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  Subjects -> ENGINEERING (Total: 2358 journals)
    - CHEMICAL ENGINEERING (201 journals)
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    - ENGINEERING (1240 journals)
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ENGINEERING (1240 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 8)
3D Research     Hybrid Journal   (Followers: 20)
AAPG Bulletin     Hybrid Journal   (Followers: 8)
AASRI Procedia     Open Access   (Followers: 14)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 264)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 6)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 3)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Active and Passive Electronic Components     Open Access   (Followers: 7)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 7)
Advanced Science     Open Access   (Followers: 5)
Advanced Science Focus     Free   (Followers: 5)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 7)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 18)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 6)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 15)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 29)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 13)
Advances in Polymer Science     Hybrid Journal   (Followers: 43)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 43)
Advances in Science and Research (ASR)     Open Access   (Followers: 4)
Aerobiologia     Hybrid Journal   (Followers: 2)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 6)
AIChE Journal     Hybrid Journal   (Followers: 35)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access   (Followers: 1)
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 26)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 10)
American Journal of Engineering Education     Open Access   (Followers: 9)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 7)
Antarctic Science     Hybrid Journal   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 6)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 18)
Applied Clay Science     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 4)
Applied Sciences     Open Access   (Followers: 3)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 8)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 3)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Avances en Ciencias e Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Batteries     Open Access   (Followers: 6)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 26)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 4)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 1)
BER : Motor Trade Survey     Full-text available via subscription  
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 1)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 10)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 2)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     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)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 13)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 14)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 31)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 42)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 6)
Case Studies in Thermal Engineering     Open Access   (Followers: 5)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 2)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 7)
Catalysis Science and Technology     Free   (Followers: 8)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 7)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
Central European Journal of Engineering     Hybrid Journal  
CFD Letters     Open Access   (Followers: 6)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencias Holguin     Open Access   (Followers: 2)
CienciaUAT     Open Access   (Followers: 1)
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Full-text available via subscription   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 13)
City, Culture and Society     Hybrid Journal   (Followers: 20)
Clay Minerals     Full-text available via subscription   (Followers: 10)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
Coal Science and Technology     Full-text available via subscription   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 11)
Coastal Engineering Journal     Hybrid Journal   (Followers: 5)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 2)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 13)
Communications Engineer     Hybrid Journal   (Followers: 1)
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: 7)
Composite Structures     Hybrid Journal   (Followers: 270)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 207)
Composites Part B : Engineering     Hybrid Journal   (Followers: 245)
Composites Science and Technology     Hybrid Journal   (Followers: 182)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 15)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Science and Engineering     Open Access   (Followers: 19)
Computers & Geosciences     Hybrid Journal   (Followers: 30)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 8)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 5)
Computers and Geotechnics     Hybrid Journal   (Followers: 11)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 33)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 8)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 9)
Control Engineering Practice     Hybrid Journal   (Followers: 43)
Control Theory and Informatics     Open Access   (Followers: 8)
Corrosion Science     Hybrid Journal   (Followers: 25)

        1 2 3 4 5 6 7 | Last

Journal Cover Biomedical Engineering Letters
  [SJR: 0.339]   [H-I: 8]   [5 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2093-9868 - ISSN (Online) 2093-985X
   Published by Springer-Verlag Homepage  [2352 journals]
  • Deep-learning-based automatic computer-aided diagnosis system for diabetic
    • Authors: Romany F. Mansour
      Pages: 41 - 57
      Abstract: The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy—94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR.
      PubDate: 2018-02-01
      DOI: 10.1007/s13534-017-0047-y
      Issue No: Vol. 8, No. 1 (2018)
  • Multiscale self-quotient filtering for an improved unsupervised retinal
           blood vessels characterisation
    • Authors: D. Relan; R. Relan
      Pages: 59 - 68
      Abstract: Digital images often suffer from contrast variability and non-uniform illumination, which seriously affect the evaluation of biomarkers such as the arteriolar to venular ratio. This biomarker provides valuable information about many pathological conditions such as diabetes, hypertension etc. Hence, in order to efficiently estimate the biomarkers, correct classification of retinal vessels extracted from digital images, into arterioles and venules is an important research problem. This paper presents an unsupervised retinal vessel classification approach which utilises the multiscale self-quotient filtering, to pre-process the input image before extracting the discriminating features. Thereafter the squared-loss mutual information clustering method is used for the unsupervised classification of retinal vessels. The proposed vessel classification method was evaluated on the publicly available DRIVE and INSPIRE-AVR databases. The proposed unclassified framework resulted in 93.2 and 88.9% classification rate in zone B for the DRIVE and the INSPIRE-AVR dataset respectively. The proposed method outperformed other tested methods available in the literature. Retinal vessel classification, in an unsupervised setting is a challenging task. The present framework provided high classification rate and therefore holds a great potential to aid computer aided diagnosis and biomarker research.
      PubDate: 2018-02-01
      DOI: 10.1007/s13534-017-0040-5
      Issue No: Vol. 8, No. 1 (2018)
  • Automatic heart activity diagnosis based on Gram polynomials and
           probabilistic neural networks
    • Authors: Francesco Beritelli; Giacomo Capizzi; Grazia Lo Sciuto; Christian Napoli; Francesco Scaglione
      Pages: 77 - 85
      Abstract: The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.
      PubDate: 2018-02-01
      DOI: 10.1007/s13534-017-0046-z
      Issue No: Vol. 8, No. 1 (2018)
  • The research of sleep staging based on single-lead electrocardiogram and
           deep neural network
    • Authors: Ran Wei; Xinghua Zhang; Jinhai Wang; Xin Dang
      Pages: 87 - 93
      Abstract: The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen’s kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.
      PubDate: 2018-02-01
      DOI: 10.1007/s13534-017-0044-1
      Issue No: Vol. 8, No. 1 (2018)
  • Naphthalocyanines as contrast agents for photoacoustic and multimodal
    • Authors: Upendra Chitgupi; Jonathan F. Lovell
      Abstract: Naphthalocyanines (Ncs) are a family of aromatic small molecule with large near infrared extinction coefficients, making them appealing contrast agent candidates for photoacoustic imaging (PAI). Depending on the substitutions on the Nc periphery or metal center, different spectrally-resolved absorption peak wavelengths are possible, which can enable photoacoustic contrast multiplexing. Owing to their generally poor aqueous solubility, approaches have been developed to modify Ncs or formulate them as biocompatible contrast agents for PAI. Due to their inherent capacity for metal ion chelation, Ncs hold potential for complementary multimodal contrast imaging techniques such as 64Cu positron emission tomography. In this research perspective, we highlight some recent reports involving the use of Ncs in PAI.
      PubDate: 2018-03-07
      DOI: 10.1007/s13534-018-0059-2
  • Fast photoacoustic imaging systems using pulsed laser diodes: a review
    • Authors: Paul Kumar Upputuri; Manojit Pramanik
      Abstract: Photoacoustic imaging (PAI) is a newly emerging imaging modality for preclinical and clinical applications. The conventional PAI systems use Q-switched Nd:YAG/OPO (Optical Parametric Oscillator) nanosecond lasers as excitation sources. Such lasers are expensive, bulky, and imaging speed is limited because of low pulse repetition rate. In recent years, the semiconductor laser technology has advanced to generate high-repetitions rate near-infrared pulsed lasers diodes (PLDs) which are reliable, less-expensive, hand-held, and light-weight, about 200 g. In this article, we review the development and demonstration of PLD based PAI systems for preclinical and clinical applications reported in recent years.
      PubDate: 2018-03-06
      DOI: 10.1007/s13534-018-0060-9
  • Machine learning in biomedical engineering
    • Authors: Cheolsoo Park; Clive Cheong Took; Joon-Kyung Seong
      PubDate: 2018-02-06
      DOI: 10.1007/s13534-018-0058-3
  • Increasing the quality of reconstructed signal in compressive sensing
           utilizing Kronecker technique
    • Authors: H. Zanddizari; S. Rajan; Houman Zarrabi
      Abstract: Quality of reconstruction of signals sampled using compressive sensing (CS) algorithm depends on the compression factor and the length of the measurement. A simple method to pre-process data before reconstruction of compressively sampled signals using Kronecker technique that improves the quality of recovery is proposed. This technique reduces the mutual coherence between the projection matrix and the sparsifying basis, leading to improved reconstruction of the compressed signal. This pre-processing method changes the dimension of the sensing matrix via the Kronecker product and sparsity basis accordingly. A theoretical proof for decrease in mutual coherence using the proposed technique is also presented. The decrease of mutual coherence has been tested with different projection matrices and the proposed recovery technique has been tested on an ECG signal from MIT Arrhythmia database. Traditional CS recovery algorithms has been applied with and without the proposed technique on the ECG signal to demonstrate increase in quality of reconstruction technique using the new recovery technique. In order to reduce the computational burden for devices with limited capabilities, sensing is carried out with limited samples to obtain a measurement vector. As recovery is generally outsourced, limitations due to computations do not exist and recovery can be done using multiple measurement vectors, thereby increasing the dimension of the projection matrix via the Kronecker product. The proposed technique can be used with any CS recovery algorithm and be regarded as simple pre-processing technique during reconstruction process.
      PubDate: 2018-01-31
      DOI: 10.1007/s13534-018-0057-4
  • Spectrum analysis for assessing red blood cell aggregation using
           high-frequency ultrasound array transducer
    • Authors: Changhan Yoon
      Pages: 273 - 279
      Abstract: The purpose of this study is to investigate a spectrum analysis technique for detecting and monitoring red blood cell (RBC) aggregation using a high-frequency array transducer. To assess the feasibility of this approach, the backscattered radio-frequency signal from non-aggregated and aggregated RBC samples with two hematocrit levels were acquired by using a 30-MHz linear array transducer and analyzed in frequency domain. Three parameters such as spectral slope, midband fit and Y intercept were extracted in a static condition. Fresh porcine blood was used and degrees of aggregation were changed by diluting plasma concentration. From the experiments, it was demonstrated that the spectral slope related to a size of scatterer progressively declined as the level of aggregation increased; its mean values at hematocrit of 40% were 1.10 and −0.22 dB/MHz for RBCs suspended in isotonic phosphate buffered saline and solution with 70% plasma concentrations, respectively. For the midband fit and Y intercept, the mean values were increased by 9.1 and 46.4 dB, respectively. These results indicated that the spectrum analysis technique is useful for monitoring RBC aggregation and can be potentially developed for assessing aggregation in clinical applications.
      PubDate: 2017-11-01
      DOI: 10.1007/s13534-017-0034-3
      Issue No: Vol. 7, No. 4 (2017)
  • Sex differences of cognitive load effects on object-location binding
    • Authors: Jinsick Park; Ga In Shin; Young Min Park; In Young Kim; Dong Pyo Jang
      Pages: 305 - 309
      Abstract: In this study, we investigated where the sex differences of object-location binding memory performance were influenced by the cognitive load. We used the fractal objects version of the ‘What was where'’ task to measure object memory, location memory and objection-location binding memory. Cognitive load was controlled by task difficulty presented two sessions: one session randomly displayed three or four fractal objects (Session 34) and the other session four or five objects (Session 45). The results showed that females outperformed males on object-location binding memory. Interestingly, even when the four object trials were compared between Session 34 and Session 45, in which we believed that the level of difficulty was similar while cognitive load varied, the swap error of males was significantly increased in Session 45 compared to females. In conclusion, there may be sex differences in object-location binding memory and the males could be more sensitive about the cognitive load than females.
      PubDate: 2017-11-01
      DOI: 10.1007/s13534-017-0038-z
      Issue No: Vol. 7, No. 4 (2017)
  • ECG arrhythmia classification using time frequency distribution techniques
    • Authors: Safa Sultan Qurraie; Rashid Ghorbani Afkhami
      Pages: 325 - 332
      Abstract: In this paper, we focus on classifying cardiac arrhythmias. The MIT-BIH database is used with 14 original classes of labeling which is then mapped into 5 more general classes, using the Association for the Advancement of Medical Instrumentation standard. Three types of features were selected with a focus on the time–frequency aspects of ECG signal. After using the Wigner–Ville distribution the time–frequency plane is split into 9 windows considering the frequency bandwidth and time duration of ECG segments and peaks. The summation over these windows are employed as pseudo-energy features in classification. The “subject-oriented” scheme is used in classification, meaning the train and test sets include samples from different subjects. The subject-oriented method avoids the possible overfitting issues and guaranties the authenticity of the classification. The overall sensitivity and positive predictivity of classification is 99.67 and 98.92%, respectively, which shows a significant improvement over previous studies.
      PubDate: 2017-11-01
      DOI: 10.1007/s13534-017-0043-2
      Issue No: Vol. 7, No. 4 (2017)
  • Adaptive filtering method for EMG signal using bounded range artificial
           bee colony algorithm
    • Authors: Agya Ram Verma; Yashvir Singh; Bhumika Gupta
      Abstract: In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal.
      PubDate: 2017-12-28
      DOI: 10.1007/s13534-017-0056-x
  • Obstructive sleep apnoea detection using convolutional neural network
           based deep learning framework
    • Authors: Debangshu Dey; Sayanti Chaudhuri; Sugata Munshi
      Abstract: This letter presents an automated obstructive sleep apnoea (OSA) detection method with high accuracy, based on a deep learning framework employing convolutional neural network. The proposed work develops a system that takes single lead electrocardiography signals from patients for analysis and detects the OSA condition of the patient. The results show that the proposed method has some advantages in solving such problems and it outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the detection of OSA. The proposed network performs both feature learning and classifies the features in a supervised manner. The scheme is computation-intensive, but can achieve very high degree of accuracy—on an average a margin of more than 9% compared to other published literature till date. The method also has a good immunity to the contamination of the signals by noise. Even with pessimistic signal to noise ratio values considered here, the methods already reported are not able to outshine the present method. The software for the algorithm reported here can be a good contender to constitute a module that can be integrated with a portable medical diagnostic system.
      PubDate: 2017-12-14
      DOI: 10.1007/s13534-017-0055-y
  • First demonstration of velocity selective recording from the pig vagus
           using a nerve cuff shows respiration afferents
    • Authors: B. W. Metcalfe; T. N. Nielsen; N. de N. Donaldson; A. J. Hunter; J. T. Taylor
      Abstract: Neural interfaces have great potential to treat disease and disability by modulating the electrical signals within the nervous system. However, whilst neural stimulation is a well-established technique, current neural interfaces are limited by poor recording ability. Low signal amplitudes necessitate the use of highly invasive techniques that divide or penetrate the nerve, and as such are unsuitable for chronic implantation. In this paper, we present the first application of the velocity selective recording technique to the detection of respiration activity in the vagus nerve, which is involved with treatments for epilepsy, depression, and rheumatoid arthritis. Further, we show this using a chronically implantable interface that does not divide the nerve. We also validate our recording setup using electrical stimulation and we present an analysis of the recorded signal amplitudes. The recording interface was formed from a cuff containing ten electrodes implanted around the intact right vagus nerve of a Danish Landrace pig. Nine differential amplifiers were connected to adjacent electrodes, and the resulting signals were processed to discriminate neural activity based on conduction velocity. Despite the average single channel signal-to-noise ratio of − 5.8 dB, it was possible to observe distinct action potentials travelling in both directions along the nerve. Further, contrary to expectation given the low signal-to-noise ratio, we have shown that it was possible to identify afferent neural activity that encoded respiration. The significance of this is the demonstration of a chronically implantable method for neural recording, a result that will transform the capabilities of future neuroprostheses.
      PubDate: 2017-11-22
      DOI: 10.1007/s13534-017-0054-z
  • A computational model of ureteral peristalsis and an investigation into
           ureteral reflux
    • Authors: G. Hosseini; C. Ji; D. Xu; M. A. Rezaienia; E. Avital; A. Munjiza; J. J. R. Williams; J. S. A. Green
      Abstract: The aim of this study is to create a computational model of the human ureteral system that accurately replicates the peristaltic movement of the ureter for a variety of physiological and pathological functions. The objectives of this research are met using our in-house fluid-structural dynamics code (CgLes–Y code). A realistic peristaltic motion of the ureter is modelled using a novel piecewise linear force model. The urodynamic responses are investigated under two conditions of a healthy and a depressed contraction force. A ureteral pressure during the contraction shows a very good agreement with corresponding clinical data. The results also show a dependency of the wall shear stresses on the contraction velocity and it confirms the presence of a high shear stress at the proximal part of the ureter. Additionally, it is shown that an inefficient lumen contraction can increase the possibility of a continuous reflux during the propagation of peristalsis.
      PubDate: 2017-11-18
      DOI: 10.1007/s13534-017-0053-0
  • Elastography for portable ultrasound
    • Authors: Bonghun Shin; Soo Jeon; Jeongwon Ryu; Hyock Ju Kwon
      Abstract: Portable wireless ultrasound has been emerging as a new ultrasound device due to its unique advantages including small size, lightweight, wireless connectivity and affordability. Modern portable ultrasound devices can offer high quality sonogram images and even multiple ultrasound modes such as color Doppler, echocardiography, and endovaginal examination. However, none of them can provide elastography function yet due to the limitations in computational performance and data transfer speed of wireless communication. Also phase-based strain estimator (PSE) that is commonly used for conventional elastography cannot be adopted for portable ultrasound, because ultrasound parameters such as data dumping interval are varied significantly in the practice of portable ultrasound. Therefore, this research aims to propose a new elastography method suitable for portable ultrasound, called the robust phase-based strain estimator (RPSE), which is not only robust to the variation of ultrasound parameters but also computationally effective. Performance and suitability of RPSE were compared with other strain estimators including time-delay, displacement-gradient and phase-based strain estimators (TSE, DSE and PSE, respectively). Three types of raw RF data sets were used for validation tests: two numerical phantom data sets modeled by an open ultrasonic simulation code (Field II) and a commercial FEA (Abaqus), and the one experimentally acquired with a portable ultrasound device from a gelatin phantom. To assess image quality of elastograms, signal-to-noise (SNRe) and contrast-to-noise (CNRe) ratios were measured on the elastograms produced by each strain estimator. The computational efficiency was also estimated and compared. Results from the numerical phantom experiment showed that RPSE could achieve highest values of SNRe and CNRe (around 5.22 and 47.62 dB) among all strain estimators tested, and almost 10 times higher computational efficiency than TSE and DSE (around 0.06 vs. 5.76 s per frame for RPSE and TSE, respectively).
      PubDate: 2017-10-27
      DOI: 10.1007/s13534-017-0052-1
  • Performance of machine learning methods in diagnosing Parkinson’s
           disease based on dysphonia measures
    • Authors: Salim Lahmiri; Debra Ann Dawson; Amir Shmuel
      Abstract: Parkinson’s disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were considered and trained with a set of twenty-two voice disorder measurements to classify healthy and PD patients. These machine learning methods included linear discriminant analysis (LDA), k nearest-neighbors (k-NN), naïve Bayes (NB), regression trees (RT), radial basis function neural networks (RBFNN), support vector machine (SVM), and Mahalanobis distance classifier. We evaluated the performance of these methods by means of a tenfold cross validation protocol. Experimental results show that the SVM classifier achieved higher average performance than all other classifiers in terms of overall accuracy, G-mean, and area under the curve of the receiver operating characteristic plot. The SVM classifier achieved higher performance measures than the majority of the other classifiers also in terms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest average precision. The RBFNN method yielded the highest F-measure.; however, it performed poorly in terms of other performance metrics. Finally, t tests were performed to evaluate statistical significance of the results, confirming that the SVM outperformed most of the other classifiers on the majority of performance measures. SVM is a promising method for identifying PD patients based on classification of dysphonia measurements.
      PubDate: 2017-10-12
      DOI: 10.1007/s13534-017-0051-2
  • Computer-assisted brain tumor type discrimination using magnetic resonance
           imaging features
    • Authors: Sajid Iqbal; M. Usman Ghani Khan; Tanzila Saba; Amjad Rehman
      Abstract: Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.
      PubDate: 2017-10-04
      DOI: 10.1007/s13534-017-0050-3
  • PID controller design to generate pulsatile flow rate for in vitro
           experimental studies of physiological flows
    • Authors: Mohammad Reza Najjari; Michael W. Plesniak
      Abstract: Producing accurate pulsatile flow rates is essential for many in vitro experimental studies in biofluid dynamics research. A controller system was developed to control a flow loop to produce easily adjustable pulsatile flow rates with sufficient accuracy. An Arduino board is used as a micro-controller to control a pump to produce various pulsatile flow rates, and an open-source proportional-integral-derivative (PID) control algorithm is developed for this purpose. Four non-trivial pulsatile waveforms were produced by the PID controller, as well as an iterative controller, and the performance of both controllers was evaluated. Both the PID and iterative controllers were able to successfully produce slowly-varying signals (single and multi-harmonic low frequency sine waves), but for high frequency signals where the flow has strong acceleration/deceleration (e.g. for physiological waveforms) the iterative controller exhibited significant undershoot. The comparison of PID and iterative controllers suggests that if the desired flow rate is a low frequency, simple waveform then the iterative controller is preferred due to simplicity of implementation. However, if the desired signal is rapidly changing and more complicated then the PID controller achieves better results. This system can be implemented in many flow loops due to its simplicity and low cost, and does not require a mathematical model of the system.
      PubDate: 2017-09-30
      DOI: 10.1007/s13534-017-0049-9
  • Gastrointestinal polyp detection in endoscopic images using an improved
           feature extraction method
    • Authors: Mustain Billah; Sajjad Waheed
      Abstract: Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.
      PubDate: 2017-09-07
      DOI: 10.1007/s13534-017-0048-x
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