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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Meredith J. Cler Carolyn M. Michener Cara E. Stepp Graduate Program for Neuroscience-Computational Neuroscience Boston University Department of Speech Language and Hearing Sciences Boston University Departments of Speech Language and Hearing Sciences and Biomedical Engineering Boston University

ISBN: (纸本)9781424479276

Over 50% of the 273,000 individuals with spinal cord injuries in the US have cervical injuries and are therefore unable to operate a keyboard and mouse with their hands. In this experiment, we compared two systems using surface electromyography (sEMG) recorded from facial muscles to control an onscreen keyboard. Both systems used five sEMG sensors to capture muscle activity during five distinct facial gestures that then mapped to five cursor commands: move left, move right, move up, move down, and click. One system used a discrete movement and feedback algorithm, in which the user would make one quick facial gesture, causing a corresponding discrete movement to an adjacent button. The other system was continuously updated and allowed the user to move in any 360° direction smoothly. Information transfer rates (ITRs) in bits per minute were high for both systems. Users of the continuous system showed significantly higher ITRs (average of 68.5;p < 0.02) compared to users of the discrete system (average of 54.3 bits/min).

关键词： Electrodes Muscles Calibration Spinal cord injury Keyboards Speech Electromyography

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Surface electromyographic control of speech synthesis

Surface electromyographic control of speech synthesis

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Meredith J. Cler Alfonso Nieto-Castanon Frank H. Guenther Cara E. Stepp Graduate Program for Neuroscience-Computational Neuroscience Boston University Department of Speech Language and Hearing Sciences Boston University Departments of Speech Language and Hearing Sciences and Biomedical Engineering Boston University

ISBN: (纸本)9781424479276

Individuals with very high spinal cord injuries (e.g. C1-C3) may be ventilator-dependent and therefore unable to support speech breathing. However, their facial musculature is intact, given that these muscles are innervated by cranial nerves. We developed a system using surface electromyography (sEMG) recorded from facial muscles to control a phonemic interface and voice synthesizer and tested the system in healthy individuals. Users were able to use five facial gestures to control an onscreen cursor and the phonemic interface. Users had mean information transfer rates (ITRs) of 59.5 bits/min when calculating ITRs using the number of phonemes selected. To compare with orthographic systems, ITRs were also calculated using the equivalent number of letters required to spell the selected word. With this calculation, users had a mean ITR of 70.1. Results are promising for further development and testing in individuals with high spinal cord injuries.

关键词： Muscles Speech Electrodes Mice Spinal cord injury Educational institutions Training

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Classification of serous ovarian tumors based on microarray data using multicategory support vector machines 36

Classification of serous ovarian tumors based on microarray ...

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2014 36th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2014

作者： Park, Jee Soo Beom Choi, Soo Chung, Jai Won Kim, Sung Woo Kim, Deok Won Dept. of Medicine Yonsei University College of Medicine Seoul Korea Republic of Brain Korea 21 PLUS Project for Medical Science Yonsei University Seoul Korea Republic of Graduate Program in Biomedical Engineering Yonsei University Seoul Korea Republic of Future IT RandD Laboratory LG Electronics Advanced Research Institute Seoul Korea Republic of Dept. of Medical Engineering Yonsei University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9781424479290

Ovarian cancer, the most fatal of reproductive cancers, is the fifth leading cause of death in women in the United States. Serous borderline ovarian tumors (SBOTs) are considered to be earlier or less malignant forms of serous ovarian carcinomas (SOCs). SBOTs are asymptomatic and progression to advanced stages is common. Using DNA microarray technology, we designed multicategory classification models to discriminate ovarian cancer subclasses. To develop multicategory classification models with optimal parameters and features, we systematically evaluated three machine learning algorithms and three feature selection methods using five-fold cross validation and a grid search. The study included 22 subjects with normal ovarian surface epithelial cells, 12 with SBOTs, and 79 with SOCs according to microarray data with 54,675 probe sets obtained from the National Center for Biotechnology Information gene expression omnibus repository. Application of the optimal model of support vector machines one-versus-rest with signal-to-noise as a feature selection method gave an accuracy of 97.3%, relative classifier information of 0.916, and a kappa index of 0.941. In addition, 5 features, including the expression of putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and SOC groups. An accurate diagnosis of ovarian tumor subclasses by application of multicategory machine learning would be cost-effective and simple to perform, and would ensure more effective subclass-targeted therapy. © 2014 IEEE.

关键词： Ovarian Neoplasms SYNTHETIC ORGANIC CHEMICALS Machine learning algorithms Support Vector Network Ovarian Serous Tumor selection methods AOX1 gene Microarray DNA microarray technology Subclasses optimal parameter Feature extraction vector computer Ovary Cancer

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A parallel Remote Center of Motion mechanism for needle-based medical interventions

A parallel Remote Center of Motion mechanism for needle-base...

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IEEE/RAS-EMBS International Conference on biomedical Robotics and Biomechatronics (BioRob)

作者： Mostafa Hadavand Michael D. Naish Rajni V. Patel Biomedical Engineering Graduate Program Western University Canadian Surgical Technologies and Advanced Robotics (CSTAR) Lawson Health Research Institute London Canada Department of Mechanical and Materials Engineering Western University Department of Electrical and Computer Engineering Western University

A novel parallel Remote Center of Motion (RCM) mechanism is proposed for a surgical robot designed to perform minimally invasive needle-based interventions for lung cancer diagnosis and treatment. The proposed robot provides four degrees of freedom (DOFs) to orient and move a surgical needle within a spherical coordinate system. The RCM is beneath the skin surface to minimize the invasiveness of the surgical procedure while providing the required workspace. This compact, patient-mounted robot benefits from a design capable of measuring the pure interaction forces between the needle and the tissue. In this paper, the mechanism design and its specifications are described. The kinematic analysis is presented and isotropy of the mechanism for targeting tumors is studied. Finally, the performance of the proposed robot is evaluated experimentally.

关键词： Needles Kinematics Robot kinematics Legged locomotion Joints Cancer

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Semi-automatic segmentation of preterm neonate ventricle system from 3D ultrasound images

Semi-automatic segmentation of preterm neonate ventricle sys...

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IEEE International Symposium on biomedical Imaging

作者： W. Qiu J. Yuan J. Kishimoto S. de Ribaupierre E. Ukwatta A. Fenster Robarts Research Institute University of Western Ontario London ON Canada Medical Biophysics University of Western Ontario London ON Canada Biomedical Engineering Graduate Program University of Western Ontario London ON Canada

3D Ultrasound (US) has been developed recently to image the intracranial ventricular system of pre-term neonates in order to monitor these patients for intraventricular hemorrhage (IVH) and the resultant dilatation of the ventricles. 3D US is capable of providing volumetric ventricle measurements, compared to clinically used 2D US, relying on linear measurements from a single slice, and visual and quantitative estimates to determine the severity of ventricular dilatation. In this work, we propose a convex optimization-based segmentation approach for 3D US images of the cerebral ventricles in preterm neonates with IVH. The proposed semi-automatic segmentation method makes use of the latest development in convex optimization techniques supervised by user interactive information. Experiments using 25 3D US images of 5 patients (5 time points for each subject) show that our proposed approach yielded a mean DSC of 78.9% compared to a manually contoured surface. This GPU-implemented semi-automated approach reduced the time required per segmentation by 1200% (mean times: 2.5 vs. 30 minutes). In addition, the intra-observer variability experiments showed that the variability introduced by the user initialization is small in terms of DSC, demonstrating a low intra-user variability.

关键词： Image segmentation Three-dimensional displays Pediatrics Manuals Convex functions Ultrasonic imaging Hemorrhaging

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Three dimensional dose distribution comparison of simple and complex acquisition trajectories in dedicated breast CT - A Monte Carlo study

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12th International Workshop on Breast Imaging, IWDM 2014

作者： Shah, Jainil P. Mann, Steve D. McKinley, Randolph L. Tornai, Martin P. Department of Biomedical Engineering Duke University Durham NC 27705 United States Medical Physics Graduate Program Duke University Medical Center Durham NC 27705 United States Multi Modality Imaging Lab. Duke University Medical Center Durham NC 27710 United States ZumaTek Inc. Research Triangle Park NC 27709 United States

ISBN: (纸本)9783319078861

The purpose of this study was to characterize the three dimensional (3D) x-ray dose distributions in a target scanned with different acquisition trajectories for dedicated breast CT imaging. Monte Carlo simulations were used to evaluate two acquisition trajectories: circular azimuthal (no tilt) and complex sinusoidal (saddle) orbit with ±15° tilts around a pendant breast. Simulations were performed with tungsten (W) and cerium (Ce) filtration of a W-anode source;the simulated source flux was normalized to the measured exposure of a clinically used W-anode source. A water filled cylindrical phantom, was divided into 1cc voxels, and each voxel was set to track the cumulative energy deposited. Energy deposited per voxel was converted to dose, yielding the 3D distributed dose volumes. Results indicate that the mean absorbed dose at the isocenter of a volume for the un-tilted acquisition is ∼10% higher than that from a saddle scan, regardless of filtration used. © 2014 Springer International Publishing.

关键词： Anodes

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Screening for pre-diabetes using support vector machine model

Screening for pre-diabetes using support vector machine mode...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Jai Won Chung Won Jae Kim Soo Beom Choi Jee Soo Park Deok Won Kim Graduate Program in Biomedical Engineering Yonsei University Dept. of Medicine Yonsei University College of Medicine Brain Korea 21 PLUS Project for Medical Science Yonsei University Department of Medical Engineering Yonsei University College of Medicine

ISBN: (纸本)9781424479276

The global prevalence of diabetes is rapidly increasing. Studies support screening and interventions for pre-diabetes, which results in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for pre-diabetes that could assist with decreasing the prevalence of diabetes through early identification and subsequent interventions. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4,685) were used for training and internal validation, while data from KNHANES 2011 (n = 4,566) were used for external validation. We developed a model to screen for pre-diabetes using support vector machine (SVM), and performed a systematic evaluation of the SVM model using internal and external validation. We compared the performance of the SVM model with that of a screening score model based on logistic regression analysis for pre-diabetes that had been developed previously. Backward elimination logistic regression resulted in associations between pre-diabetes and age, sex, waist circumference, body mass index, alcohol intake, family history of diabetes, and hypertension. The areas under the curves (AUCs) for the SVM model in the internal and external datasets were 0.761 and 0.731, respectively, while the AUCs for the screening score model were 0.734 and 0.712, respectively. The SVM model developed in this study performed better than the screening score model that had been developed previously and may be more effective for pre-diabetes screening.

关键词： Delays Stability criteria Numerical stability Time-varying systems Asymptotic stability Discrete-time systems

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Predicting in-hospital code blue events using term frequency inverse document frequency based kernel

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Journal of Electrocardiology 2014年第6期47卷 910-911页

作者： Yong Bai Quan Ding Xiao Hu Biomedical Engineering Graduate Program University of California Los Angeles CA USA Departments of Physiological Nursing and Neurosurgery University of California San Francisco CA USA Institute for Computational Health Sciences University of California San Francisco CA USA

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Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion

Calibrationless parallel imaging reconstruction based on str...

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作者： Shin, Peter J. Larson, Peder E. Z. Ohliger, Michael A. Elad, Michael Pauly, John M. Vigneron, Daniel B. Lustig, Michael Department of Radiology and Biomedical Imaging University of California at San Francisco San FranciscoCA United States UC Berkeley-UCSF Graduate Program in Bioengineering CA United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA United States Department of Computer Science Technion'Israel Institute of Technology Haifa Israel Magnetic Resonance Systems Research Laboratory Department of Electrical Engineering Stanford University StanfordCA United States

Purpose: A calibrationless parallel imaging reconstruction method, termed simultaneous autocalibrating and k-space estimation (SAKE), is presented. It is a data-driven, coil-by-coil reconstruction method that does not require a separate calibration step for estimating coil sensitivity information. Methods: In SAKE, an undersampled, multichannel dataset is structured into a single data matrix. The reconstruction is then formulated as a structured low-rank matrix completion problem. An iterative solution that implements a projection-ontosets algorithm with singular value thresholding is described. Results: Reconstruction results are demonstrated for retrospectively and prospectively undersampled, multichannel Cartesian data having no calibration signals. Additionally, non-Cartesian data reconstruction is presented. Finally, improved image quality is demonstrated by combining SAKE with wavelet-based compressed sensing. Conclusion: Because estimation of coil sensitivity information is not needed, the proposed method could potentially benefit MR applications where acquiring accurate calibration data is limiting or not possible at all. © 2013 Wiley Periodicals, Inc.

关键词： Compressed sensing

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Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine

Multicategory classification of 11 neuromuscular diseases ba...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Soo Beom Choi Jee Soo Park Jai Won Chung Tae Keun Yoo Deok Won Kim Brain Korea 21 PLUS Project for Medical Science Yonsei University Dept. of Medicine Yonsei University College of Medicine Graduate Program in Biomedical Engineering Yonsei University Dept. of Medical Engineering Yonsei University College of Medicine

ISBN: (纸本)9781424479276

We applied multicategory machine learning methods to classify 11 neuromuscular disease groups and one control group based on microarray data. To develop multicategory classification models with optimal parameters and features, we performed a systematic evaluation of three machine learning algorithms and four feature selection methods using three-fold cross validation and a grid search. This study included 114 subjects of 11 neuromuscular diseases and 31 subjects of a control group using microarray data with 22,283 probe sets from the National Center for Biotechnology Information (NCBI). We obtained an accuracy of 100%, relative classifier information (RCI) of 1.0, and a kappa index of 1.0 by applying the models of support vector machines one-versus-one (SVM-OVO), SVM one-versus-rest (OVR), and directed acyclic graph SVM (DAGSVM), using the ratio of genes between categories to within-category sums of squares (BW) feature selection method. Each of these three models selected only four features to categorize the 12 groups, resulting in a time-saving and cost-effective strategy for diagnosing neuromuscular diseases. In addition, a gene symbol, SPP1 was selected as the top-ranked gene by the BW method. We confirmed relationships between the gene (SPP1) and Duchenne muscular dystrophy (DMD) from a previous study. With our models as clinically helpful tools, neuromuscular diseases could be classified quickly using a computer, thereby giving a time-saving, cost-effective, and accurate diagnosis.

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