International Journal of Biology and Biomedical Engineering


ISSN: 1998-4510
Volume 11, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 11, 2017


Title of the Paper: Identification of a Oculo-Motor System Human Based on Volterra Kernels

 

Authors: Vitaliy D. Pavlenko, Dmytro V. Salata, Hryhori P. Chaikovskyi

Pages: 121-126

Abstract: A new method of constructing nonparametric dynamic model of the human oculomotor system on the basis of experimental data "input-output" is developed, considering nonlinear and inertial properties of the rectus muscles of the eye. A technology for tracking eye movement is based on the videos. It is possible to determine the dynamic characteristics of the oculo-motor system system functions as a transition of the first and second order - integral transforms Volterra kernels.


Title of the Paper: Collimator Study of a Pre-Clinical Pixelated Semiconductor SPECT System Using Monte Carlo Simulation

 

Authors: Hyun-Woo Jeong, Jong Seok Kim, Se Young Bae, Kanghyen Seo, Seung Hun Kim, Seong Hyeon Kang, Dong Jin Shin, Kyuseok Kim, Youngjin Lee

Pages: 115-120

Abstract: In single photon emission computed tomography (SPECT) with pixelated semiconductor detector (PSD), not only pinhole collimator but also parallel-hole collimator is often used in pre-clinical nuclear medicine imaging system. The purpose of this study was to evaluate and compare pinhole and parallel-hole collimators in PSD. In this study, we performed a simulation study of the PID 350 (Ajat Oy Ltd., Finland) CdTe PSD using a Geant4 Application for Tomographic Emission (GATE) simulation. For that purpose, we designed four collimators which are most frequently used in the pre-clinical nuclear medicine: (1) pinhole collimator, (2) low energy high resolution (LEHR), (3) low energy general purpose (LEGP), and (4) low energy high sensitivity (LEHS) parallel-hole collimator. The sensitivity and spatial resolution of the four collimators were evaluated using point source. Moreover, to assess the overall performance of the imaging system, a hot-rod phantom was designed using a GATE simulation. The highest sensitivity was achieved using LEHS, followed by LEGP, LEHR, and pinhole. Also, at 2 cm source-to-collimator distance, the spatial resolution was 1.63, 2.05, 2.79, and 3.45 mm using pinhole, LEHR, LEGP, and LEHS, respectively. The reconstructed hot-rod phantom images showed that the pinhole collimator and the LEHR parallel-hole collimator give a fine spatial resolution for pre-clinical SPECT with PSD. In conclusion, we successfully compared different types of collimator with pre-clinical pixelated semiconductor SPECT system.


Title of the Paper: Efficient Electrocardiogram (ECG) Lossy Compression Scheme for Real Time e-Health Monitoring

 

Authors: Hatim Anas, Rachid Latif, Mounir Arioua

Pages: 101-114

Abstract: E-health monitoring is adopted to solve multiple problems such as: difficult access to hospitals, health monitoring of old patients ... Several operations slow down the e health systems, the most important one is the signal compression / decompression step. In this paper we present a new algorithm for compression / decompression of the ECG vital signal. The complexity of the proposed algorithm is very low and uses simple mathematical operations. In a hardware point of view, this property makes it suitable for real-time e-health monitoring. The algorithm’s kernel is based on the delta coding technique. We introduced two coding categories low and high and we defined a new frame format. This allows us to minimize the total amount of bits of the compressed signal. Three variants of the algorithm are designed and tested using the MIT-BIH physionet and PTB Diagnostic data bases. We used several signals with different cardiac pathologies for test. We reach a maximum compression ratio (CR) of 47 with a PRD of 0,073%. Our algorithm outperforms the state of the art techniques.


Title of the Paper: Interaction of Lysine Dendrimers of 2nd and 3rd Generation with Stack of Amyloid Peptides. Molecular Dynamics Simulation

 

Authors: I. Neelov, E. Popova, D. Khamidova, F. Komilov

Pages: 95-100

Abstract: In present paper, molecular dynamics simulation is used to study amyloid fibril destruction by oppositely charged dendrimers of second and third generation. Dendrimers are often used for delivery of drugs and biological molecules. They also could be used as antibacterial, antiviral and antiamyloid agents. Since lysine dendrimers are less toxic than conventional synthetic dendrimers), they were chosen for present study and systems consisting of 2nd and 3rd generation dendrimers and stack of 16 short amyloid peptides in water were studied. It was shown that lysine dendrimers of both generations destroy amyloid stack and form stable complexes with amyloid peptides. The structures of the complexes in equilibrium state were investigated. Also it was obtained that peptides in complexes stay mainly on the surface of dendrimer and do not penetrate into them. The results obtained in present paper could be useful for elaboration in future the antiamyloiud agents for treatment of Alzheimer's disease, since it is believed that one of the reasons for its occurrence is the formation of amyloid fibrils.


Title of the Paper: Analysis of the Influence of Trauma Injury Factors on the Probability of Survival

 

Authors: M. Saleh, R. Saatchi, F. Lecky, D. Burke

Pages: 88-94

Abstract: The probability or likelihood of survival in trauma injuries is a clinically important parameter for triage, setting treatment priorities and research and management audit. The existing methods for determining it have short comings that necessitate further development. In this study, an artificial intelligence method called fuzzy inference system (FIS) for determining the likelihood of survival in trauma injuries is being developed and evaluated. The accuracy of the FIS primarily depends on the design of its knowledge base. The required knowledge base is being designed by carrying out a detailed statistical analysis of the trauma injury profiles contained in a large data base of injury cases. As part of this analysis, the relationships between the body regions affected by trauma injuries, physiological measures (such as blood pressure, respiration rate and heart rate), age, gender , the neurological factors assessed by the Glasgow Comma Score and pre-exiting medical conditions on the probability of survival were analysed and a FIS system to indicate the likelihoods survival was proposed. The preliminary results obtained are presented.


Title of the Paper: Numerical Study of Identification of the Main Characteristics of Air Transport in the Human Nasal Cavity

 

Authors: Alibek Issakhov, Aizhan Abylkassymova

Pages: 80-87

Abstract: In this paper was considered the use of the numerical study of identification the main characteristics of air transport in the human nasal cavity. Investigation of air flow in the human nasal cavity is of considerable interest since breathing is done mainly through the nose. In this study conducted a two-dimensional numerical simulation of air transport in the cross-sections model of the nasal cavity to normal human nose based on the Navier-Stokes equations, the temperature transport equations and relative humidity equation. For the numerical solution of this system of equations is used projection method. The numerical solution of the equation system is divided into five stages. At the first step, it is assumed that the momentum transfer by convection and diffusion. The intermediate velocity field is solved by the 5-step Runge–Kutta method. At the second stage, the pressure field is solved by the found intermediate velocity field. The Poisson equation for the pressure field is solved by the Jacobi method. The third step assumes that the transfer is carried out only by the pressure gradient. The fourth and fifth steps of the temperature and relative humidity equations are also solved as momentum equations, with the 5-step Runge–Kutta method. This numerical algorithm fully parallelized using different geometric decompositions. The obtained data transfer numerical modelling air human nasal cavity was verified with known numerical results in the form of velocity, temperature and relative humidity profiles.


Title of the Paper: Use of Statistical Approaches and Artificial Neural Networks to Identify Gait Deviations in Children with Autism Spectrum Disorder

 

Authors: Che Zawiyah Che Hasan, Rozita Jailani, Nooritawati Md. Tahir

Pages: 74-79

Abstract: Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% accuracy, 93.3% sensitivity, and 90.0% specificity. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments.


Title of the Paper: Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

 

Authors: Wafaa Alakwaa, Mohammad Nassef, Amr Badr

Pages: 66-73

Abstract: This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The initial approach was to directly feed the segmented CT scans into 3D CNNs for classification, but this proved to be inadequate. Instead, a modified U-Net trained on LUNA16 data (CT scans with labeled nodules) was used to first detect nodule candidates in the Kaggle CT scans. The U-Net nodule detection produced many false positives, so regions of CTs with segmented lungs where the most likely nodule candidates were located as determined by the U-Net output were fed into 3D Convolutional Neural Networks (CNNs) to ultimately classify the CT scan as positive or negative for lung cancer. The 3D CNNs produced a test set Accuracy of 86.6%. The performance of our CAD system outperforms the current CAD systems in literature which have several training and testing phases that each requires a lot of labeled data, while our CAD system has only three major phases (segmentation, nodule candidate detection, and malignancy classification), allowing more efficient training and detection and more generalizability to other cancers.


Title of the Paper: Classification of Host Origin in Influenza a Virus by Transferring Protein Sequences into Numerical Feature Vectors

 

Authors: Fayroz F. Sherif, Nourhan Zayed, Mahmoud Fakhr

Pages: 61-65

Abstract: Global outbreaks of human influenza occur from influenza A viruses with novel Hemagglutinin (HA) molecules to which humans have no immunity. So accurate detection of influenza viral origin is of particular importance to improve influenza surveillance and vaccine development. Here, a total of 1500 and 2349 protein sequences for Hemagglutinin (HA) and Neuraminidase (NA) respectively were selected to be involved in our study. We used two techniques to transfer the protein sequences into feature vectors firstly, the feature vector constructed from the composition of amino acids (AAC) and secondly the feature vector constructed from the Composition, Transition, Distribution (CTD). Both used separately for the training of machine learning algorithms. Host of origin classification models constructed using KNN and random forest based on AAC and CTD feature vectors. The results guarantee that the classification performance using AAC feature vector achieves slightly better performance than using CDT feature vector. Furthermore host classification using HA protein segment achieved higher accuracy results than NA. The highest host classification model was HA-human using random forest with accuracy 96.6% and 95.3% for AAC and CDT respectively.


Title of the Paper: Study of the Aromatic Profile of Traminer Rot (Gewürztraminer) by GC-MS

 

Authors: J. Sochor, M. Baron, L. Sochorova, M. Kumsta

Pages: 55-60

Abstract: This paper is focused on the study of the aromatic profile of Traminer rot must, cultivated in the Moravian wine-growing region in the Czech Republic. In our paper, we monitored selected terpenic substances during maceration after 0, 6, 12, 18, 24, 30, and 36 hours. The aromatic profile was studied by gas chromatography with mass detection (GC-MS). Our focus was on the determination of free, bound, and total terpenic substances, in addition to the determination of specific aromatic substances: linalool, geraniol, nerol, alpha-terpineol, and hotrienol. The study confirmed that increasing the maceration time also increases the content of free and total terpenic substances.


Title of the Paper: Risk Analysis of Transesophageal Echocardiography Telemanipulator in Catheterization Laboratory

 

Authors: Indhika F. Warsito, Christina Pahl, Eko Supriyanto, A. Soesanto

Pages: 48-54

Abstract: The use of conventional Transesophageal Echocardiography (TEE) machine in Catheterization Laboratory (Cath Lab) remain few safety issues related to radiation and ergonomics. In order to solve these, TEE telemanipulator has been proposed. This has however other risks which may arise during the use of the machine including electrical, mechanical, and electromagnetic risks. In this paper, the risk analysis of TEE Telemanipulator in Cath Lab is discussed. This includes the hazard identification and risk level estimation. Electrical, mechanical, electromagnetic, radiation and operational hazards are identified. Failure Mode and Effect Analysis (FMEA) is used to estimate the level of risk. Test result shows that the risk of TEE manipulator type II is lower compare to conventional TEE Machine and TEE manipulator type I.


Title of the Paper: B850 Ring from Light–Harvesting Complex LH2 - Fluctuations in Dipole Moment Orientations of Bacteriochlorophyll Molecules

 

Authors: Pavel Herman, David Zapletal

Pages: 39-47

Abstract: Interactions with fluctuated environment strongly influence properties of light–harvesting (LH) pigment–protein complexes. Slow fluctuations could be modeled by static disorder. Several types of these fluctuations are connected with changes of ring geometry. Slow fluctuations of bacteriochlorophyll’s dipole moment orientations in B850 ring from LH2 complex of purple bacteria are investigated in present paper. Three modifications of such uncorrelated static disorder type (Gaussian fluctuations of dipole moment orientations in the ring plane, Gaussian fluctuations of dipole moment orientations in a plane which is perpendicular to the ring one and Gaussian fluctuations of dipole moment orientations in arbitrary direction) are taking into account. Distributions of the nearest neighbour transfer integrals are presented and the most important statistical properties are calculated, discussed and compared for different strengths of static disorder.


Title of the Paper: Computational Characterization of Aerosol Delivery for Preterm Infants

 

Authors: I. Aramendia, U. Fernandez-Gamiz, A. Lopez-Arraiza, M. A. Gomez-Solaetxe, J. M. Lopez-Guede, J. Sancho, F. J. Basterretxea

Pages: 29-38

Abstract: The aerosolization of perfluorocarbons along with non-invasive respiratory support has showed promising results as an alternative to treat the respiratory distress syndrome (RDS) in preterm infants. The aim of this study was to evaluate the main characteristics of the aerosol generated by an intracorporeal inhalation catheter, where one central lumen deliver the liquid and six peripheral lumens deliver the compressed air. Initially, different experiments were made with sterile water at different driving pressures to analyze properties such as the aerodynamic diameter (Da), mass median aerodynamic diameter (MMAD) and geometric standard deviation (GSD). Subsequently, the perfluorocarbon FC-75 was tested to obtained experimental data to define the boundary conditions and to validate the numerical model. The experimental validation of the numerical model provided an accurate prediction of the air flow axial velocity and suggested that the collision and coalescence of the particles plays a crucial role in the particle size and mass distribution.


Title of the Paper: Physical Principles of the Vacuum Aspiration for Prostatitis Treatment

 

Authors: Andrei Yu. Kulinich, Irina V. Golovacheva, Mikhail Ye. Zhuravlev

Pages: 25-28

Abstract: Vacuum aspiration is an effective method of prostatitis therapy. Bacterial prostatitis leads to duct obstruction. The plug consists of dense acini epithelium and secretions. Vacuum aspiration procedure results in the destruction of the plug. The fact of the destruction has been verified by the analysis of the substance obtained during the procedure. This method is used in a few medical institutions. Meanwhile, the physical principles of the procedure have never been investigated. We analyze plausible physical mechanisms of purification of prostatic acini and ducts by means of transurethral vacuum aspiration. A mechanical model is offered to describe the process of plug destruction during vacuum aspiration procedure. The majority of medical practitioners believe that the plug is extracted as a whole during such procedure. However, our theoretical research demonstrates that the sucking of a plug as a whole, previously viewed as the most likely mechanism, is not consistent with the experimental data.


Title of the Paper: Integrated Higher-Order Evidence-Based Framework for Prediction of Higher-Order Epistasis Interactions in Alzheimer's Disease

 

Authors: Fayroz F. Sherif, Nourhan Zayed, Mahmoud Fakhr, Manal Abdel Wahed, Yasser M. Kadah

Pages: 16-24

Abstract: Alzheimer's disease (AD) is the most common form of dementia with strong genetic factors in which a combination of genetic variants contributes to AD risk. Discovering epistasis interactions among genetic variants is key to identifying valuable AD predictive models that allow earlier diagnosis and better prognosis for patient. Presently, AD predictive models are derived using either statistical or biological feature selection methods. Unfortunately, both approaches suffer from inherent limitations in their generalization and prediction power. This study presents a new hybrid method between these two approaches based on integrated higher-order evidence-based (IHOEB) framework. This method combines statistical and biological feature selection methods and allow computationally-efficient detection of up to 4-way epistasis models associated with AD. The new processing framework was applied to data obtained from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). The classification accuracies of IHOEB 4-way models varied between (0.7410-0.7860) whereas the accuracies of statistical and biological 2-way models varied between (0.6450-0.6760) and (0.5300-0.5750) respectively. This new IHOEB framework offers a promising alternative for epistasis interactions in genome wide association studies where it allows identification of AD models that are supported by both statistical and biological analyses efficiently and at higher accuracy.


Title of the Paper: A Discrete Time Population Genetic Model for X-Linked Recessive Diseases

 

Authors: Carmen Del Vecchio, Francesca Verrilli, Luigi Glielmo, Martin Corless

Pages: 7-15

Abstract: The epidemiology of X-linked recessive diseases, a class of genetic disorders, is modeled with a discretetime, structured, mathematical model. The model accounts for both de novo mutations and different reproduction rates of procreating couples depending on their health conditions. Relying on Lyapunov theory, asymptotic stability properties of equilibrium points of the model are demonstrated. The model describes the spread over time in the population of any recessive genetic disorder transmitted through the Xchromosome.


Title of the Paper: Analysis of Pediatric Foot Disorders Using Decision Tree and Neural Networks

 

Authors: J. K. Choi, Y. G. Won, J. J. Kim

Pages: 1-6

Abstract: Data mining is method to extract hidden predictive information, and it has been recognized by many studies. The object in the study was to discover meaningful knowledge between the foot disorder and biomechanical parameters related to symptom using C5.0 decision tree and neural networks. The first medical record data of 174 pediatric patients was extracted for analysis, in total 279 records, and they were diagnosed with a complex foot disorder. The dependent variable consists of five complex disorder groups, and 14 independent variables related to disorder groups were selected by importance, in 34 variables. The extracted data was separated to generate an ideal prediction model. After development of the prediction model, the prediction rate was verified and neural networks were applied for analysis of predictor importance and classification prediction. Consequently, a major symptom information in 13 diagnosis patterns was confirmed.