. With the high accuracy that we obtained, we hope that our work will help the clinician in screening of diabetes and adopting suitable treatment plan for preventing end organ damage.
  • Assessment of despeckle filtering algorithms for segmentation of breast
           tumours from ultrasound images
    • Abstract: Publication date: Available online 12 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Kriti, Jitendra Virmani, Ravinder Agarwal In the present work, the breast ultrasound images are pre-processed with various despeckle filtering algorithms to analyze the effect of despeckling on segmentation of benign and malignant breast tumours from ultrasound images. The despeckle filtering algorithms are broadly classified into eight categories namely local statistics based filters, fuzzy filters, Fourier filters, multiscale filters, non-linear iterative filters, total variation filters, non-local mean filters and hybrid filters. Total 100 breast ultrasound images (40 benign and 60 malignant) are processed using 42 despeckle filtering algorithms. A despeckling filter is considered to be appropriate if it preserves edges and features/structures of the image. Edge preservation capability of a despeckling filter is measured by beta metric (β) and feature/structure preservation capability is quantified using image quality index (IQI). It is observed that out of 42 filters, six filters namely Lee Sigma, FI, FB, HFB, BayesShrink and DPAD yield more clinically acceptable images in terms of edge and feature/structure preservation. The qualitative assessment of these images has been done on the basis of grades provided by the participating experienced radiologist. The pre-processed images are then fed to a segmentation module for segmenting the benign or malignant tumours from ultrasound images. The performance assessment of segmentation algorithm has been done quantitatively using the Jaccard index. The results of both quantitative and qualitative assessment by the radiologist indicate that the DPAD despeckle filtering algorithm yields more clinically acceptable images and results in better segmentation of benign and malignant tumours from breast ultrasound images.
  • Granular filter in medical image noise suppression and edge preservation
    • Abstract: Publication date: Available online 6 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Wieclawek Wojciech, Pietka Ewa An alternative non-linear filtering technique for medical image denoising while preserving edge is introduced. Two different variants of the approach i.e. crisp and fuzzy are developed. The solution is demonstrated based on US breast images as well as CT studies and gave promising results in comparison with commonly known and popular filtering techniques (i.e. spatial averaging and median, bilateral filter, anisotropic diffusion). Many different measures were used to evaluate the method. There are pixel-to-pixel error measures, structural information factors and edge preservation measures. The benefits are noticeable in all three categories.
  • Detection of Modic changes in MR images of spine using local binary
    • Abstract: Publication date: Available online 3 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Jiyo S. Athertya, G. Saravana Kumar, Jayaraj Govindaraj Background and objectiveWith increase in prevalence of lower back pain, fast and reliable computer aided methods for clinical diagnosis associated with the same is needed for improving the healthcare reach. The magnetic resonance images exhibit a change in signal intensity on the vertebral body close to end plates, which are termed as Modic changes (MC), and are known to be clear indicators of lower back pain. The current work deals with computer aided methods for automating the classification of signal changes between normal and degenerate cases so as to aid physicians in precise and suitable diagnosis for the ailment.MethodsIn order to detect Modic changes in vertebrae, initially the vertebrae are segmented from sagittal MR T1 and T2 imaged using a semi automatic cellular automata based segmentation. This is followed by textural feature extraction using Local Binary Patterns (LBP) and its variants. Various classifiers based on machine learning approaches using Random Forest, kNN, Bayes and SVM were evaluated for its classification performance. Since medical image dataset in general have bias towards healthy and diseased state, data augmentation techniques were also employed.ResultsThe implemented method is tested and validated over a dataset containing 100 patients. The proposed framework achieves an accuracy of 81% and 91.7% with and without augmentation of data respectively. A comparative study with the state of art methods reported in literature shows that the method proposed in better in terms of computational cost without any compromise on classification accuracy.ConclusionA novel approach to identify MC in vertebrae by exploiting textural features is proposed. This shall assist radiologists in detecting abnormalities and in treatment planning.
  • Geometrical parameters of the mandible in 3D CBCT imaging
    • Abstract: Publication date: Available online 1 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): A.M. Ryniewicz, W. Ryniewicz, Ł. Bojko The aim of the study is to report on a method for the measurement and analysis of the accuracy in mapping the shape of the mandible spatially modeled based on cone beam imaging. To achieve this goal, the geometrical determinants of the mandible shape were identified; in addition, the accuracy of their cone beam in computer tomography (CBCT) images was verified. The latter – verification of images – was based on reference measures made by a coordinate measuring machine (CMM). The parameters that were analyzed were: Bonville's triangle, the occlusal plane, and the mandible symmetry in relation to the sagittal plane. Descriptive statistics and distribution of individual variables were determined. The results were analyzed statistically using the U-Mann Whitney test. The geometrical parameters of the mandible determined on the basis of CBCT and CMM imaging do not differ significantly.
  • Automatic method for assessment of proliferation index in digital images
           of DLBCL tissue section
    • Abstract: Publication date: Available online 29 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Ryszard S. Gomolka, Anna Korzynska, Krzysztof Siemion, Karolina Gabor-Siatkowska, Wlodzimierz Klonowski Diffuse large B-cell lymphoma (DLBCL) is a fast-growing and aggressive neoplasm originating from B lymphocytes. Evaluation of proliferation index (PI) based on Ki67 immunohistochemical nuclear staining is used to distinguish proliferating (immunopositive) from nonproliferating (immunonegative) lymphoma cells. Human interpretation of PI varies and is time-consuming, therefore automatic computer-assisted approach may facilitate the performance.Herein we propose a new fully automatic proliferation index estimation (FLAPIE) algorithm, dedicated to detection of immunopositive and immunonegative nuclei, and evaluation of PI in digital microscopy images of DAB&H-stained samples from patients with high-grade DLBCL.FLAPIE performs nuclei detection in original RGB colour space and is independent of image brightness due to its textural-statistical approach. Validation of FLAPIE was performed in 61 non-overlapping whole-slide image fragments and compared to the results of PI estimation by QuPath open-source software, MetPiKi algorithm and manual evaluation by two independent observers. Interobserver agreement was calculated between the nuclei count and PIs by two observers.High concordance was found between both DAB and H-stained nuclei count, and PIs by two observers. Compared to MetPiKi, FLAPIE presented improved results of DAB and H-stained nuclei detection. In contrary to MetPiKi and QuPath, FLAPIE performed nuclei detection in all images and its results closely matched the number of DAB-stained nuclei evaluated by two observers. No significant difference was found between PIs by all computational methods and observers.FLAPIE achieved good results in PI estimation and prospectively aims to serve as a tool for clinical application in support of patients selection and decision to treatment.
  • Extraction of fuzzy rules at different concept levels related to image
           features of mammography for diagnosis of breast cancer
    • Abstract: Publication date: Available online 27 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Mahsa Goudarzi, Keivan Maghooli Mammography is an inexpensive and non-invasive method through which one can diagnose breast cancer in its early stages. As these images need interpretation by a radiologist, this may develop some problems due to fatigue, repetition, and need for a great deal of attention to details and other factors. Thus, a method capable of diagnosing breast cancer should be employed to help physicians in this regard.In this paper, The mini Mammographic Image Analysis Society (mini-MIAS) database of mammograms is used. The aim is to distinguish between normal and abnormal classes. In the preprocessing stage, noise removal, removal of labels of images, heightening the contrast, and ROI segmentation are performed, and then compactness, entropy, mean, and smoothness are extracted from the images. In addition to classification, we have come to a new approach in order to create a complete knowledge base, which then we use this knowledge base for classification. We have a comprehensive knowledge base which covers all the conceptual levels.The extracted features are referred to as fuzzy classifiers through the look-up table method. And, for evaluation of the results, the 10-fold method is used. Discretization operations are performed on training data across 2, 3, and 4 levels to develop concept hierarchy. Concept hierarchies reduce the data by replacing low-level concepts with higher-level concepts and the outcome is more meaningful and easier to interpret. Eventually, Bagging algorithm is used for finding out the majority vote and the final result of the discretization levels. The obtained accuracy is 89.37 ± 6.62.
  • Statistical methods for constructing gestational age-related charts for
           fetal size and pregnancy dating using longitudinal data
    • Abstract: Publication date: Available online 18 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Martin Hynek, Jan Kalina, Jana Zvárová, Jeffrey D. Long The assessment of fetal size and the accurate estimation of gestational age are of crucial importance for proper pregnancy management. The information is almost exclusively based on ultrasound measurements of fetal biometric parameters and the means for evaluating these measurements are age-related reference charts (centile charts) allowing interpretation of obtained fetal measurement in comparison with the expected average measurement in the reference population. The construction of such reference charts requires an appropriate statistical methodology. The most frequent method for the construction of fetal reference charts from cross-sectional data is the parametric approach with fractional polynomials regression functions for the mean and standard deviation of each fetal measurement. This article suggests how this method can be extended to longitudinal data using fractional polynomials in linear mixed effect regression. The presented approach includes maximum likelihood estimation for fitting first- and second-order fractional polynomial models, and multimodel inference using Akaike's information criterion and related tools as a suitable strategy for model selection. Finally, an example of the suggested approach is presented.
  • Evaluation of filters over different stimulation models in evoked
    • Abstract: Publication date: Available online 17 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Ayşegül Güven, Turgay Batbat Filtering is a key process which removes unwanted parts of signals. During signal recording, various forms of noises distort data. Physiological signals are highly noise sensitive and to evaluate them powerful filtering approaches must be applied. The aim of this study is to compare modern filtering approaches on scalp signals. Brain activities were generally examined by brain signals like EEG and evoked potentials (EP). In this study, data were recorded from university students whose age between 18 and 25 years with visual and auditory stimuli. Discrete wavelet transforms, singular spectrum analysis, empirical mode decomposition and discrete Fourier transform based filters were used and compared with raw data on classification performance. Higuchi fractal dimension and entropy features were extracted from EEG; P300 features were extracted from EP signals. Classification was applied with support vector machines. All filtered data gave better scores than raw data. Empirical mode decomposition (EMD) and Fourier-based filter yielded lower results than the discrete wavelet-based filter. Singular spectrum analysis gave the best result at 84.32%. The current study suggests that singular spectrum analysis removes noise from sensitive physiological signals, and EMD requires new mode selection procedures before resynthesizing.
  • Fuzzy genetic-based noise removal filter for digital panoramic X-ray
    • Abstract: Publication date: Available online 5 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Mehravar Rafati, Fateme Farnia, Mahdi Erfanian Taghvaei, Ali Mohammad Nickfarjam This paper proposed a novel fuzzy genetic-based noise removal filter and surveyed the gain of popular filters for noise removal in the digital orthopantomography (OPG) images. The proposed filter is a non-invasive technique for attaining sub-clinical information from the areas of interest in each tooth, both jaws and maxillofacial.The proposed Poisson removal filter combines 4th-order partial differential equations (PDE), total variation (TV) and Bayes shrink threshold accompanied by fuzzy genetic algorithm (FGA) and the exact unbiased inverse of generalized Anscombe transformation (EUIGAT). Experiments were performed in order to show the effect of noise removal filters on 110 simulated, 106 phantom and 104 panoramic radiographic images for subjects (aged 30–60 years old, 50 males and 54 females). Various noises degraded filters and Canny edge detection was performed separately in three kinds of images. The program measured mean square error (MSE), peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index metric (SSIM) and figure of merit (FOM).The results verify that the proposed filter enhances physicians’ and dentists’ skill of diagnosing normal and pathological events in the teeth, jaws, temporomandibular joint (TMJ) regions and changeable anatomical panoramic landmarks related to osteoporosis progress in the mandible bone using noise removal and improving images quality. Experimental results show the superiority of this filter over other noise removal filters.
  • A hybrid gene selection method for microarray recognition
    • Abstract: Publication date: Available online 5 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Alok Kumar Shukla, Pradeep Singh, Manu Vardhan DNA microarray data is expected to be a great help in the development of efficient diagnosis and tumor classification. However, due to the small number of instances compared to a large number of genes, many of the computational learning methods encounter difficulties to select the low subgroups. In order to select significant genes from the high dimensional data for tumor classification, nowadays, several researchers are exploring microarray data using various gene selection methods. However, there is no agreement between existing gene selection techniques that produce the relevant gene subsets by which it improves the classification accuracy. This motivates us to invent a new hybrid gene selection method which helps to eliminate the misleading genes and classify a disease correctly in less computational time. The proposed method composes of two-stage, in the first stage, EGS method using multi-layer approach and f-score approach is applied to filter the noisy and redundant genes from the dataset. In the second stage, adaptive genetic algorithm (AGA) work as a wrapper to identify significant genes subsets from the reduced datasets produced by EGS that can contribute to detect cancer or tumor. AGA algorithm uses the support vector machine (SVM) and Naïve Bayes (NB) classifier as a fitness function to select the highly discriminating genes and to maximize the classification accuracy. The experimental results show that the proposed framework provides additional support to a significant reduction of cardinality and outperforms the state-of-art gene selection methods regarding accuracy and an optimal number of genes.
  • Towards in-vivo assessment of fluorescence lifetime: Imaging using
           time-gated intensified CCD camera
    • Abstract: Publication date: Available online 3 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Piotr Sawosz, Stanislaw Wojtkiewicz, Michal Kacprzak, Elzbieta Zieminska, Magdalena Morawiec, Roman Maniewski, Adam Liebert A novel technique for imaging of a small animal with application of time-gated intensified CCD camera was proposed. The time-resolved method based on emission of picosecond light pulses and detection of the light penetrating in tissues was applied. In this technique, the fluorescence photons, excited in the dye circulating in the tissue, that diffusely penetrate in the optically turbid medium are detected. The data acquired during measurements carried out on a rat was analyzed in order to estimate fluorescence lifetime which depends strongly on the environment in which the dye is distributed. In the lifetime estimation a special emphasis was put on compensation of influence of the instrumental response function of the setup on the measured quantity. The proposed optical system was validated in series phantom experiments, in which estimates of fluorescence lifetime of inclusions containing indocyanine green (ICG) were obtained. ICG is a dye revealing florescence properties in near-infrared wavelength region. Images of the estimate of fluorescence lifetime of the ICG accumulated in tissues of a rat were successfully acquired around six circular spots of illumination of the diameter of 6 mm. Larger lifetime values were observed in lung/heart region of the animal. Aspect of sampling rate of the fluorescence lifetime images optimization was finally discussed.
  • Fast statistical model-based classification of epileptic EEG signals
    • Abstract: Publication date: Available online 21 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Antonio Quintero-Rincón, Marcelo Pereyra, Carlos D’Giano, Marcelo Risk, Hadj Batatia This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.
  • Extracting tumor in MR brain and breast image with Kapur’s entropy based
           Cuckoo Search Optimization and morphological reconstruction filters
    • Abstract: Publication date: Available online 20 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): R. Sumathi, M. Venkatesuslu, Sridhar P. Arjunan
  • Automated fuzzy optic disc detection algorithm using branching of vessels
           and color properties in fundus images
    • Abstract: Publication date: Available online 20 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Mehmet Nergiz, Mehmet Akın, Abdulnasır Yıldız, Ömer Takeş Optic disc (OD) detection is a basic procedure for the image processing algorithms which intend to diagnose and track retinal disorders. In this study, a new OD localization approach is proposed, based on color and shape properties of OD as well as the convergence point of the main vessels. This study is comprised of two successive fundamental steps. At the first step, an algorithm finding the approximate convergent point of the vessels is used in order to roughly localize OD. At the second step, three new features are suggested and a fuzzy logic controller (FLC) whose input membership functions are designed based on these features is proposed. The proposed method is applied to the DRIVE, STARE, DIARETDB0 and DIRETDB1 datasets and the obtained results validate the improvement in the performance by attaining success rate of 100%, 91,35%, 90% and 100% respectively and detecting OD centers and contours precisely in a reasonable execution time.
  • Eye and EEG activity markers for visual comfort level of images
    • Abstract: Publication date: Available online 13 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Vytautas Abromavičius, Artūras Serackis Depth perception by binocular cues is based on the matching of image features from one retina with corresponding elements from the second retina. However, high disparities are related to the higher visual discomfort levels and may cause the eye fatigue during extended stereoscopic perception time. The goal of the investigation was to find a set of measurable features for stereoscopic image visual comfort level prediction. The investigation involved gaze, pupillometric and EEG data from 28 subjects who evaluated visual comfort level of 120 stereoscopic images. Six different time frame windows were used to analyze four measured features: the number of focus points; the dynamics of pupil size; disparity level at the focus points; the activity of EEG bands at the frontal lobe. A significant difference was found in all investigated stereoscopic image groups. 2-s and 5-s pre-DPI window showed best results for the selected feature sets. The higher disparity at the focus points, lower number of focus points are related to the lower levels of visual comfort. However, features such as the number of focus points, the pupil size and the disparity level for the images with lowest visual comfort scores showed similar results to the images scored as “comfortable” or “very comfortable”.
  • A miniature and low-cost glucose measurement system
    • Abstract: Publication date: Available online 8 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): S.D. Adams, E. Buber, T.C. Bicak, Y. Yagci, L. Toppare, A. Kaynak, A.Z. Kouzani One of the bottlenecks in widespread adoption of biosensors is the large and sophisticated bioanalytical system that is required to perform signal transduction and analysis. A miniaturized bioanalytical system facilitates biosensing techniques that are portable, easy to handle and inexpensive for fast and reliable measurements of biochemical species. Thus, downscaling the bioanalytical system has become a highly active research area, significantly assisted by recent advances in the microelectronics technology. In this work, a miniaturized system is designed and implemented for amperometric detection, and subsequently tested with a glucose biosensor based on the one-step approach utilizing water soluble poly(o-aminophenol). Several experiments are conducted to assess the viability of this system including calibration, interference and application tests. The results are compared with the previously published work performed using the same biosensor tested with a commercial potentiostat in order to verify the applicability of the designed system.
  • RASIT: Region shrinking based accurate segmentation of inflammatory areas
           from thermograms
    • Abstract: Publication date: Available online 3 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Bardhana Shawli, Kanti Bhowmika Mrinal, Debnatha Tathagata, Debotosh Bhattacharjee Effective segmentation of thermal images reflecting the inflamed region in human body to assist medical diagnosis is a challenging task. In this paper we propose a method for thermal image segmentation, named as “Region shrinking based Accurate Segmentation of Inflammatory areas from Thermograms”, in short RASIT. The method comprising of four steps encompassing thermal image contextual electrostatic force extraction, intensity adjustment as applicable, automated generation of the weighted threshold, and segmentation of thermograms based on the computed threshold. The proposed method is operative devoid of the subjective and possibly questionable task of parameter selection clearly offering an edge over the state-of-the-art methods in terms of usage. The efficacy of our proposed technique is shown by experimenting on abnormal thermograms taken from two datasets: one is newly created knee arthritis thermogram dataset and another is online available Database of Mastology Research (DMR) of breast thermograms. The averages on correct detection rates obtained by the proposed method for both the knee and breast thermograms are 98.2% and 96.98% respectively with favorable inference on basis of Wilcoxon’s test. Application of the proposed method minimizes the complexity of parameter selection, time complexity of execution and amount of under segmentation compared to existing state-of-the-art methods of thermogram segmentation.
  • Modeling the 2D space of emotions based on the poincare plot of heart rate
           variability signal
    • Abstract: Publication date: Available online 3 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Sadaf Moharreri, Nader Jafarnia Dabanloo, Keivan Maghooli Emotions mean accepting, understanding, and recognizing something with one's senses. The physiological signals generated from the internal organs of the body can objectively and realistically reflect changes in real-time human emotions and monitor the state of the body. In this study, the two-dimensional space-based emotion model was introduced on the basis of Poincare's two-dimensional plot of the signal of heart rate variability. Four main colors of psychology, blue, red, green, and yellow were used as a stimulant of emotion, and the ECG signals from 70 female students were recorded. Using extracted features of Poincare plot and heart rate asymmetry, two tree based models estimated the levels of arousal and valence with 0.05 mean square errors, determined an appropriate estimation of these two parameters of emotion. In the next stage of the study, four different emotions mean pleasure, anger, joy, and sadness, were classified using IF-THEN rules with the accuracy of 95.71%. The results show the color red is associated with more excitement and anger, while green has small anxiety. So, this system provides a measure for numerical comparison of mental states and makes it possible to model emotions for interacting with the computer and control mental states independently of the pharmaceutical methods.
  • Formulation and statistical evaluation of an automated algorithm for
           locating small bowel tumours in wireless capsule endoscopy
    • Abstract: Publication date: Available online 31 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): A. Jagadeesan, J. Sivaraman Wireless capsule endoscopy (WCE) is an imaging modality which is highly reliable in the diagnosis of small bowel tumors. But locating the frames carrying tumors manually from the lengthy WCE is cumbersome and time consuming. A simple algorithm for the automated detection of tumorous frames from WCE is proposed in this work. In the proposed algorithm, local binary pattern (LBP) of the contrast enhanced green channel is used as the textural descriptor of the WCE frames. The features employed to differentiate tumorous and non-tumorous frames are skewness (S) and kurtosis (K) of the LBP histogram. The threshold value of the features which offers the trade-off between sensitivity and specificity is identified through Receiver Operating Characteristic (ROC) curve analysis. At the optimum threshold, both the features exhibited a sensitivity of 100% and specificity of 90%. The skewness and kurtosis of the LBP computed from the enhanced green channel of tumorous and non-tumorous frames differ significantly (p « 0.05) with a p-value of 2.2 × 10−16. The proposed method is helpful to reduce the time spent by the doctors for reviewing WCE.
  • Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face
    • Abstract: Publication date: Available online 27 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Krzysztof Kotowski, Katarzyna Stapor, Jacek Leski, Marian Kotas The article presents our proposed adaptation of the commercially available Emotiv EPOC+ EEG headset for neuroscience research based on event-related brain potentials (ERP). It solves Emotiv EPOC+ synchronization problems (common to most low-cost systems) by applying our proposed stimuli marking circuit. The second goal was to check the capabilities of our modification in neuroscience experiments on emotional face processing. Results of our experiment show the possibility of measuring small differences in the early posterior negativity (EPN) component between neutral and emotional (angry/happy) stimuli consistently with previous works using research-grade EEG systems.
  • Predicting the success of wart treatment methods using decision tree based
           fuzzy informative images
    • Abstract: Publication date: Available online 17 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Selahaddin Batuhan Akben Warts are small, rough, benign tumours caused by human papillomavirus (HPV). A challenge is predicting the success of wart treatment methods because success may vary depending on the patient and the features of disease. Recently, a machine learning based expert prediction system and related prediction rules were proposed. However, the success of this system is not satisfactory and should be improved. Furthermore, medical experts find it difficult to interpret the suggested rules of this system. The decision tree-based method was accordingly used in this study to determine the rules of predicting the success of wart treatment methods. According to findings, the success rate varied from 90 to 95% according to the treatment method; these rates are higher than previously reported. Furthermore, the decision tree rules that were determined can be transformed into images to visually interpret the success rates of treatment methods as a function of patient age and the time elapsed since disease appearance. This study provides a method for simple and more accurate interpretation of rules for medical experts. The success of treatment methods is now predictable as a percentage.
  • Design and miniaturization of dual band implantable antennas
    • Abstract: Publication date: Available online 6 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Soheil Hashemi, Jalil Rashed-Mohassel Two types of miniaturized dual band implantable antennas are designed and presented, one of a meander type and the other is the so called comb antenna. In medical applications the electromagnetic characteristic changes of tissue in different situations and the corresponding resonant frequency shifts, should not disturb the data transmission. The objective is to design dual band antennas in 400 MHz and 2.4 GHz with suitable bandwidths and small sizes. The meander type antenna was fabricated and its S parameters were measured using an equivalent liquid phantom of skin, fat and muscle which included propanol, butanol, purified water and salt. The experimental results are shown and compared.
  • Continuous blood glucose level prediction of Type 1 Diabetes based on
           Artificial Neural Network
    • Abstract: Publication date: Available online 30 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Jaouher Ben Ali, Takoua Hamdi, Nader Fnaiech, Véronique Di Costanzo, Farhat Fnaiech, Jean-Marc Ginoux Recent technological advancements in diabetes technologies, such as Continuous Glucose Monitoring (CGM) systems, provide reliable sources to blood glucose data. Following its development, a new challenging area in the field of artificial intelligence has been opened and an accurate prediction method of blood glucose levels has been targeted by scientific researchers. This article proposes a new method based on Artificial Neural Networks (ANN) for blood glucose level prediction of Type 1 Diabetes (T1D) using only CGM data as inputs. To show the efficiency of our method and to validate our ANN, real CGM data of 13 patients were investigated. The accuracy of the strategy is discussed based on some statistical criteria such as the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). The obtained averages of RMSE are 6.43 mg/dL, 7.45 mg/dL, 8.13 mg/dL and 9.03 mg/dL for Prediction Horizon (PH) respectively 15 min, 30 min, 45 min and 60 min and the average of MAPE was 3.87% for PH = 15 min, knowing that the smaller is the RMSE and MAPE, the more accurate is the prediction. Experimental results show that the proposed ANN is accurate, adaptive, and very encouraging for a clinical implementation. Furthermore, while other studies have only focused on the prediction accuracy of blood glucose, this work aims to improve the quality of life of T1D patients by using only CGM data as inputs and by avoiding human intervention.
  • Early Detection of Sudden Cardiac Death Using Nonlinear Analysis of Heart
           Rate Variability
    • Abstract: Publication date: Available online 29 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Mohammad Khazaei, Khadijeh Raeisi, Ateke Goshvarpour, Maryam Ahmadzadeh Background and ObjectiveSudden cardiac death (SCD) is one of the most widespread reasons for death around the world. A precise and early prediction of SCD can improve the chance of survival by administering cardiopulmonary resuscitation (CPR). Hence, there is a vital need for an SCD prediction system.MethodsIn this work, a novel and efficient algorithm for automated detection of SCD six minutes before its onset is proposed. This algorithm uses features based on the nonlinear modeling of heart rate variability (HRV). In fact, after the extraction of the HRV signals, increment entropy and recurrence quantification analysis-based features are extracted. The one-way ANOVA is applied for the dimension reduction of feature space—this results in lower computational cost. Finally, the distinguishing features are fed to classifiers such as the decision tree, K-nearest neighbor, naive Bayes, and the support vector machine.ResultsBy using the decision tree classifier we have achieved SCD detection six minutes before its onset with an accuracy, specificity, and sensitivity of 95%. These results demonstrate the superiority of the presented algorithm compared to the existing ones in performance.ConclusionsThis study shows that a combination of features based on the nonlinear modeling of HRV, such as laminarity (based on recurrence quantification analysis), and increment entropy leads to early detection of sudden cardiac death. Choosing the decision tree improves the performance of the algorithm. The results could help in the development of a tool that would allow the detection of cardiac arrest six minutes before its onset.
  • Use of features from RR-time series and EEG signals for automated
           classification of sleep stages in deep neural network framework
    • Abstract: Publication date: Available online 14 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): R.K. Tripathy, U. Rajendra Acharya Sleep is a physiological activity and human body restores itself from various diseases during sleep. It is necessary to get sufficient amount of sleep to have sound physiological and mental health. Nowadays, due to our present hectic lifestyle, the amount of sound sleep is reduced. It is very difficult to decipher the various stages of sleep manually. Hence, an automated system may be useful to detect the different stages of sleep. This paper presents a novel method for the classification of sleep stages based on RR-time series and electroencephalogram (EEG) signal. The method uses iterative filtering (IF) based multiresolution analysis approach for the decomposition of RR-time series into intrinsic mode functions (IMFs). The delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) waves are evaluated from EEG signal using band-pass filtering. The recurrence quantification analysis (RQA) and dispersion entropy (DE) based features are evaluated from the IMFs of RR-time series. The dispersion entropy and the variance features are evaluated from the different bands of EEG signal. The RR-time series features and the EEG features coupled with the deep neural network (DNN) are used for the classification of sleep stages. The simulation results demonstrate that our proposed method has achieved an average accuracy of 85.51%, 94.03% and 95.71% for the classification of ‘sleep vs wake’, ‘light sleep vs deep sleep’ and ‘rapid eye movement (REM) vs non-rapid eye movement (NREM)’ sleep stages.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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