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Journal Cover Biocybernetics and Biological Engineering
  [SJR: 0.279]   [H-I: 8]   [5 followers]  Follow
    
   Full-text available via subscription Subscription journal
   ISSN (Print) 0208-5216
   Published by Elsevier Homepage  [3175 journals]
  • A segment-wise reconstruction method based on bidirectional long short
           term memory for Power Line Interference suppression
    • Authors: Yue Qiu; Kejie Huang; Feng Xiao; Haibin Shen
      Pages: 217 - 227
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 2
      Author(s): Yue Qiu, Kejie Huang, Feng Xiao, Haibin Shen
      The overlap between the signal components of Power Line Interference (PLI) and biomedical signals in the frequency domain makes the filtered results prone to severe distortion. Electrocardiogram (ECG) is a type of biomedical electronic signal used for cardiac diagnosis. The objective of this work is to suppress the PLI components from biomedical signals with minimal distortion, and the object of study is mainly the ECG signals. In this study, we propose a novel segment-wise reconstruction method to suppress the PLI in biomedical signals based on the Bidirectional Recurrent Neural Networks with Long Short Term Memory (Bi-LSTM). Experiments are conducted on both synthetic and real signals, and quantitative comparisons are made with a traditional IIR notch filter and two state-of-the-art methods in the literature. The results show that by our method, the output Signal-to-Noise Ratio (SNR) is improved by more than 7dB and the settling time for step response is reduced to 0.09s on average. The results also demonstrate that our method has enough generalization ability for unforeseen signals without retraining.

      PubDate: 2018-02-26T21:34:15Z
      DOI: 10.1016/j.bbe.2018.01.003
       
  • Numerical simulations of the pulsatile blood flow in the different types
           of arterial fenestrations: Comparable analysis of multiple vascular
           geometries
    • Authors: Zbigniew Tyfa; Damian Obidowski; Piotr Reorowicz; Ludomir Stefańczyk; Jan Fortuniak; Krzysztof Jóźwik
      Pages: 228 - 242
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 2
      Author(s): Zbigniew Tyfa, Damian Obidowski, Piotr Reorowicz, Ludomir Stefańczyk, Jan Fortuniak, Krzysztof Jóźwik
      In medical terms, fenestration stands for an anomaly within the circulatory system in which the blood vessel lumen is divided into two separate channels that rejoin in the distal part of this vessel. The primary objective of this research was to analyze the impact of the left vertebral artery (LVA) and basilar artery (BA) fenestrations on the blood flow characteristics in their regions and downstream, in the cerebral circulation. The geometrical data, obtained from the angio-Computed Tomography, were the basis for the generation of a 3D model in SolidWorks 2015. In order to observe the flow characteristics within the whole spatial domain, computational fluid dynamics was involved in performing simulations of the blood flow in the patient-specific arterial system (beginning with the aortic arch and finishing with the Circle of Willis). To examine the flow distribution changes resulting from altered fenestration geometries, additional models were built. The blood flow velocity, volume flow rate and shear stress distribution were analyzed within this study. It was proven that the length/size/position of the fenestration altered the flow characteristics in different manners. The investigations showed that the patient-specific LVA, at the V3 section (extracranial part of the artery located between the spine and the skull), is not a reason of aneurysm formation. However, BA fenestration at the proximal segment might be a possible reason of future aneurysm formation. It was proven that the computational fluid dynamics tool could support medical diagnostic procedures and multivessel brain vascular disease treatment planning.

      PubDate: 2018-03-07T23:02:06Z
      DOI: 10.1016/j.bbe.2018.01.004
       
  • An improved feature based image fusion technique for enhancement of liver
           lesions
    • Authors: P. Sreeja; S. Hariharan
      Abstract: Publication date: Available online 19 March 2018
      Source:Biocybernetics and Biomedical Engineering
      Author(s): P. Sreeja, S. Hariharan
      This paper describes two methods for enhancement of edge and texture of medical images. In the first method optimal kernel size of range filter suitable for enhancement of liver and lesions is deduced. The results have been compared with conventional edge detection algorithms. In the second method the feasibility of feature based pixel wise image fusion for enhancing abdominal images is investigated. Among the different algorithms developed in the medical image fusion pixel level fusion is capable of retaining the maximum relevant information with better implementation and computational efficiency. Conventional image fusion includes multi-modal fusion and multi-resolution fusion. The present work attempts to fuse together, texture enhanced and edge enhanced images of the input image in order to obtain significant enhancement in the output image. The algorithm is tested in low contrast medical images. The result shows an improvement in contrast and sharpness of output image which will provide a basis for a better visual interpretation leading to more accurate diagnosis. Qualitative and quantitative performance evaluation is done by calculating information entropy, MSE, PSNR, SSIM and Tenengrad values.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      PubDate: 2018-02-18T20:50:29Z
      DOI: 10.1016/j.bbe.2018.01.001
       
  • Development of a practical high frequency brain–computer interface based
           on steady-state visual evoked potentials using a single channel of EEG
    • Authors: Saba Ajami; Amin Mahnam; Vahid Abootalebi
      Pages: 106 - 114
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 1
      Author(s): Saba Ajami, Amin Mahnam, Vahid Abootalebi
      Brain–computer interfaces based on steady-state visual evoked potentials have recently gained increasing attention due to high performance and minimal user training. Stimulus frequencies in the range of 4–60Hz have been used in these systems. However, eye fatigue when looking at low-frequency flickering lights, higher risk of induced epileptic seizure for medium-frequency flickers, and low signal amplitude for high-frequency flickers complicate appropriate selection of flickering frequencies. Here, different flicker frequencies were evaluated for development of a brain–computer interface speller that ensures user's comfort as well as the system's efficiency. A frequency detection algorithm was also proposed based on Least Absolute Shrinkage and Selection Operator estimate that provides excellent accuracy using only a single channel of EEG. After evaluation of the SSVEP responses in the range of 6–60Hz, three stimulus frequency sets of 30–35, 35–40 and 40–45Hz were adopted and the system's performance and corresponding eye fatigue were compared. While the accuracy of the asynchronous speller for all three stimulus frequency sets was close to the maximum (average 97.6%), repeated measures ANOVA demonstrated that the typing speed for 30–35Hz (8.09char/min) and 35–40Hz (8.33char/min) are not significantly different, but are significantly higher than for 40–45Hz (6.28char/min). On the other hand, the average eye fatigue scale for 35–40Hz (80%) is comparable to that for 40–45Hz (85%), but very higher than for 30–35Hz (60%). Therefore, 35–40Hz range was proposed for the system which resulted in 99.2% accuracy and 67.1bit/min information transfer rate.

      PubDate: 2017-11-18T20:21:15Z
      DOI: 10.1016/j.bbe.2017.10.004
       
  • A bionic hand controlled by hand gesture recognition based on surface EMG
           signals: A preliminary study
    • Authors: Wan-Ting Shi; Zong-Jhe Lyu; Shih-Tsang Tang; Tsorng-Lin Chia; Chia-Yen Yang
      Pages: 126 - 135
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 1
      Author(s): Wan-Ting Shi, Zong-Jhe Lyu, Shih-Tsang Tang, Tsorng-Lin Chia, Chia-Yen Yang
      A bionic hand with fine motor ability could be a favorable option for replacing the human hand when performing various operations. Myoelectric control has been widely used to recognize hand movements in recent years. However, most of the previous studies have focused on whole-hand movements, with only a few investigating subtler motions. The aim of this study was to construct a prototype system for recognizing hand postures with the aim of controlling a bionic hand by analyzing sEMG signals measured at the flexor digitorum superficialis and extensor digitorum muscles. We adopted multiple features commonly used in previous studies—mean absolute value, zero crossing, slope sign change, and waveform length—in the algorithm for extracting hand-posture features, and the k-nearest-neighbors (KNN) algorithm as the classifier to perform hand-posture recognition. The bionic hand was controlled by an Arduino microprocessor, which converted the signals received from the classification process that were fed to the servo motors controlling the bionic fingers. We constructed a two-channel sEMG pattern-recognition system that can identify human hand postures and control a homemade bionic hand to perform corresponding hand postures. The KNN approach was able to recognize four different hand postures with a classification accuracy of 94% in the online experiment by using the channel combination. Moreover, the experimental tests show that the bionic hand could faithfully imitate the hand postures of the human hand. This study has bridged the gap between the features of sEMG signals of fingers and the postures of a bionic hand.

      PubDate: 2017-12-17T13:52:18Z
      DOI: 10.1016/j.bbe.2017.11.001
       
  • Automated quantification of ultrasonic fatty liver texture based on
           curvelet transform and SVD
    • Authors: R. Bharath; Pradeep Kumar Mishra; P. Rajalakshmi
      Abstract: Publication date: Available online 27 December 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): R. Bharath, Pradeep Kumar Mishra, P. Rajalakshmi
      Fatty liver is a prevalent disease and is the major cause for the dysfunction of the liver. If fatty liver is untreated, it may progress into chronic diseases like cirrhosis, hepatocellular carcinoma, liver cancer, etc. Early and accurate detection of fatty liver is crucial to prevent the fatty liver progressing into chronic diseases. Based on the severity of fat, the liver is categorized into four classes, namely Normal, Grade I, Grade II and Grade III respectively. Ultrasound scanning is the widely used imaging modality for diagnosing the fatty liver. The ultrasonic texture of liver parenchyma is specific to the severity of fat present in the liver and hence we formulated the quantification of fatty liver as a texture discrimination problem. In this paper, we propose a novel algorithm to discriminate the texture of fatty liver based on curvelet transform and SVD. Initially, the texture image is decomposed into sub-band images with curvelet transform enhancing gradients and curves in the texture, then an absolute mean of the singular values are extracted from each curvelet decomposed image, and used it as a feature representation for the texture. Finally, a cubic SVM classifier is used to classify the texture based on the extracted features. Tested on a database of 1000 image textures with 250 image textures belonging to each class, the proposed algorithm gave an accuracy of 96.9% in classifying the four grades of fat in the liver.

      PubDate: 2017-12-27T14:32:18Z
      DOI: 10.1016/j.bbe.2017.12.004
       
  • Nonsubsampled shearlet domain fusion techniques for CT–MR neurological
           images using improved biological inspired neural model
    • Authors: Deep Gupta
      Abstract: Publication date: Available online 27 December 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Deep Gupta
      The fusion of multimodality medical images performs a very crucial role in the clinical diagnosis, analysis and the treatment of especially in critical diseases. It is considered as an assisted approach for the radiologist by providing the composite images having significant diagnostic information acquired from the source images. The main purpose of this work is to develop an efficient framework for fusing the multimodal medical images. Three different fusion techniques are proposed in this paper that presents the CT and MR medical image fusion in nonsubsampled shearlet transform (NSST) domain using the adaptive spiking neural model. The NSST having different features and a competent depiction of the image coefficients provides several directional decomposition coefficients. Maximum selection approach and regional energy are utilized for low frequency coefficients fusion. Spatial frequency, novel modified spatial frequency and novel sum modified Laplacian motivated spiking model are used for every high frequency subimage component. Finally, fused images are reconstructed by applying inverse NSST. The performance of proposed fusion techniques is validated by extensive simulations performed on different CT-MR image datasets using proposed and other thirty seven existing fusion approaches in terms of both the subjective and objective manner. The results revealed that the proposed techniques provide better visualization of resultant images and higher quantitative measures compared to several existing fusion approaches.

      PubDate: 2017-12-27T14:32:18Z
      DOI: 10.1016/j.bbe.2017.12.005
       
  • Application of intrinsic band function technique for automated detection
           of sleep apnea using HRV and EDR signals
    • Authors: R.K. Tripathy
      Abstract: Publication date: Available online 22 December 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): R.K. Tripathy
      Sleep apnea is the most common sleep disorder that causes respiratory, cardiac and brain diseases. The heart rate variability (HRV) and the electrocardiogram-derived respiration (EDR) signals to capture the cardio-respiratory information and the features extracted from these two signals have been used for the detection of sleep apnea. Detection of sleep apnea using the combination of HRV and EDR signals may provide more information. This paper proposes a novel method for the automated detection of sleep apnea based on the features extracted from HRV and EDR signals. The method involves the extraction of features from the intrinsic band functions (IBFs) of both EDR and HRV signals, and the classification using kernel extreme learning machine (KELM). The IBFs of HRV and EDR signals are evaluated using the Fourier decomposition method (FDM). The energy and the fuzzy entropy (FE) features are extracted from these IBFs. The kernel extreme learning machine (KELM) classifier with four kernel functions such as ‘linear’, ‘polynomial’, ‘radial basis function (RBF)’ and ‘cosine wavelet kernel’ is used for the automated detection of sleep apnea. The proposed technique yielded a sensitivity and a specificity of 78.02% and 74.64%, respectively using the public database. The method outperformed some of the reported works using HRV and EDR signals.

      PubDate: 2017-12-27T14:32:18Z
      DOI: 10.1016/j.bbe.2017.11.003
       
  • Strabismic amblyopia affects decision processes preceding saccadic
           response
    • Authors: Maciej Perdziak; Dagmara Witkowska; Wojciech Gryncewicz; Jan Ober
      Abstract: Publication date: Available online 17 December 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Maciej Perdziak, Dagmara Witkowska, Wojciech Gryncewicz, Jan Ober


      PubDate: 2017-12-17T13:52:18Z
      DOI: 10.1016/j.bbe.2017.12.003
       
  • Detection of valvular heart diseases using impedance cardiography ICG
    • Authors: Souhir Chabchoub; Sofienne Mansouri; Ridha Ben Salah
      Abstract: Publication date: Available online 11 December 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Souhir Chabchoub, Sofienne Mansouri, Ridha Ben Salah
      Impedance cardiography (ICG) is a simple, non invasive and cost effective tool for estimating stroke volume, cardiac output and other hemodynamic parameters. It has been successfully used to diagnose several cardiovascular diseases, like the heart failure and myocardial infarction. In particular, valvular heart disease (VHD) is characterized by the affection of one or more heart valves: mitral, aortic, tricuspid or pulmonary valves and it is usually diagnosed using the Doppler echocardiography. However, this technique is rather expensive, requires qualified expertise, discontinuous, and often not necessary to make just a simple diagnosis. In this paper, a new computer aided diagnosis system is proposed to detect VHD using the ICG signals. Six types of ICG heartbeats are analyzed and classified: normal heartbeats (N), mitral insufficiency heartbeats (MI), aortic insufficiency heartbeats (AI), mitral stenosis heartbeats (MS), aortic stenosis heartbeats (AS), and pulmonary stenosis heartbeats (PS). The proposed methodology is validated on 120 ICG recordings. Firstly, ICG signal is denoised using the Daubechies wavelet family with order eight (db8). Then, these signals are segmented into several heartbeats and, later, subjected to the Linear Prediction method (LP) and Discrete Wavelet Transform approach (DWT) to extract temporal and time–frequency features, respectively. In order to reduce the number of features and select the most relevant ones among them, the Student t-test is applied. Therefore, a total of 16 features are selected (3 temporal features and 13 time–frequency features). For the classification step, the support vector machine (SVM) and k-Nearest Neighbors (KNN) classifiers are used. Different combinations between extracted features and classifiers are proposed. Hence, experimental results showed that the combination between temporal features, time–frequency features and SVM classifier achieved the highest classification performance in classifying the N, MI, MS, AI, AS and PS heartbeats with 98.94% of overall accuracy.

      PubDate: 2017-12-17T13:52:18Z
      DOI: 10.1016/j.bbe.2017.12.002
       
  • Easy and affordable method for rapid prototyping of tissue models in vitro
           using three-dimensional bioprinting
    • Authors: Ashutosh Bandyopadhyay; Vimal Kumar Dewangan; Kiran Yellappa Vajanthri; Suruchi Poddar; Sanjeev Kumar Mahto
      Abstract: Publication date: Available online 8 December 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Ashutosh Bandyopadhyay, Vimal Kumar Dewangan, Kiran Yellappa Vajanthri, Suruchi Poddar, Sanjeev Kumar Mahto
      In vitro tissue model systems have attracted considerable attention in drug discovery owing to their ability to facilitate identification of promising compounds in the near-physiological environment during drug development. Additive manufacturing helps in mimicking complex geometries including the microarchitecture of the body tissues. Exploiting this emerging technology, the present study demonstrates a simple and inexpensive approach for the fabrication of three-dimensional (3D) in vitro tissue models using a custom-designed automated bioprinting system. The bioink mixture comprised of a novel optimized composition of widely known biomaterials including gelatin, alginate and hydrolyzed type-1 collagen to embed and print the C2C12 myoblast cells. The structural stability and integrity of the cells-laden constructs were found to be significantly consistent for more than 14 days in culture. Rheological and mechanical properties of the bioink blend were characterized to assess its efficacy for the fabrication of cells-laden tissue constructs. Scanning electron micrographs were acquired to analyze porosity of the scaffold for cellular growth and proliferation. The viability of cells embedded within the hydrogel was >80%, 3h post-printing. We anticipate that the fabricated tissues will serve as an alternative model for in vitro toxicological and drug response studies.

      PubDate: 2017-12-17T13:52:18Z
      DOI: 10.1016/j.bbe.2017.12.001
       
  • Parachuting training improves autonomic control of the heart in novice
           parachute jumpers
    • Authors: Krzysztof Mazurek; Nawoja Koprowska; Jan Gajewski; Piotr Zmijewski; Franciszek Skibniewski; Krzysztof Różanowski
      Abstract: Publication date: Available online 6 December 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Krzysztof Mazurek, Nawoja Koprowska, Jan Gajewski, Piotr Zmijewski, Franciszek Skibniewski, Krzysztof Różanowski
      This study aimed to investigate the acute effect of skydiving and the chronic effect of parachute jump training on the cardiac response in novice and trained parachuters. The study included 11 experienced skydivers (expert group), aged 35.9±7.2 years, and 12 students (novice group), aged 27.9±7.2 years. Participants underwent 10-unit training in accelerated freefall (AFF) from an altitude of 4000m. In experts, the highest HR was noted during the phase of opening of the parachute and during the landing phase, and in pre-training novices during the phase of exit from the plane and the descent by parachute. Mean standard deviation of NN intervals (SDNN) was higher in experts than pre-training novices. In novices, post-training values of SDNN, root mean square of successive differences (RMSSD), and the low/high frequency oscillation ratio (LF/HF) were higher, and HF and LF were lower, than pre-training values. In experts the values of SDNN, RMSSD, LF, HF, and total power spectrum (TP) were significantly higher and LF/HF significantly lower than in pre-training novices. Novice compared to experienced skydivers are characterized by higher modulation of the sympathetic, and lower modulation of the parasympathetic autonomic nervous system (ANS). Chronic effects of 10-unit AFF training are characterized by decreased modulation of the sympathetic nervous system, increased total power spectrum of HRV, and increased activity of the parasympathetic nervous system. The changes in ANS modulation suggest that parachute training leads to a reduction of the stress response and improves autonomic control of cardiovascular function in novice skydivers.

      PubDate: 2017-12-17T13:52:18Z
      DOI: 10.1016/j.bbe.2017.11.004
       
  • Novel expert system for glaucoma identification using non-parametric
           spatial envelope energy spectrum with fundus images
    • Authors: U. Raghavendra; Sulatha V. Bhandary; Anjan Gudigar; U. Rajendra Acharya
      Abstract: Publication date: Available online 29 November 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): U. Raghavendra, Sulatha V. Bhandary, Anjan Gudigar, U. Rajendra Acharya
      Glaucoma is the prime cause of blindness and early detection of it may prevent patients from vision loss. An expert system plays a vital role in glaucoma screening, which assist the ophthalmologists to make accurate decision. This paper proposes a novel technique for glaucoma detection using optic disk localization and non-parametric GIST descriptor. The method proposes a novel area based optic disk segmentation followed by the Radon transformation (RT). The change in the illumination levels of Radon transformed image are compensated using modified census transformation (MCT). The MCT images are then subjected to GIST descriptor to extract the spatial envelope energy spectrum. The obtained dimension of the GIST descriptor is reduced using locality sensitive discriminant analysis (LSDA) followed by various feature selection and ranking schemes. The ranked features are used to build an efficient classifier to detect glaucoma. Our system yielded a maximum accuracy (97.00%), sensitivity (97.80%) and specificity (95.80%) using support vector machine (SVM) classifier with nineteen features. Developed expert system also achieved maximum accuracy (93.62%), sensitivity (87.50%) and specificity (98.43%) for public dataset using twenty six features. The proposed method is efficient and computationally less expensive as it require only nineteen features to model a classifier for the huge dataset. Therefore the proposed method can be effectively utilized in hospitals for glaucoma screening.

      PubDate: 2017-12-17T13:52:18Z
      DOI: 10.1016/j.bbe.2017.11.002
       
  • Medical image registration in image guided surgery: Issues, challenges and
           research opportunities
    • Authors: Fakhre Alam; Sami Rahman Sehat Ullah Kamal Gulati
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 1
      Author(s): Fakhre Alam, Sami Ur Rahman, Sehat Ullah, Kamal Gulati
      Multimodal images of a patient obtained at different time, pre-surgical planning, intra-procedural guidance and visualization, and post-procedural assessment are the core components of image-guided surgery (IGS). In IGS, the goal of registration is to integrate corresponding information in different images of the same organ into a common coordinate system. Registration is a fundamental task in IGS and its main purpose is to provide better visualization and navigation to the surgeons. In this paper, we describe the most popular types of medical image registration and evaluate their prominent state-of-the art issues and challenges in image-guided surgery. We have also presented the factors which affect the accuracy, reliability and efficiency of medical image registration methods. It is not possible to achieve highly successful IGS until all the issues and challenges in registration process are identified and subsequently solved.

      PubDate: 2017-11-18T20:21:15Z
       
  • A generalized method for the segmentation of exudates from pathological
           retinal fundus images
    • Authors: Jaskirat Kaur; Deepti Mittal
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 1
      Author(s): Jaskirat Kaur, Deepti Mittal
      Diabetic retinopathy, an asymptomatic complication of diabetes, is one of the leading causes of blindness in the world. The exudates, abnormal leaked fatty deposits on retina, are one of the most prevalent and earliest clinical signs of diabetic retinopathy. In this paper, a generalized exudates segmentation method to assist ophthalmologists for timely treatment and effective planning in the diagnosis of diabetic retinopathy is developed. The main contribution of the proposed method is the reliable segmentation of exudates using dynamic decision thresholding irrespective of associated heterogeneity, bright and faint edges. The method is robust in the sense that it selects the threshold value dynamically irrespective of the large variations in retinal fundus images from varying databases. Since no performance comparison of state of the art methods is available on common database, therefore, to make a fair comparison of the proposed method, this work has been performed on a diversified database having 1307 retinal fundus images of varying characteristics namely: location, shapes, color and sizes. The database comprises of 649 clinically acquired retinal fundus images from eye hospital and 658 retinal images from publicly available databases such as STARE, MESSIDOR, DIARETDB1 and e-Optha EX. The segmentation results are validated by performing two sets of experiments namely: lesion based evaluation criteria and image based evaluation criteria. Experimental results at lesion level show that the proposed method outperforms other existing methods with a mean sensitivity/specificity/accuracy of 88.85/96.15/93.46 on a composite database of retinal fundus images. The segmentation results for image-based evaluation with a mean sensitivity/specificity/accuracy of 94.62/98.64/96.74 respectively prove the clinical effectiveness of the method. Furthermore, the significant collective performance of these experiments on clinically as well as publicly available standard databases proves the generalization ability and the strong candidature of the proposed method in the real-time diagnosis of diabetic retinopathy.

      PubDate: 2017-11-18T20:21:15Z
       
  • An automated ECG signal quality assessment method for unsupervised
           diagnostic systems
    • Authors: Udit Satija; Barathram Ramkumar Sabarimalai Manikandan
      Abstract: Publication date: 2018
      Source:Biocybernetics and Biomedical Engineering, Volume 38, Issue 1
      Author(s): Udit Satija, Barathram Ramkumar, M. Sabarimalai Manikandan
      In this paper, the authors present an automated method for quality assessment of electrocardiogram (ECG) signal. Our proposed method not only detects and classifies the ECG noises but also localizes the ECG noises which can play a crucial role in extracting reliable clinical features for ECG analysis systems. The proposed method is based on three stages: Wavelet decomposition of ECG signal into sub-bands; simultaneous ECG signal and noise reconstruction; extraction of temporal features such as maximum absolute amplitude, zerocrossings, kurtosis and autocorrelation function for detection, localization and classification of ECG noises including flat line (FL), time-varying noise or pause (TVN), baseline wander (BW), abrupt change (AB), power line interference (PLI), muscle artifacts (MA) and additive white Gaussian noise (AWGN). The proposed method is tested and validated against manually annotated ECG signals corrupted with aforementioned noises taken from MIT-BIH arrhythmia database, Physionet challenge database, and real-time recorded ECG signals. Comparative detection and classification results depict the superior performance of the proposed method over state of art methods. Detection results show that our method can achieve an average sensitivity (Se), average specificity (Sp) and accuracy (A) of 99.61%, 98.51%, 99.49% respectively. Also, the method achieves a Se of 98.18%, and Sp of 94.97% for real-time recorded ECG signals. The method has an average timing error of 0.14s in localizing the noise segments. Further, classification results demonstrate that the proposed method achieves an average sensitivity (Se), average positive predictivity (PP) and classification accuracy (A c ) of 98.53%, 98.89%, 97.50% respectively.

      PubDate: 2017-11-18T20:21:15Z
       
  • Contralateral asymmetry for breast cancer detection: A CADx approach
    • Authors: Jose Celaya-Padilla; Cesar Carlos Jorge Hamurabi Gamboa-Rosales Idalia Garza-Veloz Margarita
      Abstract: Publication date: Available online 10 November 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Jose M. Celaya-Padilla, Cesar H. Guzmán-Valdivia, Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, Idalia Garza-Veloz, Margarita L. Martinez-Fierro, Miguel A. Cid-Báez, Antonio Martinez-Torteya, Francisco J. Martinez-Ruiz, Huizilopoztli Luna-García, Arturo Moreno-Baez, Amita Nandal
      Early detection is fundamental for the effective treatment of breast cancer and the screening mammography is the most common tool used by the medical community to detect early breast cancer development. Screening mammograms include images of both breasts using two standard views, and the contralateral asymmetry per view is a key feature in detecting breast cancer. However, most automated detection algorithms do not take it into account. In this research, we propose a methodology to incorporate said asymmetry information into a computer-aided diagnosis system that can accurately discern between healthy subjects and subjects at risk of having breast cancer. Furthermore, we generate features that measure not only a view-wise asymmetry, but a subject-wise one. Briefly, the methodology co-registers the left and right mammograms, extracts image characteristics, fuses them into subject-wise features, and classifies subjects. In this study, 152 subjects from two independent databases, one with analog- and one with digital mammograms, were used to validate the methodology. Areas under the receiver operating characteristic curve of 0.738 and 0.767, and diagnostic odds ratios of 23.10 and 9.00 were achieved, respectively. In addition, the proposed method has the potential to rank subjects by their probability of having breast cancer, aiding in the re-scheduling of the radiologists’ image queue, an issue of utmost importance in developing countries.

      PubDate: 2017-11-18T20:21:15Z
       
  • Automated and effective content-based mammogram retrieval using wavelet
           based CS-LBP feature and self-organizing map
    • Authors: Vibhav Prakash Singh; Rajeev Srivastava
      Abstract: Publication date: Available online 11 October 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Vibhav Prakash Singh, Rajeev Srivastava
      Content-based mammogram retrieval is a crucial problem, because it supports radiologists in their decision to find similar mammograms out of a database to compare the current case with past cases. Labels, scratches, and pectoral muscles present in mammograms can bias the retrieval procedures. For the removal of these, generally manual cropping is performed which is very labour intensive and time consuming process. In this paper, automated, fast and effective content based-mammogram image retrieval system is proposed. The proposed pre-processing steps include automatic labelling-scratches suppression, automatic pectoral muscle removal and image enhancement. Further, for segmentation selective thresholds based seeded region growing algorithm is introduced. Furthermore, we apply 2-level discrete wavelet transform (DWT) on the segmented region and wavelet based centre symmetric-local binary pattern (WCS-LBP) features are extracted. Then, extracted features are fed to self-organizing map (SOM) which generates clusters of images, having similar visual content. SOM produces different clusters with their centres and query image features are matched with all cluster representatives to find closest cluster. Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure. So, at the searching time the query image is searched only in small subset depending upon cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Descriptive experimental and empirical discussions confirm the effectiveness of this paper.

      PubDate: 2017-10-14T03:18:21Z
      DOI: 10.1016/j.bbe.2017.09.003
       
  • A hybrid intelligent system for the prediction of Parkinson's Disease
           progression using machine learning techniques
    • Authors: Mehrbakhsh Nilashi; Othman Ibrahim; Hossein Ahmadi; Leila Shahmoradi; Mohammadreza Farahmand
      Abstract: Publication date: Available online 2 October 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Mehrbakhsh Nilashi, Othman Ibrahim, Hossein Ahmadi, Leila Shahmoradi, Mohammadreza Farahmand
      Parkinson's Disease (PD) is a progressive degenerative disease of the nervous system that affects movement control. Unified Parkinson's Disease Rating Scale (UPDRS) is the baseline assessment for PD. UPDRS is the most widely used standardized scale to assess parkinsonism. Discovering the relationship between speech signal properties and UPDRS scores is an important task in PD diagnosis. Supervised machine learning techniques have been extensively used in predicting PD through a set of datasets. However, the most methods developed by supervised methods do not support the incremental updates of data. In addition, the standard supervised techniques cannot be used in an incremental situation for disease prediction and therefore they require to recompute all the training data to build the prediction models. In this paper, we take the advantages of an incremental machine learning technique, Incremental support vector machine, to develop a new method for UPDRS prediction. We use Incremental support vector machine to predict Total-UPDRS and Motor-UPDRS. We also use Non-linear iterative partial least squares for data dimensionality reduction and self-organizing map for clustering task. To evaluate the method, we conduct several experiments with a PD dataset and present the results in comparison with the methods developed in the previous research. The prediction accuracies of method measured by MAE for the Total-UPDRS and Motor-UPDRS were obtained respectively MAE=0.4656 and MAE=0.4967. The results of experimental analysis demonstrated that the proposed method is effective in predicting UPDRS. The method has potential to be implemented as an intelligent system for PD prediction in healthcare.

      PubDate: 2017-10-10T02:10:31Z
      DOI: 10.1016/j.bbe.2017.09.002
       
  • A retinal image authentication framework based on a graph-based
           representation algorithm in a two-stage matching structure
    • Authors: Mahrokh Khakzar; Hossein Pourghassem
      Abstract: Publication date: Available online 20 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Mahrokh Khakzar, Hossein Pourghassem
      Retinal vascular pattern has many valuable characteristics such as uniqueness, stability and permanence as a basis for human authentication in security applications. This paper presents an automatic rotation-invariant retinal authentication framework based on a novel graph-based retinal representation scheme. In the proposed framework, to replace the retinal image with a relational mathematical graph (RMG), we propose a novel RMG definition algorithm from the corresponding blood vessel pattern of the retinal image. Then, the unique features of RMG are extracted to supplement the authentication process in our framework. The authentication process is carried out in a two-stage matching structure. In the first stage of this scenario, the defined RMG of enquiry image is authenticated with enrolled RMGs in the database based on isomorphism theory. If the defined RMG of enquiry image is not isomorphic with none enrolled RMG in the database, in the second stage of our matching structure, the authentication is performed based on the extracted features from the defined RMG by a similarity-based matching scheme. The proposed graph-based authentication framework is evaluated on VARIA database and accuracy rate of 97.14% with false accept ratio of zero and false reject ratio of 2.85% are obtained. The experimental results show that the proposed authentication framework provides the rotation invariant, multi resolution and optimized features with low computational complexity for the retina-based authentication application.

      PubDate: 2017-09-24T22:38:33Z
      DOI: 10.1016/j.bbe.2017.09.001
       
  • The effect of zinc oxide doping on mechanical and biological properties of
           3D printed calcium sulfate based scaffolds
    • Authors: Betül Aldemir Dikici; Serkan Dikici; Ozan Karaman; Hakan Oflaz
      Abstract: Publication date: Available online 18 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Betül Aldemir Dikici, Serkan Dikici, Ozan Karaman, Hakan Oflaz
      Fabrication of defect-matching scaffolds is the most critical step in bone tissue engineering. Three-dimensional (3D) printing is a promising technique for custom design scaffold fabrication due to the high controllability and design independency. The objective of this study is to investigate the effect of zinc oxide (ZnO) doping on mechanical and biological characteristics of 3D printed (3DP) calcium sulfate hemihydrate (CSHH) scaffolds. Crystalline phases, wettability, compressive strength and Young's modulus, human bone marrow derived mesenchymal stem cells (hMSCs) attachment, proliferation and morphology were investigated. XRD results showed that CSHH powder transformed into gypsum after the printing process due to the water content of binder. Contact angle measurements indicated that ZnO doped CSHH scaffolds have hydrophilic character, which stimulates cell attachment. The mechanical and cell culture studies demonstrated that increasing the ZnO doping concentration both mechanical strength and cell proliferation on CSHH scaffolds were enhanced.

      PubDate: 2017-09-24T22:38:33Z
      DOI: 10.1016/j.bbe.2017.08.007
       
  • Electroencephalography (EEG) signal processing for epilepsy and autism
           spectrum disorder diagnosis
    • Authors: Sutrisno Ibrahim; Ridha Djemal; Abdullah Alsuwailem
      Abstract: Publication date: Available online 14 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Sutrisno Ibrahim, Ridha Djemal, Abdullah Alsuwailem
      Quantification of abnormality in brain signals may reveal brain conditions and pathologies. In this study, we investigate different electroencephalography (EEG) feature extraction and classification techniques to assist in the diagnosis of both epilepsy and autism spectrum disorder (ASD). First, the EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). Two nonlinear methods were studied, namely, Shannon entropy and largest Lyapunov exponent, which measure complexity and chaoticity in the EEG recording, in addition to the two conventional methods (namely, standard deviation and band power). We also study the use of a cross-correlation approach to measure synchronization between EEG channels, which may reveal abnormality in communication between brain regions. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design exploration techniques: the University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and our own EEG recordings (46 subjects). The combination of DWT, Shannon entropy, and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem. The proposed method obtained better classification accuracy compared to the existing methods and tested using larger and more comprehensive EEG dataset. The proposed method could potentially be used to assist epilepsy and ASD diagnosis therefore improving the speed and the accuracy.

      PubDate: 2017-09-18T21:06:35Z
      DOI: 10.1016/j.bbe.2017.08.006
       
  • Human gait pattern changes detection system: A multimodal vision-based and
           novelty detection learning approach
    • Authors: João Paulo; Alireza Asvadi; Paulo Peixoto; Paula Amorim
      Abstract: Publication date: Available online 14 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): João Paulo, Alireza Asvadi, Paulo Peixoto, Paula Amorim
      This paper proposes a novel gait rehabilitation analysis system, based on an innovative multimodal vision-based sensor setup, focused on detecting gait pattern changes over time. The proposed setup is based on inexpensive technologies, without compromising performance, and was designed to be deployed on walkers, which are a typical assistive aid used in gait rehabilitation. In the medical field the evaluation of a patient's rehabilitation progress is typically performed by a medical professional through subjective techniques based on the professional's visual perception and experience. In this context, we are proposing an automatic system to detect the progress of patients undergoing rehabilitation therapy. Our approach is able to perform novelty detection for gait pattern classification based on One-Class Support Vector Machines (OC-SVM). Using point-cloud and RGB-D data, we detect the lower limbs (waist, legs and feet) by using Weighted Kernel-Density Estimation and Weighted Least-Squares to segment the legs into several parts (thighs and shins), and by fitting 3D ellipsoids to model them. Feet are detected using k-means clustering and a Circular Hough Transform. A temporal analysis of the feet's depth is also performed to detect heel strike events. Spatiotemporal and kinematic features are extracted from the lower limbs’ model and fed to a classifier based on the fusion of several OC-SVMs. Experiments with volunteers using the robotic walker platform ISR-AIWALKER, where the proposed system was deployed, revealed a lower limbs tracking accuracy of 3° and that our novelty detection approach successfully identified novel gait patterns, evidencing an overall 97.89% sensitivity.

      PubDate: 2017-09-18T21:06:35Z
      DOI: 10.1016/j.bbe.2017.08.002
       
  • Cardiac arrhythmia classification using the phase space sorted by Poincare
           sections
    • Authors: Reza Yaghoobi Karimui; Sassan Azadi
      Abstract: Publication date: Available online 8 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Reza Yaghoobi Karimui, Sassan Azadi
      Many methods for automatic heartbeat classification have been applied and reported in literature, but methods, which used the basin geometry of quasi-periodic oscillations of electrocardiogram (ECG) signal in the phase space for classifying cardiac arrhythmias, frequently extracted a limited amount of information of this geometry. Therefore, in this study, we proposed a novel technique based on Poincare section to quantify the basin of quasi-periodic oscillations, which can fill the mentioned gap to some extent. For this purpose, we first reconstructed the two-dimensional phase space of ECG signal. Then, we sorted this space using the Poincare sections in different angles. Finally, we evaluated the geometric features extracted from the sorted spaces of five heartbeat groups recommend by the association for the advancement of medical instrumentation (AAMI) by using the sequential forward selection (SFS) algorithm. The results of this algorithm indicated that a combination of nine features extracted from the sorted phase space along with per and post instantaneous heart rate could significantly separate the five heartbeat groups (99.23% and 96.07% for training and testing sets, respectively). Comparing these results with the results of earlier work also indicated that our proposed method had a figure of merit (FOM) about 32.12%. Therefore, this new technique not only can quantify the basin geometry of quasi-periodic oscillations of ECG signal in the phase space, but also its output can improve the performance of detection systems developed for the cardiac arrhythmias, especially in the five heartbeat groups recommend by the AAMI.

      PubDate: 2017-09-12T20:15:43Z
      DOI: 10.1016/j.bbe.2017.08.005
       
  • Application of MODWT and log-normal distribution model for automatic
           epilepsy identification
    • Authors: Mingyang Li; Wanzhong Chen; Tao Zhang
      Abstract: Publication date: Available online 5 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Mingyang Li, Wanzhong Chen, Tao Zhang
      In this paper, a novel approach based on the maximal overlap discrete wavelet transform (MODWT) and log-normal distribution (LND) model was proposed for identifying epileptic seizures from electroencephalogram (EEG) signals. To carry out this study, we explored the potentials of MODWT to decompose the signals into time-frequency sub-bands till sixth level. And demodulation analysis (DA) was investigated to reveal the subtle envelope information from the sub-bands. All obtained coefficients are then used to calculate LDN features, scale parameter (σ) and shape parameter (μ), which were fed to a random forest classifier (RFC) for classification. Besides, some experiments have been conducted to evaluate the performance of proposed model in the consideration of various wavelet functions as well as feature extractors. The implementation results demonstrated that our proposed technique has yielded remarkable classification performance for all the concerned problems that outperformed the reported methods in terms of the universality. The major finding of this research is that the proposed technique was capable of classifying EEG segments with satisfied accuracy and clinically acceptable computational time. These advantages have make our technique an attractive diagnostic and monitoring tool, which helps doctors in providing better and timely care for the patients.

      PubDate: 2017-09-06T18:17:47Z
      DOI: 10.1016/j.bbe.2017.08.003
       
  • Fractal analysis of the grey and binary images in diagnosis of Hashimoto's
           thyroiditis
    • Authors: Zbigniew Omiotek
      Abstract: Publication date: Available online 3 September 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Zbigniew Omiotek
      In the study, a fractal analysis of thyroid ultrasound images was applied. This method has not been too often used for testing such kind of images so far. Its advantage is a tool in a form of a fractal dimension, which easily quantifies a complexity of an image texture surface. There is a close relationship between the lesions and an ultrasound image texture in a case of a diffuse form of the Hashimoto's disease. As a result of the analysis, a set of nine fractal descriptors was obtained which made it possible to distinguish healthy cases from sick ones that suffer from the diffuse form of the Hashimoto's thyroiditis. The Hellwig's method for feature selection was utilised. It found the combinations of features of the highest value of the information capacity index. These combinations were applied to build and test five popular classifiers. The following methods were implemented: decision tree, random forests, K-nearest neighbours, linear and quadratic discriminant analysis. The best results were achieved with a combination of three descriptors – fractal dimension and intercept obtained by the power spectral density method and fractal dimension estimated by the box counting method. The LDA (linear discriminant analysis) classifier based on them was characterised by a sensitivity of 96.88%, a specificity at a level of 98.44%, and its overall classification accuracy was equal to 97.66%. These results are similar to the best results of other authors cited in the work where the greyscale image analysis was used.

      PubDate: 2017-09-06T18:17:47Z
      DOI: 10.1016/j.bbe.2017.08.004
       
  • Lumped models of the cardiovascular system of various complexity
    • Authors: Filip Ježek; Tomáš Kulhánek; Karel Kalecký; Jiri Kofránek
      Abstract: Publication date: Available online 30 August 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Filip Ježek, Tomáš Kulhánek, Karel Kalecký, Jiri Kofránek
      Purpose The main objective is to accelerate the mathematical modeling of complex systems and offer the researchers an accessible and standardized platform for model sharing and reusing. Methods We describe a methodology for creating mathematical lumped models, decomposing a system into basic components represented by elementary physical laws and relationships expressed as equations. Our approach is based on Modelica, an object-oriented, equation-based, visual, non-proprietary modeling language, together with Physiolibrary, an open-source library for the domain of physiology. Results We demonstrate this methodology on an open implementation of a range of simple to complex cardiovascular models, with great complexity variance (simulation time from several seconds to hours). The parts of different complexity could be combined together. Conclusions Thanks to the equation-based nature of Modelica, a hierarchy of subsystems can be built with an appropriate connecting component. Such a structural model follows the concept of the system rather than the computational order. Such a model representation retains structural knowledge, which is important for e.g., model maintainability and reusability of the components and multidisciplinary cooperation with domain experts not familiar with modeling methods.

      PubDate: 2017-08-31T16:56:41Z
      DOI: 10.1016/j.bbe.2017.08.001
       
  • Fully automatic ROI extraction and edge-based segmentation of radius and
           ulna bones from hand radiographs
    • Authors: Shreyas Simu; Shyam Lal; Pranav Nagarsekar; Amrish Naik
      Abstract: Publication date: Available online 12 August 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Shreyas Simu, Shyam Lal, Pranav Nagarsekar, Amrish Naik
      Bone age is a reliable measure of person's growth and maturation of skeleton. The difference between chronological age and bone age indicates presence of endocrinological problems. The automated bone age assessment system (ABAA) based on Tanner and Whitehouse method (TW3) requires monitoring the growth of radius, ulna and short bones (phalanges) of left hand. In this paper, a detailed analysis of two bones in the bone age assessment system namely, radius and ulna is presented. We propose an automatic extraction method for the region of interest (ROI) of radius and ulna bones from a left hand radiograph (RUROI). We also propose an improved edge-based segmentation technique for those bones. Quantitative and qualitative results of the proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation techniques. Medical experts have also validated the qualitative results of proposed segmentation technique. Experimental results reveal that these proposed techniques provide better segmentation accuracy as compared to the other state-of-the-art segmentation techniques.

      PubDate: 2017-08-21T14:40:18Z
      DOI: 10.1016/j.bbe.2017.07.004
       
  • Computer assisted classification framework for prediction of acute
           lymphoblastic and acute myeloblastic leukemia
    • Authors: Jyoti Rawat; Annapurna Singh; H.S. Bhadauria; Jitendra Virmani; Jagtar Singh Devgun
      Abstract: Publication date: Available online 25 July 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Jyoti Rawat, Annapurna Singh, H.S. Bhadauria, Jitendra Virmani, Jagtar Singh Devgun
      Hematological malignancies i.e. acute lymphoid leukemia and acute myeloid leukemia are the types of blood cancer that can affect blood, bone marrow, lymphatic system and are the major contributors to cancer deaths. In present work, an attempt has been made to design a CAC (computer aided classification system) for diagnosis of myeloid and lymphoid cells and their FAB (French, American, and British) characterization. The proposed technique improves the AML and ALL diagnostic accuracy by analyzing color, morphological and textural features from the blood image using image processing and to assist in the development of a computer-aided screening of AML and ALL. This paper endeavors at proposing a quantitative microscopic approach toward the discrimination of malignant from normal in stained blood smear. The proposed technique firstly segments the nucleus from the leukocyte cell background and then computes features for each segmented nucleus. A total of 331 geometrical, chromatic and texture features are computed. A genetic algorithm using support vector machine (SVM) classifier is used to optimize the feature space. Based on optimized feature space, an SVM classifier with various kernel functions is used to eradicate noisy objects like overlapped cells, stain fragments, and other kinds of background noises. The significance of the proposed method is tested using 331 features on 420 microscopic blood images acquired from the online repository provided by the American society of hematology. The results confirmed the viability or potential of using a computer aided classification method to reinstate the monotonous and the reader-dependent diagnostic methods.

      PubDate: 2017-07-31T11:49:05Z
      DOI: 10.1016/j.bbe.2017.07.003
       
  • Determining the shift of a bronchoscope catheter from the analysis of a
           video sequence of a bronchoscope video camera
    • Authors: Dariusz Michalski; Zbisław Tabor; Bartosz Zieliński
      Abstract: Publication date: Available online 24 July 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Dariusz Michalski, Zbisław Tabor, Bartosz Zieliński
      In this study we have proposed an algorithm for automated monitoring of the movements of a catheter used in peripheral bronchoscopy examination. We have shown that the shift of the catheter can be controlled in an automated way with quite a good accuracy by the means of analysis of video sequence recorded by a video camera of a bronchoscope. For a catheter moving between successive frames by no more than 1/3 of the distance between successive markers associated with a catheter the accuracy of a catheter shift measurement was equal to 1% and for a catheter moving between successive frames by no more than 1/2 of the distance between successive markers associated with a catheter the accuracy of a catheter shift measurement was equal to 5%. Visual inspection proved that the observed measurement errors were associated with faster movements of a catheter. Bronchoscope redesign option is proposed to improve catheter shift measurement accuracy. The results of this study demonstrate that application of image analysis techniques to data recorded during bronchoscopy examination can at least support the existing navigation methods for peripheral bronchoscopy with respect to the determination of the location of the catheter distal tip within the lumen of the pulmonary airways.

      PubDate: 2017-07-31T11:49:05Z
      DOI: 10.1016/j.bbe.2017.07.002
       
  • A rapid algebraic 3D volume image reconstruction technique for Cone Beam
           Computed Tomography
    • Authors: Mohammed A. Al-masni; Mugahed A. Al-antari; Mohamed K. Metwally; Yasser M. Kadah; Seung-Moo Han; Tae-Seong Kim
      Abstract: Publication date: Available online 11 July 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Mohammed A. Al-masni, Mugahed A. Al-antari, Mohamed K. Metwally, Yasser M. Kadah, Seung-Moo Han, Tae-Seong Kim
      Computed Tomography (CT) is a widely used imaging technique in medical diagnosis. Among the latest advances in CT imaging techniques, the use of cone-beam X-ray projections, instead of the usual planar fan beam, promises faster yet safer 3D imaging in comparison to the previous CT imaging methodologies. This technique is called Cone Beam CT (CBCT). However, these advantages come at the expense of a more challenging 3D reconstruction problem that is still an active research area to improve the speed and quality of image reconstruction. In this paper, we present an implementation of rapid parallel Multiplicative Algebraic Reconstruction Technique (rpMART) for CBCT which gives more accurate and faster reconstruction even with a lower number of projections via parallel computing. We have compared rpMART with the parallel version of Algebraic Reconstruction Technique (pART) and the conventional non-parallel versions of npART, npMART and Feldkamp, Davis, and Kress (npFDK) techniques. The results indicate that the reconstructed volume images from rpMART provide a higher image quality index of 0.99 than the indices of pART and npFDK of 0.80 and 0.39, respectively. Also the proposed implementation of rpMART and pART via parallel computing significantly reduce the reconstruction time from more than 6h with npART and npMART to 580 and 560s with the full 360° projections data, respectively. We consider that rpMART could be a better image reconstruction technique for CBCT in clinical applications instead of the widely used FDK method.

      PubDate: 2017-07-14T15:31:25Z
      DOI: 10.1016/j.bbe.2017.07.001
       
  • Physical activity recognition by smartphones, a survey
    • Authors: Jafet Morales; David Akopian
      Abstract: Publication date: Available online 4 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Jafet Morales, David Akopian
      Human activity recognition (HAR) from wearable motion sensor data is a promising research field due to its applications in healthcare, athletics, lifestyle monitoring, and computer–human interaction. Smartphones are an obvious platform for the deployment of HAR algorithms. This paper provides an overview of the state-of-the-art when it comes to the following aspects: relevant signals, data capture and preprocessing, ways to deal with unknown on-body locations and orientations, selecting the right features, activity models and classifiers, metrics for quantifying activity execution, and ways to evaluate usability of a HAR system. The survey covers detection of repetitive activities, postures, falls, and inactivity.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.004
       
 
 
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