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Journal Cover Biocybernetics and Biological Engineering
  [SJR: 0.279]   [H-I: 8]   [5 followers]  Follow
    
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   ISSN (Print) 0208-5216
   Published by Elsevier Homepage  [3118 journals]
  • 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
       
  • A description of hand matrices to extract various characteristics of human
           hand in three-dimensional space
    • Authors: Tsutomu Sekine; Shun Hibino; Yuya Nakamura
      Abstract: Publication date: Available online 20 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Tsutomu Sekine, Shun Hibino, Yuya Nakamura
      This study focuses on a description of hand matrices to extract various characteristics of human hand in three-dimensional space. A mathematical expression for human hand has scarcely been proposed so far, and the practical, versatile description has been required to analyze a gesture behavior in detail. In this study, the bones and joints of human hand were explained supplementarily. After that, a CG model of human hand was created according to the anatomical structure. With reference to the model's structure, hand matrices were proposed to investigate poses, positions, and postural orientations of human hand in a uniform manner. The several examples were also discussed with appropriate illustrations. As a result, the characteristics of hand matrices were revealed in practically-possible cases; moreover, the mathematical treatments were theoretically versatile and simple to find a difference or common feature of hand motion in three-dimensional space.

      PubDate: 2017-06-23T15:52:38Z
      DOI: 10.1016/j.bbe.2017.05.003
       
  • Improving the accuracy of detecting steroid abuse in cattle by pairwise
           learning of serum samples
    • Authors: Xiao-Lei Xia
      Abstract: Publication date: Available online 7 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Xiao-Lei Xia


      PubDate: 2017-06-08T16:31:01Z
      DOI: 10.1016/j.bbe.2017.05.009
       
  • Development of a real time emotion classifier based on evoked EEG
    • Authors: Moon Inder Singh; Mandeep Singh
      Abstract: Publication date: Available online 1 June 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Moon Inder Singh, Mandeep Singh
      Our quality of life is more dependent on our emotions than on physical comforts alone. This is motivation enough to classify emotions using Electroencephalogram (EEG) signals. This paper describes the acquisition of evoked EEG signals for classification of emotions into four quadrants. The EEG signals have been collected from 24 subjects on three electrodes (Fz, Cz and Pz) along the central line. The absolute and differential attributes of single trial ERPs have been used to classify emotions. The single trial ERP attributes collected from each electrode have been used for developing an emotion classifier for each subject. The accuracy of classification of emotions into four classes lies between 62.5–83.3% for single trials. The subject independent analysis has been done using absolute and differential attributes of single trial signals of ERP. An overall accuracy of 55% has been obtained on Fz electrode for multi subject trials. The methodology used to classify emotions by fixing the attributes for classification of emotions brings us a step closer to developing a real time emotion recognition system with benefits including applications like Brain-Computer Interface for locked-in subjects, emotion classification for highly sensitive jobs like fighter pilots etc.

      PubDate: 2017-06-04T15:49:15Z
      DOI: 10.1016/j.bbe.2017.05.004
       
  • Robust and accurate optic disk localization using vessel symmetry line
           measure in fundus images
    • Authors: Rashmi Panda; N.B. Puhan; Ganapati Panda
      Abstract: Publication date: Available online 31 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Rashmi Panda, N.B. Puhan, Ganapati Panda
      Accurate optic disk (OD) localization is an important step in fundus image based computer-aided diagnosis of glaucoma and diabetic retinopathy. Robust OD localization becomes more challenging with the presence of common pathological variations which could alter its overall appearance. This paper presents a novel OD localization method by incorporating salient visual cues of retinal vasculature: (1) global vessel symmetry, (2) vessel component count and (3) local vessel symmetry inside OD region. In the proposed method, a new vessel symmetry line (VSL) measure is designed to demarcate the lines that divide the retinal vasculature into approximately similar halves. The initial OD center location is computed using the highest number of major blood vessel components in the skeleton image. The final OD center involves an iterative center of mass computation to exploit the local vessel symmetry in the OD region of interest. The proposed method shows effectiveness in diseased retinas having diverse symptoms like bright lesions, hemorrhages, and tortuous vessels that create potential ambiguity for OD localization. A total of ten publicly available retinal image databases are considered for extensive evaluation of the proposed method. The experimental results demonstrate high average OD detection accuracy of 99.49%, while achieving state-of-the-art OD localization error in all databases.

      PubDate: 2017-06-04T15:49:15Z
      DOI: 10.1016/j.bbe.2017.05.008
       
  • Blood flows in end-to-end arteriovenous fistulas: Unsteady and steady
           state numerical investigations of three patient-specific cases
    • Authors: Daniel Jodko; Damian Obidowski; Piotr Reorowicz; Krzysztof Jóźwik
      Abstract: Publication date: Available online 30 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Daniel Jodko, Damian Obidowski, Piotr Reorowicz, Krzysztof Jóźwik


      PubDate: 2017-06-04T15:49:15Z
      DOI: 10.1016/j.bbe.2017.05.006
       
  • A multi-layered incremental feature selection algorithm for adjuvant
           chemotherapy effectiveness/futileness assessment in non-small cell lung
           cancer
    • Authors: Roghayeh Esmaeili Naftchali; Mohammad Saniee Abadeh
      Abstract: Publication date: Available online 24 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Roghayeh Esmaeili Naftchali, Mohammad Saniee Abadeh
      Non-small cell lung cancer (NSCLC) is the most common type of lung cancer; and is one of the leading causes of death in the world. Surgery combined with chemotherapy is the recommended treatment for NSCLC. Since chemotherapy is an expensive treatment for either medical staff or patients suffering from pain, this study attempts to construct an intelligent predictive model to predict the adjuvant chemotherapy (ACT) effectiveness/futileness in the patients, in order to help futile cases for unnecessary applications. There is a 2-step method: preprocessing and predicting. First a purposefully preprocessing technique: chi-square test, SVM-RFE and correlation matrix, were employed in NSCLC gene expression dataset as a novel multi-layered feature selection method to defeat the curse of dimension and detect the chemotherapy target genes from tens of thousands features, based on which the patients can be classified into two groups, with NB classifier at second step. 10-Fold cross-validation was found with accuracy of 68.93% for 2 genes, TGFA (205015_s_at) and SEMA6C (208100_x_at), which is preferable compared to earlier studies, even though more than 2 input features are employed for the prediction. According to the results found in this study, one can concludes that the multi-layered feature selection approach has increased the classification accuracy in terms of finding the fitted patient for receiving ACT by reducing the number of features and has significant power to be used in medical datasets with small train samples and large number of features.

      PubDate: 2017-05-24T14:48:45Z
      DOI: 10.1016/j.bbe.2017.05.002
       
  • ECG signals reconstruction in subbands for noise suppression
    • Authors: Marian Kotas; Tomasz Moroń
      Abstract: Publication date: Available online 18 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marian Kotas, Tomasz Moroń
      In this study, we propose a combination of two methods for ECG noise suppression. The first one is the robust principal component analysis, applied to QRS complexes reconstruction. The second is the method of weighted averaging of nonlinearly aligned signal cycles. The novelty of the approach consists in the way these methods are combined. First, a processed ECG signal is decomposed into three spectral subbands, of high, medium and low frequency. Then both methods are applied in such a way that their operation is prevented from the most common unfavorable factors. RPCA reconstructs QRS complexes in a medium and high frequency subbands added. This makes the method more immune to low frequency artifacts that can be caused by electrodes motion. Dynamic time-warping is performed on the medium frequency subband which again prevents the procedure from the unfavorable influence of electrode motion artifacts. After the warping paths have been determined, the weighted addition of nonlinearly aligned signal cycles is executed, separately in the three subbands, with optimal weights estimated in each subband. Finally, by the appropriate addition of the obtained signals, the whole spectrum ECG is reconstructed. In the experimental section, the method was investigated with the use of real and artificially generated signals. In both cases, it allowed for effective suppression of noise, preserving important features of the processed signals. When it was applied to ECG enhancement prior to determination of the QT interval, the measurements appeared to be remarkably immune to different types of noise.

      PubDate: 2017-05-19T14:16:29Z
      DOI: 10.1016/j.bbe.2017.03.002
       
  • A new computer-based approach for fully automated segmentation of knee
           meniscus from magnetic resonance images
    • Authors: Ahmet Saygili; Songül Albayrak
      Abstract: Publication date: Available online 18 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Ahmet Saygili, Songül Albayrak
      Menisci are tissues that enable mobility and absorb excess loads on the knee. Problems in meniscus can trigger the disorder of osteoarthritis (OA). OA is one of the most common causes of disability, especially among young athlethes and elderly people. Therefore, the early diagnosis and treatment of abnormalities that occur in the meniscus are of significant importance. This study proposes a new computer-based and fully automated approach to support radiologists by: (i) the segmentation of medial menisci, (ii) enabling early diagnosis and treatment, and (iii) reducing the errors caused by MR intra-reader variability. In this study, 88 different MR images provided by the Osteoarthritis Initiative (OAI) are used. The histogram of oriented gradients (HOG) and local binary patterns (LBP) methods are used for feature extraction from these MR images along with the extreme learning machine (ELM) and random forests (RF) methods which are used for model learning (regression). As the first step of the pipeline, the most compact rectangular patches bounding the menisci are located. After this, meniscus boundaries are revealed by the morphological processes. Then, the similarities between these boundaries and the ground truth images are measured and compared with each other. The highest score is acquired with Dice similarity measurement with a success rate of 82%. A successful segmentation is performed on the diseased knee MR images. The proposed approach can be implemented as a decision support system for radiologists, while the segmented menisci can be used in classification of meniscal tear in future studies.

      PubDate: 2017-05-19T14:16:29Z
      DOI: 10.1016/j.bbe.2017.04.008
       
  • The autonomic nervous system and cancer
    • Authors: Milan T. Makale; Santosh Kesari; Wolfgang Wrasidlo
      Abstract: Publication date: Available online 17 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Milan T. Makale, Santosh Kesari, Wolfgang Wrasidlo
      Recent data have demonstrated extensive autonomic nervous system (ANS) neural participation in malignant tumors and infiltration of nerve fibers in and around malignant tumors. ANS cybernetic imbalances deriving from central nervous system (CNS) stress are associated with poorer patient outcome and may play a key role in tumor expansion. The ANS modulates and can destabilize tissue stem cells, and it drives the expression of neurotransmitter receptors on tumor cells. Disruption of tumor innervation and pharmacological ANS blockade have abrogated cancer growth in preclinical models. The present review interprets recent key findings with respect to the ANS and cancer. We highlight new data from animal models addressing specific cancers suggesting that unbalanced autonomic cybernetic control loops are associated with tissue instability which in turn promotes, (1) cancer stem cell based tumor initiation and growth, and (2) metastasis. We posit that identifying the sources of neural control loop dysregulation in specific tumors may reveal potential targets for antitumor therapy. Given the striking tumor regression results obtained with gastric vagotomy in gastric cancer models, and the effects of β-adrenergic blockade in pancreatic tumor models, it may be feasible to improve cancer outcomes with therapeutics targeted to the nervous system.

      PubDate: 2017-05-19T14:16:29Z
      DOI: 10.1016/j.bbe.2017.05.001
       
  • In-silico evaluation of left ventricular unloading under varying speed
           continuous flow left ventricular assist device support
    • Authors: Selim Bozkurt; Surhan Bozkurt
      Abstract: Publication date: Available online 4 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Selim Bozkurt, Surhan Bozkurt
      Continuous flow left ventricular assist device (cf-LVAD) operating speed modulation techniques are proposed to achieve different purposes such as improving arterial pulsatility, aortic valve function or ventricular unloading etc. Although it is possible to improve the left ventricular unloading by modulating the operating speed of a cf-LVAD, it is still unclear what type of pump operating mode should be applied to generate a better left ventricular unloading. This study presents a comparison of different heart pump support modes including constant speed support, copulsative and counterpulsative direct cf-LVAD speed modulation and pump flow rate control to regulate the cf-LVAD operating speed. The simulations were performed using a cardiovascular system model, which consists of active left atrium and ventricle, mitral and aortic valve leaflets, circulatory loop and a cf-LVAD. The cf-LVAD was operated between 7500rpm and 12,500rpm with 1000rpm intervals to simulate constant speed support. The same mean pump operating speeds over a cardiac cycle were applied in the direct operating speed modulation for the copulsative and counterpulsative direct speed modulation cf-LVAD support as in the constant speed support while the same pump-output over a cardiac cycle was applied to drive the pump in flow rate controlled copulsative and counterpulsative cf-LVAD support modes as in the constant speed support. Simulation results show that flow rate controlled counterpulsative pump support mode generates lower end-diastolic left ventricular volume and pressure–volume area while generating more physiological left ventricular volume signals over a cardiac cycle with respect to the other pump operating modes.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.03.003
       
  • Artifacts removal from EEG signal: FLM optimization-based learning
           algorithm for neural network-enhanced adaptive filtering
    • Authors: M.H. Quazi; S.G. Kahalekar
      Abstract: Publication date: Available online 4 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): M.H. Quazi, S.G. Kahalekar
      Electroencephalogram (EEG) denotes a neurophysiologic measurement, which observes the electrical activity of the brain through making a record of the EEG signal from the electrodes positioned on the scalp. The EEG signal gets mixed with other biological signals, called artifacts. Few artifacts include electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). Removal of artifacts from the EEG signal poses a great challenge in the medical field. Hence, the FLM (Firefly+Levenberg Marquardt) optimization-based learning algorithm for neural network-enhanced adaptive filtering model is introduced to eliminate the artifacts from the EEG. Initially, the EEG signal was provided to the adaptive filter for yielding the optimal weights using the renowned optimization algorithms, called firefly algorithm and LM. These two algorithms are effectively hybridized and applied to the neural network to find the optimal weights for adaptive filtering. Then, the designed filtering process renders an improved system for artifacts removal from the EEG signal. Finally, the performance of the proposed model and the existing models regarding SNR, computation time, MSE and RMSE are analyzed. The results conclude that the proposed method achieves a high SNR of 42.042dB.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.003
       
  • 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
       
  • Ensemble of classifiers and wavelet transformation for improved
           recognition of Fuhrman grading in clear-cell renal carcinoma
    • Authors: Michal Kruk; Jaroslaw Kurek; Stanislaw Osowski; Robert Koktysz; Bartosz Swiderski; Tomasz Markiewicz
      Abstract: Publication date: Available online 1 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Michal Kruk, Jaroslaw Kurek, Stanislaw Osowski, Robert Koktysz, Bartosz Swiderski, Tomasz Markiewicz
      The paper presents an improved system to recognition of Fuhrman grading in clear-cell renal carcinoma using an ensemble of classifiers. The novelty of solution includes the segmentation applying wavelet transformation in preprocessing stage, application of few selection methods for feature generation and using the ensemble of classifiers in final recognition step. The wavelet transformation is a very efficient tool for image de-noising and enhancing the edges of cell nuclei. The important distinction to other approaches is that diagnostic features of nuclei, based on the texture, geometry, color and histogram, are selected by using few methods, each relying on different mechanism of selection. These different sets of features have enabled creating the ensemble of classifiers based on the support vector machine and random forest, both cooperating with them. Such approach has led to the significant increase of the quality factors in comparison to the best existing results: sensitivity (the average of this solution 94.3% compared to 91.5%) and specificity (the average 98.6% compared to 97.5%.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.005
       
  • Multi-objective binary DE algorithm for optimizing the performance of
           Devanagari script-based P300 speller
    • Authors: Rahul Kumar Chaurasiya; Narendra D. Londhe; Subhojit Ghosh
      Abstract: Publication date: Available online 1 May 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Rahul Kumar Chaurasiya, Narendra D. Londhe, Subhojit Ghosh
      P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliability in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.

      PubDate: 2017-05-05T07:57:46Z
      DOI: 10.1016/j.bbe.2017.04.006
       
  • Magnetic navigation and tracking of multiple ferromagnetic microrobots
           inside an arterial phantom setup for MRI guided drug therapy
    • Authors: Nitesh Kumar; Vivek Verma; Laxmidhar Behera
      Abstract: Publication date: Available online 21 April 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Nitesh Kumar, Vivek Verma, Laxmidhar Behera
      Magnetic steering of ferromagnetic microrobots facilitates active drug targeting and minimally invasive treatment of deep seated tumour cells. Several techniques for magnetic steering of nanostructured single microrobot in human vasculature exist but literatures on multirobot navigation are scarce. In the current work, preliminary experimental validation of a novel magnetic navigation model for multiple ferromagnetic microrobots is performed inside a bifurcated arterial phantom apparatus. The proposed model includes the formation of a single linear assembly of ferromagnetic microrobots inside the arterial setup placed under a magnetic field. This field is intended to mimic the magnetic field generated by a Magnetic Resonance Imaging (MRI) device which finds application in targeted drug therapy. The linear assembly process was studied with the help of Newtonian dynamics simulation including dipole–dipole and near field forces. As, the assembly was found to be structurally intact in a pulsatile flow, its simulated trajectory was controlled by manipulating a single microrobot present in the middle of the assembly. The trajectory convergence algorithm used for this purpose includes a fuzzy logic based nonlinear “Proportional-Integral-Derivative” (PID) control scheme with magnetic field gradient as its control input. Since most of the modern MRI devices are based on PID controller for manipulation of magnetic gradients, the proposed fuzzy PID based control scheme can easily be interfaced with these devices for the intended application.

      PubDate: 2017-04-28T07:06:38Z
      DOI: 10.1016/j.bbe.2017.04.002
       
  • Fast, accurate and robust retinal vessel segmentation system
    • Authors: Zhexin Jiang; Juan Yepez; Sen An; Seokbum Ko
      Abstract: Publication date: Available online 19 April 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Zhexin Jiang, Juan Yepez, Sen An, Seokbum Ko
      The accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic ophthalmological and cardiovascular diagnosis systems. Aside from accuracy, robustness and processing speed are also considered crucial for medical purposes. In order to meet those requirements, this work presents a novel approach to extract blood vessels from the retinal fundus, by using morphology-based global thresholding to draw the retinal venule structure and centerline detection method for capillaries. The proposed system is tested on DRIVE and STARE databases and has an average accuracy of 95.88% for single-database test and 95.27% for the cross-database test. Meanwhile, the system is designed to minimize the computing complexity and processes multiple independent procedures in parallel, thus having an execution time of 1.677s per image on CPU platform.

      PubDate: 2017-04-21T05:06:23Z
      DOI: 10.1016/j.bbe.2017.04.001
       
  • Full-automatic computer aided system for stem cell clustering using
           Content-based Microscopic Image Analysis
    • Authors: Chen Li; Xinyu Huang; Tao Jiang; Ning Xu
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Chen Li, Xinyu Huang, Tao Jiang, Ning Xu
      Stem cells are very original cells that can differentiate into other cells, tissues and organs, which play a very important role in biomedical treatments. Because of the importance of stem cells, in this paper we propose a full-automatic computer aided clustering system to assist scientists to explore potential co-occurrence relations between the cell differentiation and their morphological information in phenotype. In this proposed system, a multi-stage Content-based Microscopic Image Analysis (CBMIA) framework is applied, including image segmentation, feature extraction, feature selection, feature fusion and clustering techniques. First, an Improved Supervised Normalized Cuts (ISNC) segmentation algorithm is newly introduced to partition multiple stem cells into individual regions in an original microscopic image, which is the most important contribution in this paper. Then, based on the segmented stem cells, 11 different feature extraction approaches are applied to represent the morphological characteristics of them. Thirdly, by analysing the robustness and stability of the extracted features, Hu and Zernike moments are selected. Fourthly, these two selected features are combined by an early fusion approach to further enhance the properties of the feature representation of stem cells. Finally, k-means clustering algorithm is chosen to classify stem cells into different categories using the fused feature. Furthermore, in order to prove the effectiveness and usefulness of this proposed system, we carry out a series of experiments to evaluate our methods. Especially, our ISNC segmentation obtains 92.4% similarity, 96.0% specificity and 107.8% ration of accuracy, showing the potential of our work.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.01.004
       
  • Non-uniform viscosity caused by red blood cell aggregation may affect NO
           concentration in the microvasculature
    • Authors: Huiting Qiao; Hongjun Zhao; Dov Jaron
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Huiting Qiao, Hongjun Zhao, Dov Jaron
      Aggregation of red blood cells in the micro vasculature may affect blood viscosity in the vessel. The purpose of this study was to investigate the potential effect of non-uniform viscosity caused by red blood cell (RBC) aggregation on nitric oxide (NO) concentration and distribution. A 3-D multi-physics model was established to simulate the production, transport and consumption of NO. Two non-uniform viscosity models caused by RBC aggregation were investigated: one assuming a linear and the other a step hematocrit distribution. In addition, the effect of the thickness of the plasma layer was tested. Simulation results demonstrate that non-uniform viscosity caused by RBCs aggregation influences NO concentration distribution. Compared with the uniform viscosity model, NO concentration using non-uniform viscosity is lower than that using uniform viscosity. Moreover, NO concentration calculated from the step hematocrit model is higher than that calculated from the linear hematocrit model. NO concentrations in the endothelium and the vascular wall decrease with the decline of the thickness of the plasma layer. The relative decrease differs between the linear and the step model. Our results suggest that non-uniform viscosity caused by red blood cell aggregation affects nitric oxide distribution in the micro vasculature. If uniform viscosity is assumed when performing numerical simulations, NO concentration values may be overestimated.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2016.10.004
       
  • An objective method to identify optimum clip-limit and histogram
           specification of contrast limited adaptive histogram equalization for MR
           images
    • Authors: Justin Joseph; Sivaraman Jayaraman; R. Periyasamy; V.R. Simi
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Justin Joseph, Sivaraman Jayaraman, R. Periyasamy, V.R. Simi
      In contrast limited adaptive histogram equalization (CLAHE), the selection of tile size, clip-limit and the distribution which specify desired shape of the histogram of image tiles is paramount, as it critically influences the quality of the enhanced image. The optimal value of these parameters devolves on the generic of the image to be enhanced and usually they are selected empirically. In this paper, the degradation of intensity, textural and geometric features of the medical image with respect to the variation in clip-limit and specified histogram shape is analyzed. The statistical indices used to quantify the feature degradation are Absolute Mean Brightness Error (AMBE), Absolute Deviation in Entropy (ADE), Peak Signal to Noise Ratio (PSNR), Variance Ratio (VR), Structural Similarity Index Matrix (SSIM) and Saturation Evaluation Index (SEI). The images used for the analysis are axial plane MR images of magnetic resonance spectroscopy (MRS), under gradient recalled echo (GRE), diffusion weighted imaging (DWI) 1000b Array Spatial Sensitivity Encoding Technique (ASSET), T2 Fluid Attenuation Inversion Recovery (FLAIR) and T1 Fast Spin-Echo Contrast Enhanced (FS-ECE) series of pre-operative Glioblastoma-edema complex. The experimental analysis was performed using Matlab®. Results show that for MR images the exponential histogram specification with a clip-limit of 0.01 is found to be optimum. At optimum clip-limit, the mean of SSIM exhibited by the Rayleigh, uniform and exponential histogram specification were found to be 0.7477, 0.7946 and 0.8457, for ten sets of MR images and mean of variance ratio are 1.242, 2.0316 and 1.7711, respectively.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.11.006
       
  • Spatial and spatio-temporal filtering based on common spatial patterns and
           Max-SNR for detection of P300 component
    • Authors: Fereshteh Salimian Rizi; Vahid Abootalebi; Mohamad Taghi Sadeghi
      Abstract: Publication date: Available online 18 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Fereshteh Salimian Rizi, Vahid Abootalebi, Mohamad Taghi Sadeghi
      Recent advances in brain-computer interfaces (BCIs) have developed a new arena for designing systems to help disabled persons to communicate with the surrounding environment. P300 speller is one of the most famous BCI systems choosing the characters from a virtual keyboard through the analysis of EEG signals. P300 detection is an important processing step of these systems. The accuracy of P300 detection highly depends on the feature extraction method. In this study, the maximum signal to noise ratio (Max-SNR) has been used for feature extraction, which rarely applied in this area. This study presents a novel feature extraction technique, named spatio-temporal Max-SNR (ST.Max-SNR). Unlike the standard Max-SNR which only uses spatial patterns of a signal, the proposed method, separately consider the spatial and temporal patterns of the signal to enhance the accuracy of feature extraction. Due to the similarity of the common spatial pattern (CSP) and the Max-SNR algorithms, the performance of this technique and its extension, common Spatio-temporal pattern (CSTP), has been compared with the proposed method. Then, the LDA and SWLDA classifiers are used for classification of the features. Our experimental results show that the Max-SNR based spatio-temporal features lead to an average classification accuracy of 94.4 percent suggesting the best performance.

      PubDate: 2017-01-18T18:16:37Z
      DOI: 10.1016/j.bbe.2016.11.001
       
 
 
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