Subjects -> ENGINEERING (Total: 2844 journals)
    - CHEMICAL ENGINEERING (259 journals)
    - CIVIL ENGINEERING (255 journals)
    - ELECTRICAL ENGINEERING (182 journals)
    - ENGINEERING (1420 journals)
    - ENGINEERING MECHANICS AND MATERIALS (454 journals)
    - HYDRAULIC ENGINEERING (60 journals)
    - INDUSTRIAL ENGINEERING (101 journals)
    - MECHANICAL ENGINEERING (113 journals)

ENGINEERING (1420 journals)                  1 2 3 4 5 6 7 8 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 9)
3D Research     Hybrid Journal   (Followers: 22)
AAPG Bulletin     Hybrid Journal   (Followers: 11)
Abstract and Applied Analysis     Open Access   (Followers: 4)
Aceh International Journal of Science and Technology     Open Access   (Followers: 9)
ACS Nano     Hybrid Journal   (Followers: 452)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 10)
Acta Nova     Open Access   (Followers: 1)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 4)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access   (Followers: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8)
Adaptive Behavior     Hybrid Journal   (Followers: 9)
Adsorption     Hybrid Journal   (Followers: 5)
Advanced Energy and Sustainability Research     Open Access   (Followers: 8)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 14)
Advanced Engineering Research     Open Access  
Advanced Journal of Graduate Research     Open Access   (Followers: 4)
Advanced Quantum Technologies     Hybrid Journal   (Followers: 1)
Advanced Science     Open Access   (Followers: 13)
Advanced Science Focus     Free   (Followers: 7)
Advanced Science Letters     Full-text available via subscription   (Followers: 13)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 11)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 20)
Advanced Theory and Simulations     Hybrid Journal   (Followers: 5)
Advances in Catalysis     Full-text available via subscription   (Followers: 8)
Advances in Complex Systems     Hybrid Journal   (Followers: 12)
Advances in Engineering Software     Hybrid Journal   (Followers: 31)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 20)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 22)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 30)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 27)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 10)
Advances in Natural Sciences : Nanoscience and Nanotechnology     Open Access   (Followers: 36)
Advances in Operations Research     Open Access   (Followers: 14)
Advances in OptoElectronics     Open Access   (Followers: 6)
Advances in Physics Theories and Applications     Open Access   (Followers: 21)
Advances in Polymer Science     Hybrid Journal   (Followers: 54)
Advances in Porous Media     Full-text available via subscription   (Followers: 6)
Advances in Remote Sensing     Open Access   (Followers: 59)
Advances in Science and Research (ASR)     Open Access   (Followers: 8)
Aerobiologia     Hybrid Journal   (Followers: 4)
Aerospace Systems     Hybrid Journal   (Followers: 10)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 8)
AIChE Journal     Hybrid Journal   (Followers: 38)
Ain Shams Engineering Journal     Open Access   (Followers: 7)
Al-Nahrain Journal for Engineering Sciences     Open Access  
Al-Qadisiya Journal for Engineering Sciences     Open Access   (Followers: 2)
AL-Rafdain Engineering Journal     Open Access   (Followers: 3)
Alexandria Engineering Journal     Open Access   (Followers: 3)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 27)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 12)
American Journal of Engineering Education     Open Access   (Followers: 20)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 31)
Annals of Civil and Environmental Engineering     Open Access   (Followers: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 6)
Annals of Regional Science     Hybrid Journal   (Followers: 10)
Annals of Science     Hybrid Journal   (Followers: 10)
Annual Journal of Technical University of Varna     Open Access   (Followers: 1)
Antarctic Science     Hybrid Journal   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 2)
Applications in Energy and Combustion Science     Open Access   (Followers: 4)
Applications in Engineering Science     Open Access   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 8)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 22)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Engineering Letters     Open Access   (Followers: 5)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 11)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 6)
Applied Physics Research     Open Access   (Followers: 7)
Applied Sciences     Open Access   (Followers: 6)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6)
Arab Journal of Basic and Applied Sciences     Open Access  
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
Archives of Thermodynamics     Open Access   (Followers: 13)
Arctic     Open Access   (Followers: 7)
Arid Zone Journal of Engineering, Technology and Environment     Open Access   (Followers: 2)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ArtefaCToS : Revista de estudios sobre la ciencia y la tecnología     Open Access   (Followers: 1)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 2)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 9)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 7)
Assembly Automation     Hybrid Journal   (Followers: 2)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
AURUM : Mühendislik Sistemleri ve Mimarlık Dergisi = Aurum Journal of Engineering Systems and Architecture     Open Access   (Followers: 1)
Australasian Journal of Engineering Education     Hybrid Journal   (Followers: 3)
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Hybrid Journal   (Followers: 2)
Autocracy : Jurnal Otomasi, Kendali, dan Aplikasi Industri     Open Access  
Automotive and Engine Technology     Hybrid Journal  
Automotive Experiences     Open Access  
Automotive Innovation     Hybrid Journal   (Followers: 1)
Avances en Ciencias e Ingenierías     Open Access  
Avances: Investigación en Ingeniería     Open Access   (Followers: 6)
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 2)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 6)
Batteries     Open Access   (Followers: 11)
Batteries & Supercaps     Hybrid Journal   (Followers: 7)
Bautechnik     Hybrid Journal   (Followers: 3)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 29)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
Beyond : Undergraduate Research Journal     Open Access  
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Bilge International Journal of Science and Technology Research     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 14)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 6)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 6)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 8)
Biomicrofluidics     Open Access   (Followers: 7)
Biotechnology Progress     Hybrid Journal   (Followers: 44)
Black Sea Journal of Engineering and Science     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 13)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 15)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers Droit, Sciences & Technologies     Open Access   (Followers: 1)
Calphad     Hybrid Journal   (Followers: 2)
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 30)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 6)
Case Studies in Thermal Engineering     Open Access   (Followers: 8)
Catalysis Communications     Hybrid Journal   (Followers: 7)
Catalysis Letters     Hybrid Journal   (Followers: 3)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 9)
Catalysis Science and Technology     Hybrid Journal   (Followers: 13)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 8)
CEAS Space Journal     Hybrid Journal   (Followers: 6)
Cell Reports Physical Science     Open Access  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 2)
Central European Journal of Engineering     Hybrid Journal  
CFD Letters     Open Access   (Followers: 8)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 3)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Journal of Population, Resources and Environment     Open Access  
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencia y Tecnología     Open Access  
Ciencias Holguin     Open Access   (Followers: 2)
CienciaUAT     Open Access   (Followers: 1)
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Hybrid Journal   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 27)
Clay Minerals     Hybrid Journal   (Followers: 9)
Coal Science and Technology     Full-text available via subscription   (Followers: 4)
Coastal Engineering     Hybrid Journal   (Followers: 14)
Coastal Engineering Journal     Hybrid Journal   (Followers: 9)
Coastal Engineering Proceedings : Proceedings of the International Conference on Coastal Engineering     Open Access   (Followers: 2)
Coastal Management     Hybrid Journal   (Followers: 30)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 3)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Color Research & Application     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 18)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 21)
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering     Open Access  
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 28)
Composite Interfaces     Hybrid Journal   (Followers: 10)
Composite Structures     Hybrid Journal   (Followers: 335)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 279)
Composites Part B : Engineering     Hybrid Journal   (Followers: 312)
Composites Part C : Open Access     Open Access   (Followers: 3)
Composites Science and Technology     Hybrid Journal   (Followers: 247)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Computation     Open Access   (Followers: 1)
Computational Geosciences     Hybrid Journal   (Followers: 20)
Computational Optimization and Applications     Hybrid Journal   (Followers: 11)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 21)

        1 2 3 4 5 6 7 8 | Last

Similar Journals
Journal Cover
Biomedical Engineering: Applications, Basis and Communications
Journal Prestige (SJR): 0.188
Citation Impact (citeScore): 1
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1016-2372 - ISSN (Online) 1793-7132
Published by World Scientific Homepage  [119 journals]
  • DETECTION OF AGE-RELATED MACULAR DEGENERATION FROM OCT IMAGES USING DOUBLE
           SCALE CNN ARCHITECTURE

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      Authors: S. Sabi, Varun P. Gopi, J. R. Anoop Raj
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Volume 33, Issue 04, August 2021.
      An ocular disease that affects the elderly is Age-related Macular Degeneration (AMD). Because of the aging population in society, AMD incidence is increasing; early diagnosis is vital to avoid vision loss in the elderly. It is a challenging process to organize a comprehensive eye screening system for detecting AMD. This paper proposes a novel Double Scale Convolutional Neural Network (DSCNN) architecture for an accurate AMD diagnosis. The architecture proposed is a DSCNN with six convolutional layers for classifying AMD or normal images. The double-scale convolution layer enables many local structures to be generated with two different filter sizes. In this proposed network, the sigmoid function is used as the classifier. The proposed CNN network is trained on the Mendeley data set and tested on four data sets, namely Mendeley, OCTID, Duke, SD-OCT Noor data set, and achieved an accuracy of 99.46%, 98.08%, 96.66%, and 94.89% respectively. The comparison with alternative methods provided results showing the efficacy of the proposed algorithm in detecting AMD. Although the proposed model is trained only on the Mendeley data set, it achieved good detection accuracy when evaluated with other data sets. This indicates the proposed model’s ability to classify AMD/Normal images from different data sets. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in detecting AMD. The proposed architecture can be applied in the rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters, the proposed CNN can be implemented in real-time.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-03T07:00:00Z
      Issue No: Vol. 33, No. 04 (2021)
       
  • AUTOMATED SEGMENTATION OF HEEL FISSURES BASED ON THERMAL IMAGE PROCESSING
           AND CLASSIFICATION BASED ON MACHINE LEARNING ALGORITHMS

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      Authors: Bhargavee Guhan, S. Sowmiya, U. Snekhalatha, T. Rajalakshmi
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Volume 33, Issue 04, August 2021.
      Heel fissures are cracks in the skin over the heels that lead to pain, discomfort and decreased confidence levels. If left untreated, they may also lead to infections and in rare cases, become life-threatening. Therefore, people with heel fissures generally try to find some remedy to relieve their symptoms. The objectives of this study are as follows: (1) To use thermal imaging to determine whether a characteristic difference in temperature exists in the heel fissure regions before and after performing heel therapy; (2) To segment the images and extract the features using k-means, GLCM and SURF methods, respectively; (3) To implement machine learning classifier for classification on normal heel and fissured heel. A number of 30 heel fissure and 30 normal subjects were considered for this study. All the candidates were from the age group of 35–55 years. Thermography was used to acquire the images of heel regions, and the thermographs were analyzed for feature extraction. Naïve Bayes, Bagging, Random Forest, LMT and Simple Logistic classifiers were used for classification of the thermograms. After heel therapy, a 2.2% and 2.6% decrease in temperature was observed in the right and left heel, respectively. The GLCM mean is increased by 6% and 4.3% in the right and left heel, respectively. A considerable decrease in variance in the fissure regions after therapy has also been observed. All three classifiers were shown to be efficient, with Nave Bayes and Bagging classifier both showing accuracy of 89%. The ROC curves have also been obtained, with an area under curve equal to 0.97.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-05-25T07:00:00Z
      Issue No: Vol. 33, No. 04 (2021)
       
  • EXPLORATION OF THE NOVEL CORONA VIRUS TRANSITION GRAPHS WITH PETRINET
           MODELING

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      Authors: Fatima Alam, Abdel-Salam G. Abdel-Salam, Ayesha Sohail, M. Yousaf, Sümeyye Tunç
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Volume 33, Issue 04, August 2021.
      Corona virus (CoV) is a group of viruses with non-bifurcated, single-stranded, and positive-sense RNA genomes. Apart from infecting several economically significant vertebrates (such as pigs and chickens), it is reported in the recent literature that six main types of CoVs infect the human hosts and cause lung infections. In animals, CoVs cause several diseases, including pneumonia, gastrointestinal tract, and central nervous system diseases. In humans, the CoVs work as respiratory tract diseases, and the new CoVs can penetrate the barrier between other species and humans and can cause a high mortality rate. In the course of this study, a novel approach to networking, based on the density-dependent differential equations, is adopted for the precise explanation of the propagation of the virus and the effect of quarantine on it. An infectious disease model with a time delay is suggested based on the conventional infectious disease model. To describe the viral infection period and treatment time, the time differential is used. Using the epidemic data released in real-time, the minimum error is obtained firstly through the inversion of the numerical simulation parameter; then we simulate the development pattern of the epidemic according to the dynamics system; finally, the effectiveness of quarantine steps is compared and analyzed. With the help of a discrete model, the transformations are documented in detail that is difficult to evaluate numerically. The provided numerical results are in close agreement with the experimental findings. The modeling of Petri nets (PNs) used has proven to be a successful method. The current research strategy can help the public to gain awareness of the disease spread, which is highly desired.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-05-04T07:00:00Z
      Issue No: Vol. 33, No. 04 (2021)
       
  • A CHAOTIC MULTILAYER LIF SCHEME TO MODEL THE PRIMARY VISUAL CORTEX

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      Authors: Nahid Abolpour, Reza Boostani, Mohammad-Ali Masnadi-Shirazi, Bahman Tahayori, Ali Almasi
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Volume 33, Issue 04, August 2021.
      Precise mathematical modeling of the primary visual cortex (V1) is still a challenging problem. Due to the high similarity of visual system of cat and human, in this paper, we present a hybrid model to track the electrical responses of neurons that are measured by a multi-electrode array implanted in cat V1. The proposed model combines a stochastic phenomenological model with a multilayer leaky integrate-and-fire (LIF) model to predict V1 responses. Since all the existing visual cortex models do not capture the stochastic properties of synaptic changes, the proposed phenomenological model provides input currents for V1 by utilizing continuous chaotic neural equations with a quantization rule. Then a multilayer LIF model is presented to mimic the functions of lateral geniculate nucleus (LGN) and V1 neurons by their corresponding differential equations. The input current in these models is from the presynaptic neurons, which are computed using the LIF model. The LGN-V1 neuronal connections are adopted from previous studies, where the receptive fields (RFs) of LGN neurons converge onto elongated spatial structures that denote RFs of V1 neurons. The main purpose of this paper is to develop a short-term plasticity model that is more consistent with the nature of the LGN and V1 responses compared to state-of-the-art models. Previous studies have not incorporated the stochastic and quantized behaviors of neurons that in the recorded data of implemented electrodes. The experimental results show the ability of the proposed model to accurately predict spikes recorded experimentally, indicating the model outperforms the state-of-the-art method.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-05-04T07:00:00Z
      Issue No: Vol. 33, No. 04 (2021)
       
  • DEEP LEARNING SURVIVAL PREDICTION FOR LUNG CANCER PATIENTS

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      Authors: Caiyun Huang, Changhua Yin
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Volume 33, Issue 04, August 2021.
      Lung cancer is one of the most common cancers; lung cancer is a malignant tumor that seriously threatens the lives of patients. Improving survival prediction performance is meaningful for making the treatment plans and improving the survival rates of lung cancer patients. In this paper, an approach for predicting the survival of lung cancer patients is proposed based on pathological images. First, the deep learning method is used to automatically detect lung cancer cells in pathological pictures, and features of the detected lung cancer cells are extracted. In feature selection, an extraction method of topological features is given, it reflects the relationship and distribution characteristics of lung cancer cells, and the extracted topological features are used as predictive factors for survival analysis. In this paper, the extraction methods of global topological features are mainly studied; for example, the overall association, location relationship and distribution of cells, and the global topological features of lung cancer cells are extracted through the Voronoi diagram, Delaunay triangle, and minimum spanning tree methods. Finally, the Cox–LASSO method was used to predict the survival of lung cancer patients. Experimental results show that this method can improve the efficiency and accuracy of cell detection, and there is a higher ability to predict and analyze the survival of lung cancer patients.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-05-04T07:00:00Z
      Issue No: Vol. 33, No. 04 (2021)
       
  • PROLIFERATION OF SACCHAROMYCES CEREVISIAE EXPOSED TO PULSED MAGNETIC
           FIELDS OF LOW INTENSITY

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      Authors: Erandeni Xuxumarat Rodríguez-Pérez, Laura Patricia Álvarez De la Paz, Verónica Alejandra Mondragón-Jaimes, Benjamín Hernández-Reyes, Julio César Villagómez-Castro, Modesto Sosa-Aquino
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Yeast cultures of Saccharomyces cerevisiae were exposed to pulsed magnetic fields of low intensity. Field intensities from 0.92 mT to 2.73[math]mT and frequencies of 20, 77.5, 135, 192.5 and 250[math]Hz were employed. Proliferation and cell viability measurements were made in exposed and control cultures. For the frequency of 20[math]Hz, statistically significant differences were found ([math]) in cell proliferation using field intensities of 1.81 and 2.71[math]mT. No changes were observed at this frequency for lower field strengths. At 77.5[math]Hz, only changes in proliferation were observed for 0.92[math]mT ([math]). No changes were observed to any other frequency or intensity of the magnetic field. The cell viability studies were performed at different times, from 4 to 12[math]h of exposure. At 1.81[math]mT and 20[math]Hz, an increase in colony-forming units (CFUs) of 26.1% was observed ([math]) at 4[math]h, and after 10[math]h of exposure the increase in CFUs was 51.5% ([math]). But when exposing the cells for 9[math]h to a magnetic field of 2.71[math]mT at 20[math]Hz, the CFUs decreased by 25.9% ([math]), and at 0.92[math]mT and 77.5[math]Hz, a decrease in CFUs of 18.6% was observed ([math]) at 4[math]h, while after 12 h of exposure an increase in CFU of 23.5% ([math]) was determined. These results suggest the presence of a frequency window between 20[math]Hz and 77.5[math]Hz, where the effects of the magnetic field are significant.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-10-20T07:00:00Z
       
  • MODIFIED P-SPECTRUM-BASED APPROACH TO ENHANCE SENSITIVITY FOR THE
           DETECTION OF CpG ISLANDS IN DNA SEQUENCES IN HUMAN SPECIES

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      Authors: Pardeep Garg, Sunil Datt Sharma
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      CpG Island (CGI) is considered to be one of the important segments of deoxyribonucleic acid (DNA) sequences. Out of the various epigenetic events which are associated with CGIs, some such events are like: CGIs are useful in the prediction of promoter region and subsequently for gene prediction, CGIs’ contribution in finding the epigenetic reasons of cancer is of great importance, CGIs can be used to identify chromosome inactivation. Therefore, the exact and maximum number of CGIs hidden in DNA sequences need to explored. A lot of computational, transform-based approaches have been developed and reported in literature for the identification of CGIs in DNA sequences since last many years. The problem associated with transform-based approaches is that the domain of functioning of algorithm requires to be changed which can probably lead to biasing and result in loss of important information in terms of CGIs. Hence, to provide a solution to this issue, a modified P-spectrum-based approach has been proposed here which does not suffer from domain transformation issue. Also, the performance of proposed algorithm has been tested on a large data set of 100 DNA sequences of human species and the performance has been compared with other recently reported methods of CGIs identification in DNA sequences. The results obtained prove that the proposed algorithm is better than the existing methods in terms of identification of more number of CGIs in DNA sequences. Therefore, the proposed algorithm has been considered as an efficient approach to enhance the sensitivity of CGIs identification.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-10-20T07:00:00Z
       
  • MOTILITY OF THE UNPREPARED INTESTINAL TRACT IN HEALTHY HUMANS AND PATIENTS
           

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      Authors: Liu Cui, Jing Huang, Yanjiao Shi, Hongzhe Yang
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Slow transit constipation (STC) is usually accompanied by intestinal motility abnormalities. Although conventional anorectal manometry could record the pressure in the colon, most patients need preparation of intestinal tract. The intervention of catheter for monitoring the intestinal pressure also affects the clinical measurement. The pressure data collected by the conventional anorectal manometry cannot fully characterize the dynamic characteristics of the intestine. Thus, we aimed to obtain colonic pressure data under normal physiological conditions. Utilizing these data, we analyze the difference of colonic motility parameters between healthy control and patients with STC. A micro-electronic capsule made by ourselves was used to gather the subjects’ intestinal pressure in their daily life. Several intestinal motility parameters were calculated from the pressure profile. The average energy of colonic pressure data in the STC group is higher than the healthy control group (HC: 259.95 vs. STC: 821.28). But the STC group has a lower average complexity of colonic motility (HC: 0.80 vs. STC: 0.64). About 81.25% of the colonic data from patients with STC could be identified by using slow transit constipation (SVM) classifier. Compared with health control, most colonic parameters of patients with STC are higher under the normal physiological conditions, but the complexity of colonic motility is lower in the STC group. The correct rate of colonic pressure recognition in the STC group is more than 80% by using SVM classifier.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-10-20T07:00:00Z
       
  • THE IMPACT OF HEAD POSITIONING ON THE IMAGE QUALITY OF MULTI-DETECTOR
           COMPUTED TOMOGRAPHY

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      Authors: Yang-Ting Hsu, Jo-Chi Jao
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Radiologic technologists face various types of patients during multi-detector computed tomography (CT) examinations. In emergency departments, it is common to have patients who cannot follow instructions for the examinations. The asymmetric axial view of the head CT might affect the correctness of the clinician’s diagnosis. This study aimed to assess the impact of head positioning on the image quality of head CT using two phantoms. All scans were performed on a 16-slice CT scanner. In the control group, the tilted angle of the phantoms was 0[math], and no multiplanar reconstruction (MPR) was performed. In the experimental groups, the tilted angles of the phantoms were 5[math], 10[math] and 15[math], respectively, and MPR was performed afterwards. The results showed that if the head was tilted during the head CT examinations, image asymmetry and artifacts appeared without MPR. After MPR, one phantom showed that there were significant differences and the other phantom showed no significant differences quantitatively in image symmetry and artifacts between experimental groups and the control group, while both phantoms showed no significant differences qualitatively in image symmetry and artifacts between experimental groups and the control group. Although MPR can correct the image asymmetry and artifacts caused by tilted head positioning to some extent, it consumes time. Therefore, technologists should position the head as exactly as possible when performing head CT examinations.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-08-31T07:00:00Z
       
  • EFFECTS OF WRIST GUARD AND ELBOW ARREST STRATEGY ON IMPACT FORCE IN
           FORWARD FALLS

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      Authors: Yan-Ren Lin, Chiung-Ling Chen, Yu-Chi Chen, Min-Hsien Cho, Shu-Zon Lou
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Wrist guards are widely used for preventing distal radius fracture during in-line skating and snowboard-related activities. However, more than half of people wearing wrist guards nonetheless sustain a fracture of the wrist in forward falls. Accordingly, this study evaluates the effects of three factors, namely the wrist guard design, the fall height and the arrest strategy, on the impact force during a forward fall onto a single outstretched hand. Fifteen physically healthy male participants volunteered for the biomechanical investigation. None of the participants had a previous history of upper extremity injuries or disorders. A 1000[math]Hz AMTI force plate was used to measure the ground reaction force (GRF) in forward falls performed using a self-built release system onto a single hand. The GRF and impact time were analyzed in terms of three factors, namely (1) the wrist guard design, including bare hand (BH), conventional wrist guard (WG), wrist guard pad on palm (WG+), and WG+ with no lower splint (WG[math]; (2) the elbow arrest strategy, including elbow extended and elbow flexed; and (3) the fall height, including 4[math]cm and 8[math]cm. The impact force and loading rate significantly increased with an increasing fall height. However, the elbow flexed strategy attenuated the GRF peak force and delayed the point of peak impact force. The GRF in the WG, WG+ and WG− conditions was significantly lower than that in the BH condition. Overall, a lower fall height, a wrist guard with a compliant pad (WG+ or WG[math], and an elbow flexed strategy reduced the impact force, delayed the peak impact force, and reduced the loading rate in forward falls.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-08-28T07:00:00Z
       
  • PREDICTING CLINICAL RESPONSE TO TRANSCRANIAL MAGNETIC STIMULATION IN MAJOR
           DEPRESSION USING TIME-FREQUENCY EEG SIGNAL PROCESSING

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      Authors: Elias Ebrahimzadeh, Mostafa Asgarinejad, Sarah Saliminia, Sarvenaz Ashoori, Masoud Seraji
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Repetitive transcranial magnetic stimulation (rTMS) is defined as a noninvasive technique of brain stimulation conducted for both diagnostic and therapeutic purposes. rTMS can effectively excite the brain neurons and increase brain plasticity, which becomes particularly useful in psychiatric and neurological fields. Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a noninvasive neurophysiological test that is promising as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel nonlinear index of the resting state EEG activity as a predictor of clinical outcome and compare its predictive capacity to traditional frequency-based indices. EEG was recorded from 50 patients with treatment resistant depression (TRD) and 24 healthy comparison (HC) subjects. TRD patients were treated with excitatory rTMS to the dorsolateral prefrontal cortex (DLPFC) for 4–6 weeks. EEG signals were first decomposed using the ICA algorithm and the extracted components were then processed by time-frequency analysis. We then go on to compare the participants’ depression severity before, after, and 2 months after finishing the last treatment session using the proposed rTMS therapy. Absolute powers (APs), band powers (BPs), and theta and beta band entropies (BAs), which were extracted from the EEG, are used as features for the classification of changes in patients and normal cases after applying rTMS. Accordingly, we can go beyond the Beck score and clinically classify the EEG signal into two classes: depression and normal. The results demonstrated 78.37%, 74.32%, and 82.43% accuracy for artificial neural network (ANN), [math]-nearest neighbor (KNN), and support vector machine (SVM) classifiers, respectively, indicating the superiority of the proposed method to those mentioned in similar studies. Also, the electrophysiological changes are shown to be evident in patients with major depression. Our data show that the time-frequency index yields superior outcome prediction performance compared to the traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-08-14T07:00:00Z
       
  • ON THE DIFFERENT VELOCITIES OF INLET BOUNDARY CONDITIONS FOR HUMAN ARM
           ANALYSIS OF ARTERIAL FLOW USING ONE-WAY FLUID–SOLID COUPLING

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      Authors: Quanyu Wu, Liu Xiaojie, Liu Meijun, Pan Lingjiao, Qian Chunqi
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Simulations for blood hydrodynamic problems have been still largely incomplete despite years of research, especially for the inlet of boundary conditions that served as an essential part in computational fluid dynamics simulations of blood flow in human arteries. In this paper, the four different velocities of inlet boundary conditions were tested and compared in the human arm arterial model developed by us previously. Based on the selected points of nine key areas in the blood model,[math] we analyzed the calculation results of pressure and shear stress distributions in detail. Our results show that they are changeable in different [math] (different peak velocities of inlet boundary). The results further show that the static pressure of the aortic tree is higher than the static pressure of the branch, while the shear stress of the aortic tree is lower than the shear stress of the branch. On the other hand, the velocities changed in different [math], the vessel walls of max total deformation appear in the middle radial obviously, compared with the equivalent and shear stress show at the entrance and bifurcations. In all, the simulation results of the brachial arteries provide the wall deformation, pressure and shear stress characteristics in different [math], and offer a new strategy to study the two-way coupling of hemodynamics in the arm arterial model.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-08-14T07:00:00Z
       
  • MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO
           PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

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      Authors: Marwa M. Abd El Hamid, Mohamed Shaheen, Mai S. Mabrouk, Yasser M. K. Omar
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-08-05T07:00:00Z
       
  • THE ROTATIONAL EFFECT OF MAGNETIC PARTICLES ON CELLULAR APOPTOSIS BASED ON
           FOUR ELECTROMAGNET FEEDBACK CONTROL SYSTEM

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      Authors: Jia Ji Lee, Chang Hong Pua, Misni Misran, Poh Foong Lee
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Objectives: Magnetic drug targeting offers the latest popular alternative option to deliver magnetic drug carriers into targeting region body parts through manipulation of an external magnetic field. However, the effectiveness of using an electromagnetic field to manipulate and directing magnetic particles is yet to be established. Methods: In this paper, a homemade cost-effective electromagnet system was built for the purpose of studying the control and directing the magnetic drug carriers. The electromagnet system was built with four electromagnetic sources and tested the capability in directing the particles’ movement in different geometry patterns. Besides that, the creation of the self-rotation of individual magnetic particle clusters was achieved by using fast switching between magnetic fields. This self-rotation allows the possibility of cell apoptosis study to carry out. The system was constructed with four electromagnets integrated with a feedback control system and built to manipulate a droplet of commercially available iron (II, III) oxide nanoparticles to steer the magnetic droplet along different arbitrary trajectories (square, circle, triangle, slanted line) in 2-dimensional. Results: A dynamic magnetic field of 25 Hz was induced for magnetic nanoparticles rotational effect to observe the cell apoptosis. A profound outcome shows that the declining cell viability of the cell lines by 40% and the morphology of shrinking cells after the exposure of the dynamic magnetic field. Conclusion: The outcome from the pilot study gives an idea on the laboratory setup serves as a fundamental model for studying the electromagnetic field strength in applying mechanical force to target and to rotate for apoptosis on cancer cell line study.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-07-13T07:00:00Z
       
  • EFFICACY OF CIELAB AND CMYK COLOR SPACES IN LEUKEMIA IMAGE ANALYSIS: A
           COMPARISON BY STATISTICAL TECHNIQUES

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      Authors: K. K. Anilkumar, V. J. Manoj, T. M. Sagi
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      There are several color models available and which color model to be used is a question to many researchers in the field of Medical Image Processing and Analysis. In this paper, the suitability of using the CIELAB and CMYK color spaces in processing and analyzing blood smear images of leukemia is assessed for comparison. Leukemia is commonly known as blood cancer and it usually leads to an abnormal proliferation of leukocytes (White Blood Cells) in the bone marrow and blood. The diagnosis of leukemia is primarily done by Pathologists by microscopically examining the blood and bone marrow smears of the suspected patient. Image processing-based methods can be used for automated detection and classification of leukemia to assist the Pathologists for a speedy diagnosis. The proposed study used [math]-means clustering to segment the leukocytes and leukemic blast cells. The microscopic smear images in RGB color format were transformed and processed in CIELAB as well as CMYK color spaces for comparison. Statistical analysis was performed using Student’s [math] test. The visual observation of the segmented images revealed that, processing the images in the CIELAB color space is comparatively better than the CMYK color space and it is reflected in the results of statistical comparison by Student’s [math] test. The study assessed the suitability of using the CIELAB and CMYK color spaces in segmentation and analysis of microscopic smear images of Leukemic and Normal cases and found that processing the images in CIELAB color space is comparatively better than CMYK and such comparative analysis is not available in the literature.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-07-08T07:00:00Z
       
  • RECONSTRUCTED TEETH IMAGE FROM BRACES WITH GAN

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      Authors: Vu Tuan Hai, Dang Thanh Vu, Huynh Ho Thi Mong Trinh, Pham The Bao
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Recent advances in deep learning models have shown promising potential in object removal, which is the task of replacing undesired objects with appropriate pixel values using known context. Object removal-based deep learning can commonly be solved by modeling it as the Img2Img (image to image) translation or Inpainting. Instead of dealing with a large context, this paper aims at a specific application of object removal, that is, erasing braces trace out of an image having teeth with braces (called braces2teeth problem). We solved the problem by three methods corresponding to different datasets. Firstly, we use the CycleGAN model to deal with the problem that paired training data is not available. In the second case, we try to create pseudo-paired data to train the Pix2Pix model. In the last case, we utilize GraphCut combining generative inpainting model to build a user-interactive tool that can improve the result in case the user is not satisfied with previous results. To our best knowledge, this study is one of the first attempts to take the braces2teeth problem into account by using deep learning techniques and it can be applied in various fields, from health care to entertainment.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-07-08T07:00:00Z
       
  • TELE-MONITORING SYSTEM OF RISK IN RESPIRATORY PATIENTS

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      Authors: Awad Al-Zaben, Lina M.K. Al-Ebbini, Badr Qatashah
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      In many situations, health care professionals need to evaluate the respiration rate (RR) for home patients. Moreover, when cases are more than health care providers’ capacity, it is important to follow up cases at home. In this paper, we present a complete system that enables healthcare providers to follow up with patients with respiratory-related diseases at home. The aim is to evaluate the use of a mobile phone’s accelerometer to capture respiration waveform from different patients using mobile phones. Whereas measurements are performed by patients themselves from home, and not by professional health care personnel, the signals captured by mobile phones are subjected to many unknowns. Therefore, the validity of the signals has to be evaluated first and before any processing. Proper signal processing algorithms can be used to prepare the captured waveform for RR computations. A validity check is considered at different stages using statistical measures and pathophysiological limitations. In this paper, a mobile application is developed to capture the accelerometer signals and send the data to a server at the health care facility. The server has a database of each patient’s signals considering patient privacy and security of information. All the validations and signal processing are performed on the server side. The patient’s condition can be followed up over a few days and an alarm system may be implemented at the server-side in case of respiration deterioration or when there is a risk of a patient’s need for hospitalization. The risk is determined based on respiration signal features extracted from the received respiration signal including RR, and Autoregressive (AR) moving average (ARMA) model parameters of the signal. Results showed that the presented method can be used at a larger scale enabling health care providers to monitor a large number of patients.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-07-05T07:00:00Z
       
  • WIDEBAND ENERGY HARVESTING FOR IMPLANTABLE BIOMEDICAL APPLICATIONS

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      Authors: Mohammad Reza Balazadeh Bahar, Manouchehr Bahrami, Mohammad Bagher Bannae Sharifian
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      In this paper, a nonresonant electromagnetic micro-generator is proposed. The proposed device is capable of converting nonresonant environmental vibrations to electrical power. The energy harvester could generate output power from heartbeat, human leg and arm motion. The proposed energy harvester uses Frequency up CONVersion technique (FCONV) to improve the bandwidth of the device. The results approve the high bandwidth of the proposed method. The micro-generator is designed by micro-electro-mechanical systems (MEMS) methods. Consequently, the volume of the power harvester is minimized and power density is maximized. The new configuration of energy harvester with imposed motion trigger is proposed. Output power, bandwidth and performance of the designed micro-power harvester are discussed. The proposed micro-generator exhibits higher bandwidth in comparison with resonant, multi-resonant and tunable bandwidth structures. The nonresonant device is designed using FCONV to convert 1–3[math]Hz heartbeat mechanical vibrations to output electrical power. The optimum upconverted mechanical vibration frequency is 60[math]Hz and the output voltage frequency is 120[math]Hz. The peak output electrical power of FCONV is 17.75[math][math]W. For 1[math]Hz, 2[math]Hz and 3[math]Hz mechanical vibration with imposed motion trigger, average output powers are 1.60[math][math]W, 3.81[math][math]W and 5.19[math][math]W, respectively. The achieved results illustrate that the proposed FCONV method exhibits better and wider frequency response in comparison with different methods. The designed device can be utilized to supply implantable biomedical sensors. Also, the heat generation of the device is studied. The results illustrate that the temperature rise of the micro-generator remains in the normal human body temperature range. Hence, the proposed power harvester is biocompatible.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-07-05T07:00:00Z
       
  • AN AUTOMATED METHOD TO DETECT AGE-RELATED MACULAR DEGENERATION FROM
           OPTICAL COHERENCE TOMOGRAPHIC IMAGES

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      Authors: Anju Thomas, P. M. Harikrishnan, Varun P. Gopi, P. Palanisamy
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Age-related macular degeneration (AMD) is an eye disease that affects the elderly. AMD’s prevalence is increasing as society’s population ages; thus, early detection is critical to prevent vision loss in the elderly. Arrangement of a comprehensive examination of the eye for AMD detection is a challenging task. This paper suggests a new poly scale and dual path (PSDP) convolutional neural network (CNN) architecture for early-stage AMD diagnosis automatically. The proposed PSDP architecture has nine convolutional layers to classify the input image as AMD or normal. A PSDP architecture is used to enhance classification efficiency due to the high variation in size and shape of perforation present in OCT images. The poly scale approach employs filters of various sizes to extract features from local regions more effectively. Simultaneously, the dual path architecture incorporates features extracted from different CNN layers to boost features in the global regions. The sigmoid function is used to classify images into binary categories. The Mendeley data set is used to train the proposed network and tested on Mendeley, Duke, SD-OCT Noor, and OCTID data sets. The testing accuracy of the network in Mendeley, Duke, SD-OCT Noor, and OCT-ID is 99.73%,96.66%,94.89%,99.61%, respectively. The comparison with alternative approaches showed that the proposed algorithm is efficient in detecting AMD. Despite having been trained on the Mendeley data set, the proposed model exhibited good detection accuracy when tested on other data sets. This shows that the suggested model can distinguish AMD/Normal images from various data sets. As compared to other methods, the findings show that the proposed algorithm is efficient at detecting AMD. Rapid eye scanning for early detection of AMD could be possible with the proposed architecture. The proposed CNN can be applied in real-time due to its lower complexity and less learnable parameters.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-19T07:00:00Z
       
  • REFLECTED WAVE PARAMETER ESTIMATION AND AUGMENTATION INDEX CALCULATION
           FROM PHOTOPLETHYSMOGRAPHY MODEL FOR DIFFERENT AGE GROUPS

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      Authors: Remya Raj, J. Selvakumar
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      The reflected wave of Photoplethysmography (PPG) is generated due to impedance mismatch, when the pulse wave propagates along the arterial branches. The objective of the study is to estimate the reflected wave amplitude and duration from PPG and to determine whether it changes with age. Reflected wave of PPG was modeled using the lognormal model fitting method. The duration and amplitude of reflected wave and augmentation index from modeled PPG was compared with the Slope Sum Function (SSF) and second derivative of PPG. PPG was collected from 120 subjects with age [math]40 and [math]40 years old for carrying out this experiment. Reflected wave duration from modeled PPG and SSF method shows a mean square error value of [math] and [math] for subjects [math]40 and [math]40 years age, respectively. The amplitude of reflected wave from PPG model and Second Derivative of Photolethysmography (SDPPG) results in a mean square error of [math] and [math] for subjects [math]40 and [math]40 years age, respectively. Augmentation index calculated from PPG model and SDPPG was similar with good level of agreement in the scatter plot. The modeled PPG helps to identify the reflected wave parameter noninvasively and the results show a considerable variation in these parameters with age.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-19T07:00:00Z
       
  • A PRACTICAL NONINVASIVE BLOOD GLUCOSE MEASUREMENT SYSTEM USING
           NEAR-INFRARED SENSORS

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      Authors: Reza Taghizadeh-Behbahani, Reza Boostani, Mehran Yazdi
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Due to the drastic fluctuation of glucose level in diabetic patients throughout a day, especially when they take a sweet breakfast, and greasy lunch and dinner, their insulin must be promptly measured invasively after each meal. However, invasive measurement of the blood glucose level (BGL) is painful and might lead to infection. In this paper, we design a hardware–software system for estimating BGL in a real-time manner, according to the amount of near-infrared (NIR) absorption at 950 nm. The received light wave is amplified and passed through a low-pass filter (LPF) to eliminate the irrelevant frequency components. We have executed our experiment according to three scenarios: (i) Certain amount of glucose is dissolved in water and the glucose concentration is measured by our equipment, (ii) 5-cm3 blood sample of participants is taken and the BGL is measured in a test tube by our apparatus and then this sample is moved to the gold-standard equipment and the actual value of BGL is measured. The measured voltage versus BGL in our designed optical equipment indicates a fairly linear relation between them. (iii) BGL is measured over the fingertip and compared to the gold-standard value. In this case, the measurement error does not exceed 25% and the voltage change with respect to the BGL shows a nonlinear behavior. The designed system has a low cost and provides an acceptable error, making it suitable to be commercialized for diabetic patients.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-19T07:00:00Z
       
  • MATHEMATICAL ANALYSIS OF VISCOSITY AND REABSORPTION ON URINE FLOW THROUGH
           A STRAIGHT NARROW TUBE

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      Authors: Hira Mehboob, Khadija Maqbool, Abdul Majeed Siddiqui, Farah Awan
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      This study investigates the effect of variable viscosity (exponential and linear) and constant reabsorption for the urine flow through a narrow tube. The inertial free flow of viscous fluid has been governed by the momentum and mass conservation through the cross-section of axisymmetric tube. The governing partial differential equations have been simplified with the help of stream function and stress components with exponential and linear variable viscosity. The resulting partial differential equations have been solved by the inverse method and give the explicit expressions for velocity, pressure, shear stress, flux and leakage of flow. It has been observed that flow in transverse direction increases with the increase in reabsorption velocity at wall, whereas horizontal flow, shear stress and volume flow rate become slow with the increase in uniform reabsorption velocity. Effect of viscosity is significant near the walls of tube because the axial velocity accelerates by increasing viscosity parameter due to the pressure gradient near the center of tube but it decelerates near the walls of tube due to surface friction. Also, the special case of variable viscosity is discussed by assuming the linear type of viscosity. The derived data for the velocity and flow rate have been used to measure the fractional reabsorption in proximal tube with varying viscosity near the wall.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-09T07:00:00Z
       
  • PARADIGMS OF COVID 19 AND THE CYTOKINE THERAPY APPROACHES

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      Authors: Muhammad Idrees, Alessandro Nutini, Ayesha Sohail
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      One of the complications caused by the viral agent SARS-CoV2 is atypical pneumonia that occurs classically in viral pathologies. These infection complications produce a sort of “cytokine release syndrome” that sees interleukin 6, a glycosylated protein of approximately 212 amino acids, among the leading players in the inflammatory process. IL-6 typically produces a transient inflammatory state that promotes the hosts immune defence through its pleiotropic function. There is the stimulation of a response in the acute infectious phase, hematopoiesis and the regular advent of immune reactions. The action of the anti-inflammatory cytokines, which tends to regulate the inflammatory ones activity, is directed to the same cells that produce IL-6, which, through an inhibition mechanism, slow down or production ceases altogether. Evidently, in the case of the IL-6 storm, the action of these anti-inflammatory cytokines is insufficient, and the blockade of IL-6R receptors and through the use of monoclonal antibody-like tocilizumab has proved to be optimal to manage complications and avoid potentially fatal situations. Therefore, the purpose of this paper is to create a mathematical model that describes the action of the IL-6 cytokine in SARS-CoV2 virus infection to understand better the extent of the disease itself and the associated severe side effects. We represent the concentration of tocilizumab, soluble IL-6R, absolute neutrophils and circulating platelets using computational modelling. Tocilizumab is administered by intravenous infusions with a minimum dose of 80[math]mg and a maximum dose of 400[math]mg. Following tocilizumab administration, simulation results indicate that the population of absolute neutrophils and circulating platelets is decreasing. After the removal of tocilizumab concentration, both absolute neutrophils and circulating platelets return at their baselines.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-07T07:00:00Z
       
  • EFFECT OF PHYSICAL ACTIVITIES ON HEART RATE VARIABILITY AND SKIN
           CONDUCTANCE

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      Authors: Ankita Soni, Kirti Rawal
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      The sympathetic and parasympathetic function of the Autonomic Nervous System[math]ANS[math] is the primary cause of the variations in Heart Rate and Skin Conductance[math]SC[math] during different physical activities. This paper aims to analyze the effect of different physical activities i.e. (a) Supine (b) Standing and (c) Warm-up, on Heart Rate Variability (HRV) and SC. The standard dataset of 18 subjects has been used to analyze the effect of physical activities on the HRV and SC. In the used dataset, the subjects are in supine, standing, and warm-up positions. The linear methods (time domain & frequency domain) of HRV are implemented on the standard dataset for analyzing the effect of physical activities. It has been observed with the analysis of the HRV that the mean value of time domain methods i.e. the NN interval’s standard deviation (SDNN), the successive RR interval’s root mean square (RMSSD), RR intervals with more than 50 ms differences in between them (NN50), percentage of successive RR intervals that have the difference of more than 50 ms (pNN50) are decreased and the value of Heart rate (HR) increased when the activity has been changed from supine to standing and standing to the warm-up positions. The value of frequency domain methods, such as low frequency (LF) and the ratio of low and high frequency (LF/HF) increased, while the value of HF decreases as activity changes from supine to standing and from supine to warm-up position. Further, the increment is also observed in the value of SC when activity is switched from supine to standing and from standing to the warm-up position. It is concluded from the results that there is a significant decrement that is observed in the value of HRV, while the increment is observed in the value of SC and HR. Decrement of HRV reflects that the sympathetic activity is increased as activity changed from supine to standing and further from standing to warm-up positions.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-07T07:00:00Z
       
  • AI OPTIMIZATION OF THE EXOTHERMIC REACTION OF ETHYLENE OXIDE WITH WATER

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      Authors: Khaled A. Al-Utaibi, Ayesha sohail, Andleeb Zafar, Rana Talha, Sadiq M. Sait
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      A computational framework, for the numerical approximation of the exothermic reaction of ethylene oxide (EO) with water, to form ethylene glycol is presented in this paper. Ethylene Glycol also known as Mono-ethylene Glycol (MEG), is a diol with a boiling of 198[math]C and conventionally produced through hydrolysis of ethylene oxide which is obtained through the oxidation of ethylene. It is used as an excellent automobile coolant as the 1:1 ratio mixture of MEG with Water boils at 129[math]C and freezes at [math]C. Other than its use as an antifreeze, it is also used as a reagent during the production of polyester fibers, pharmaceutics, cosmetics, hydraulic fluids, printing inks, explosives, polyesters and paint solvents. The mathematical model presented here, consists of an energy balance and a material balance system, described in an axisymmetric coordinate system. The optimized resulting values using the artificial intelligence approach are summarized in this paper. We derive an analytical solution. The analytical solution for the mathematical model equations is in general not possible for this model but it may be possible to derive an analytical solution to this mathematical model if we consider the equation for the conservation of material (chemical species) as a formulation for plug flow and isothermal conditions. Noteworthy findings are reported in this paper for future research.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-03T07:00:00Z
       
  • MULTISCALE BSBL COMPRESSED SENSING-BASED ECG SIGNAL COMPRESSION WITH
           ENCODING FOR TELEMEDICINE

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      Authors: K. S. Surekha, B. P. Patil, Ranjeet Kumar, Davinder Pal Sharma
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-06-03T07:00:00Z
       
  • AUTOMATIC SEGMENTATION AND INDICATORS MEASUREMENT OF THE VOCAL FOLDS AND
           GLOTTAL IN LARYNGEAL ENDOSCOPY IMAGES USING MASK R-CNN

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      Authors: Chiun-Li Chin, Chun-Lung Chang, Yu-Chieh Liu, Yong-Long Lin
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      In present clinic practice of otolaryngology, otolaryngologists utilized laryngoscopy to diagnose the larynx lesion of patients preliminarily. Nevertheless, it was challenging for otolaryngologists to interpret the detailed information from laryngoscopy videos comprehensively. In this paper, we proposed Mask R-CNN deep learning algorithm to segment the regions of the vocal folds and glottal from laryngoscopy videos, and self-built algorithm to calculate measured indicators including the length and curvature of vocal folds, the angle of glottal, the area of vocal folds and glottal, and the triangle type composed of vocal folds and glottal. Moreover, in order to provide otolaryngologists critical and immediate medical information during diagnosis, we also provided visualized information, which is labeled on the laryngoscopy images to meet all the needs in clinical practice. From the result of this research, the precision of segmentation has reached a high rate of 90.4% on average. It shows that the model not only achieves great performance in segmentation, but also further proved the indicators are accurate enough to be considered in practical diagnosis. In the future, it is possible for the proposed model to be applied in more kinds of laryngoscopy analyses for more comprehensive diagnosis, which would make a positive influence toward the clinical practice of otolaryngology.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-04-19T07:00:00Z
      DOI: 10.4015/S1016237221500277
       
  • EFFECT OF CHEMICAL REACTION AND THERMAL RADIATION ON AXISYMMETRIC MHD FLOW
           

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      Authors: Sidra Shaheen, Khadija Maqbool, Farah Gul, Ayesha Sohail
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      To prevent the respiratory diseases in an air ways, a defense mechanism based on mucus transport by the moving cilia plays an important role. The mucus transport is affected by the thermal radiation, chemical reaction that changes the physics of fluid due to nanoparticles and thickness of mucus, also different problems in respiratory tract may occur due to the mucus efficacy. In this study, it is observed that the mucus transport can be controlled by the magnetic field that is produced by the drug delivery of nanoparticles, thermal radiation due to temperature difference, porous medium due to respiratory infection, and diffusion of the nanoparticles (chemical reaction) due to the magnetic drug delivery. In this model, flow of Jeffrey nanofluid through the ciliated tube resembles with the mucus flow in a wind pipe. The movement of the mucus is observed by the momentum, energy and concentration equation in the presence of body forces due to magnetic field, heat source due to radiation, Darcy’s resistance due to infection and chemical reaction due to the concentration of nanoparticles. Mathematical model of this study forms a complex system of partial differential equations under the low Reynolds number and long wavelength approximation. The nonlinear set of partial differential equations is solved by the Homotopy perturbation method and software “Mathematica,” results are found for velocity, temperature and concentration profiles and concluded that the mucus flow decelerates due to magnetic field produced by the drug delivery of the nanoparticles but accelerates due to the viscoelastic parameter of Jeffrey fluid and Darcy’s resistance parameter due to infection. The heat transfer rate in the mucus flow rises by increasing the random motion and reduces by the radiation and energy loss. The diffusion of the nanoparticles in the mucus rises by the growing values of thermophoresis and chemical reaction parameter and reduces by the growing values of viscoelastic and Brownian motion parameter.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-04-08T07:00:00Z
      DOI: 10.4015/S1016237221500253
       
  • RECONSTRUCTION OF HIGH-RESOLUTION HEPATIC TUMOR CT IMAGES USING AN
           AUGMENTATION-BASED SUPER-RESOLUTION TECHNIQUE

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      Authors: Moslem Farhadi, Amir Hossein Foruzan, Mina Esfandiarkhani, Yen-Wei Chen, Hongjie Hu
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Improving the resolution of medical images is crucial in diagnosis, feature extraction, and data retrieval. A significant group of super-resolution algorithms is multi-frame techniques. However, they are not appropriate to medical data since they need several frames of the same scene, which bring a high risk of radiation or require a considerable acquisition time. We propose a new data augmentation technique and employ it in a multi-frame image reconstruction algorithm to improve the resolution of pathologic liver CT images. The input to our algorithm is a 3D CT-scan of the abdominal region. Neighboring slices are considered to increase the resolution of a single slice. Augmented slices are prepared using the nearby slices and the interpolation approach. The new data is aligned to the original slice, and it is used as an augmented version of the data. Then, a multi-frame scheme is utilized to reconstruct the high-resolution image. Our method’s novelty is to remove the need for multiple scans of a patent to employ multi-resolution techniques in medical applications. The results reveal that the proposed method is superior to conventional interpolation methods and available augmentation techniques. Compared to the tricubic interpolation, the proposed method improved the PSNR by 3.1. Concerning conventional augmentation techniques, it enhanced the SSIM measure by 0.06. The proposed algorithm improved the SSIM by 0.11 compared to traditional interpolation techniques and 0.1 for recent researches. Therefore, a multi-frame super-resolution technique has the potential to reconstruct medical data better.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-04-07T07:00:00Z
      DOI: 10.4015/S1016237221500265
       
  • HYBRID CLASSIFICATION STRATEGY OF EMG SIGNALS FOR ROBOTIC HAND CONTROL

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      Authors: Fazia sbargoud, Mohamed Djeha, Mohamed Guiatni, Noureddine Ababou
      Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print.
      Among the different bio-signals modalities, Electromyographic signal (EMG) has been one of the frequently used signals in the bio-robotics applications field. This is due to the fact that the EMG reflects directly the muscle activity of the user following the human motion intention. Consequently, the decoding of this intention is an essential task for controlling devices such as prosthetic hands and exoskeletons, based on EMG signals. This paper deals with the processing of EMG signals of the forearm muscles, in order to control two degrees of freedom (2 DoFs) robotic hand. The main contribution of this paper is the proposal of a hybrid approach that combines a pattern and a non-pattern recognition-based strategy. The proposed approach aims to take advantage of both strategies and overcome their shortcomings leading to a better analysis of the user movement intention. The EMG recorded signals are processed for feature extraction based on a Wavelet Packet Decomposition (WPD) method and classification using an Artificial Neural Network (ANN). Furthermore, we investigate the effect of the various parameters such as the applied force level, the number of the EMG channels and the window length of the EMG signal. The proposed approach is validated experimentally under realistic conditions. Very interesting results have been obtained for user intention decoding.
      Citation: Biomedical Engineering: Applications, Basis and Communications
      PubDate: 2021-03-16T07:00:00Z
      DOI: 10.4015/S1016237221500150
       
 
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