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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Kittichai Wantanajittikul Suwit Saekho Arintaya Phrommintikul Nipon Theera-Umpon Sansanee Auephanwiriyakul Monruedee Tapanya Busakol Ngammuang Biomedical Engineering Program Chiang Mai University Chiang Mai Thailand Department of Electrical Engineering Chiang Mai University Chiang Mai Thailand Department of Radiologic Technology Chiang Mai University Chiang Mai Thailand Department of Computer Engineering Chiang Mai University Chiang Mai Thailand Department of Internal Medicine Chiang Mai University Chiang Mai Thailand

Heart failure due to iron-overload cardiomyopathy is one of the main causes of mortality. The cardiomyopathy is reversible if intensive iron chelation treatment is done in time. However, the diagnosis is often delayed because the cardiac iron deposition is unpredictable and the symptoms are lately detected. The widely used method in many countries is by calculating a parameter called the T2* (T2-star) from magnetic resonance (MR) image sequences. In order to compute the T2* value, the region of interest (ROI) is manually selected by an expert which may require considerable time and skills. The aim of this work is hence to develop the fully automatic cardiac T2* measurement by using marker-controlled watershed for segmenting the interventricular septum in cardiac MR images. Mathematical morphologies are also used to reduce some errors. Moreover, a new approach for T2* evaluation is suggested in this work. The interventricular septa from MR images in all echo times (TE's) are segmented to calculate the signal intensities (SI's) while the classical method segments the interventricular septum only in the first TE. Thirty cardiac MR images were used in this work. The segmentation performances are evaluated only from the MR images in the first TE. The precision and recall are 0.9 and 0.8 compared with the two experts' opinions. It shows that the segmentation results of our proposed technique are quite similar to the experts' opinions. The T2* values were carried out based on the automatically segmented ROI's. The mean difference of T2* values between the proposed method and the experts' opinions is about 1 ms. This demonstrates that the T2* values from the proposed technique are also close to T2* values calculated by the experts.

关键词： Image segmentation Iron Magnetic resonance imaging Morphology Estimation biomedical engineering Conferences

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A New Approach for SSVEP Detection Using PARAFAC and Canonical Correlation Analysis

A New Approach for SSVEP Detection Using PARAFAC and Canonic...

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

作者： Richard Tell Saeed Pouryazdian Andre Ferreira Soosan Beheshti Sridhar Krishnan Teodiano Bastos Post-Graduate Program in Electrical Engineering (PPGEE). UFES. Av. Fernando Ferrari 514. Vitoria Brazil. Department of Electrical and Computer Engineering. Ryerson University. 350 Victoria St Toronto ON M5B 2K3 Canada.

ISBN: (纸本)9781424492695

This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel x frequency x time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

关键词： EEG SSVEPs PARAFAC

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Mumford-Shah based unsupervised segmentation of brain tissue on MR images

Mumford-Shah based unsupervised segmentation of brain tissue...

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13th Mediterranean Conference on Medical and Biological engineering and Computing 2013, MEDICON 2013

作者： Cevik, A. Eyuboglu, B.M. Biomedical Engineering Graduate Program Graduate School of Natural and Applied Sciences Turkey Department of Electrical and Electronics Engineering Middle East Technical University 06800 Ankara Turkey

ISBN: (纸本)9783319008455

Automated segmentation of different tissues on medical images is a crucial concept for medical image analysis. In this study, unsupervised image segmentation problem is generalized as a Mumford-Shah energy minimization problem, and several solution proposals for the problem are investigated. Ambrosio-Tortorelli approximation method is implemented, and the performance of the algorithm on magnetic resonance (MR) images of brain is evaluated. First image used in the experiments is chosen among the ones which contain an edema formation due to a brain tumor, and the second one belongs to a healthy subject on which gray matter/white matter segmentation is aimed. Acquired results are presented in visual, tabular and numerical forms. Results and performance are discussed and quantitatively evaluated. © Springer International Publishing Switzerland 2014.

关键词： Magnetic resonance

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Maximizing predictability of a bottom-up complex multi-scale model through systematic validation and multi-objective multi-level optimization

Maximizing predictability of a bottom-up complex multi-scale...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Jean-Marie C. Bouteiller Zhuobo Feng Alexander Onopa Mike Huang Eric Y. Hu Endre Somogyi Michel Baudry Serge Bischoff Theodore W. Berger Department of Biomedical Engineering University of Southern California Los Angeles CA USA Department of Computer Science University of Southern California Los Angeles USA Department of Electrical Engineering University of Southern California Los Angeles USA University of Southern California Los Angeles CA US Biocomplexity Institute Indiana University Bloomington Bloomington IN USA Graduate College of Biomedical Sciences Western University of Health Sciences Pomona CA USA Rhenovia Pharma 20c Mulhouse FRANCE Engineering with the Department of Biomedical Engineering University of Southern California Los Angeles CA USA

Computational models are mathematical representations meant to replicate the biological system they represent, as well as provide insights and predict the system's dynamics in response to changing conditions. In a bottom-up modeling approach, a multitude of models may be compounded to represent more complex higher level biological systems. However, guaranteeing the validity and predictability of the compounded ensemble may become increasingly challenging as more components are integrated. We herein present a sequential and iterative method to maximize predictability of a complex multiscale model. We have successfully developed a multiscale modeling platform comprised of mechanisms ranging from the biomolecular level to multi-cellular networks. To maintain a high level of predictability of the global platform, we introduce a systematic approach to not only validate all models independently, but also verify the validity of compounded models as additional information becomes available at higher levels of complexity. Iterative and systematic application of these validation steps at increasing levels of complexity is intended to maximize the predictive power of the platform, making it a powerful tool to study the impacts of low-levels modifications (pathologies, drugs, etc.) on higher functional levels. The work presented lays down the rationale of the approach, the open design implementation and results.

关键词： Biological system modeling Computational modeling Optimization Predictive models Neurons Mathematical model Complexity theory

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

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BMC Neurosci 2017年第SUPPL 1期18卷 58-58页

作者： Sue Denham Panayiota Poirazi Erik De Schutter Karl Friston Ho Ka Chan Thomas Nowotny Dongqi Han Sungho Hong Sophie Rosay Tanja Wernle Alessandro Treves Sarah Goethals Romain Brette Tomas Van Pottelbergh Rodolphe Sepulchre Alex D. Bird Hermann Cuntz Pedro J. Gonçalves Jan-Matthis Lueckmann Giacomo Bassetto Marcel Nonnenmacher Jakob H. Macke Audrey J. Sederberg Jason N. MacLean Stephanie E. Palmer Ulisse Ferrari Christophe Gardella Olivier Marre Thierry Mora Emina Ibrahimovic Martin Müller Jean-Pascal Pfister Tushar Chauhan Timothée Masquelier Alexandre Montlibert Benoit R. Cottereau Moritz Helias Jannis Schuecker David Dahmen Sven Goedeke Alexandre Hyafil Ainhoa Hermoso-Mendizabal Pavel E. Rueda-Orozco Santiago Jaramillo David Robbe Jaime de la Rocha Marie Rooy Fani Koukouli David DiGregorio Uwe Maskos Boris Gutkin Andrey Yu Verisokin Darya V. Verveyko Dmitry E. Postnov Willy Wong Omid Talakoub Robert Chen Milos Popovic Dmitriy Lisitsyn Eric Drebitz Iris Grothe Sunita Mandon Andreas Kreiter Udo Ernst Peter A. Robinson Xuelong Zhao Kevin M. Aquino John D. Griffiths Grishma Mehta-Pandejee Natasha Gabay James MacLaurin Somwrita Sarkar Tim Kunze Jens Haueisen Thomas R. Knösche Subutai Ahmad Yuwei Cui Marcus Lewis Jeff Hawkins Simona Olmi Spase Petkoski Fabrice Bartolomei Maxime Guye Viktor Jirsa Hazem Toutounji Daniel Durstewitz Matteo Cantarelli Adrian Quintana Boris Marin Matt Earnshaw Padraig Gleeson Robert Court Robert McDougal R. Angus Silver Salvador Dura-Bernal Stephen Larson William W. Lytton Giovanni Idili Lorenzo Posani Simona Cocco Karel Ježek Rémi Monasson School of Psychology Faculty of Health & Human Sciences Plymouth University Plymouth Devon PL4 8AA UK Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology-Hellas (FORTH) Heraklion Crete Greece Okinawa Institute of Science and Technology Graduate University Okinawa Japan Wellcome Trust Centre for Neuroimaging Institute of Neurology UCL London WC1N 3BG UK School of Engineering and Informatics University of Sussex Brighton BN1 9QJ UK Graduate School Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Cognitive Neuroscience Area Scuola Internazionale Superiore di Studi Avanzati Trieste 34136 Italy Kavli Institute for Systems Neuroscience NTNU Trondheim 7030 Norway Institut de la Vision Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Paris 75012 France Department of Engineering University of Cambridge Cambridge Cambridgeshire CB2 1PZ UK Ernst Strüngmann Institute for Neuroscience Max Planck Society Frankfurt Germany Frankfurt Institute for Advanced Studies Frankfurt Germany Neural Systems Analysis Research Center Caesar An Associate of the Max Planck Society Bonn Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Present Address: Biomedical Engineering Department Georgia Institute of Technology Atlanta GA 30306 USA Department of Neurobiology University of Chicago Chicago IL 60637 USA Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 USA Committee on Computational Neuroscience University of Chicago Chicago IL 60637 USA Institut de la Vision Sorbonne Universités INSERM Paris France Laboratoire de Physique Statistique Ecole Normale Supérieure Paris France Institute of Molecular Life Sciences University of Zürich 8057 Zürich Switzerland Institute of Neuroinformatics Univer

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Assistive robotic manipulation through shared autonomy and a Body-Machine Interface

Assistive robotic manipulation through shared autonomy and a...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Siddarth Jain Ali Farshchiansadegh Alexander Broad Farnaz Abdollahi Ferdinando Mussa-Ivaldi Brenna Argall Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA Sensory Motor and Performance Program Rehabilitation Institute of Chicago Chicago IL USA Department of Biomedical Engineering Northwestern University Evanston IL USA Department of Physiology Northwestern University Chicago IL USA Department of Physical Medicine and Rehabilitation Northwestern University Chicago IL USA

ISBN: (纸本)9781479918096

Assistive robotic manipulators have the potential to improve the lives of people with motor impairments. They can enable individuals to perform activities such as pick-and-place tasks, opening doors, pushing buttons, and can even provide assistance in personal hygiene and feeding. However, robotic arms often have more degrees of freedom (DoF) than the dimensionality of their control interface, making them challenging to use-especially for those with impaired motor abilities. Our research focuses on enabling the control of high-DoF manipulators to motor-impaired individuals for performing daily tasks. We make use of an individual's residual motion capabilities, captured through a Body-Machine Interface (BMI), to generate control signals for the robotic arm. These low-dimensional controls are then utilized in a shared-control framework that shares control between the human user and robot autonomy. We evaluated the system by conducting a user study in which 6 participants performed 144 trials of a manipulation task using the BMI interface and the proposed shared-control framework. The 100% success rate on task performance demonstrates the effectiveness of the proposed system for individuals with motor impairments to control assistive robotic manipulators.

关键词： Manipulators Trajectory Rehabilitation robotics Robot kinematics Aerospace electronics

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A quantitative classification of essential and Parkinson's tremor using wavelet transform and artificial neural network on sEMG and accelerometer signals

A quantitative classification of essential and Parkinson's t...

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IEEE International Conference on Networking, Sensing and Control

作者： Santosh Kumar Nanda Wen-Yen Lin Ming-Yih Lee Rou-Shayn Chen PhD Program in Biomedical Engineering Chang Gung University Taiwan Tao-Yuan Taiwan Graduate Institute of Medical Mechatronics Chang Gung University Taiwan Tao-Yuan Taiwan Department of Electrical Engineering Chang Gung University Taiwan Tao-Yuan Taiwan Department of Neurology Chang Gung Memorial Hospital Taiwan Tao-Yuan Taiwan

Correct discrimination of essential tremor from Parkinson's tremor is a major problem in clinical neurology as minor differences in the tremor patterns are hard to distinguish. Mathematical analysis of tremor signals recorded non-invasively has been widely accepted for tremor differentiation. However, classification of tremor signals collected from electromyograph or accelerometer, based on time and frequency domain techniques has limited accuracy because of overlapping frequency range and non-stationary nature of those signals. This paper describes a simple, non-invasive decision making logic method for discrimination of tremor. Wavelet transform based feature extraction technique in combination with feed forward type artificial neural network is proposed. Fractal dimensions of wavelet features of the decomposed detailed coefficients are used as the feature matrix. The neural network classified the tremor sEMG signals with 91.66% accuracy and 100% in case of accelerometer signals. Although, the classification accuracy of sEMG signal is comparable to that of accelerometer but the localized involuntary vibratory nature of tremor at the extremities of human body puts accelerometer as a better option in cases where tremor fails to excite the muscle. This proposed classification algorithm adds strength to the non-invasive signal detection methods at reduced cost and higher sensitivity.

关键词： Accelerometers Artificial neural networks Fractals Wavelet analysis Wavelet transforms Accuracy

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Bioelectronic Interfaces: Inkjet‐Printed Flexible Gold Electrode Arrays for Bioelectronic Interfaces (Adv. Funct. Mater. 7/2016)

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Advanced Functional Materials 2016年第7期26卷

作者： Yasser Khan Felippe J. Pavinatto Monica C. Lin Amy Liao Sarah L. Swisher Kaylee Mann Vivek Subramanian Michel M. Maharbiz Ana C. Arias Department of Electrical Engineering and Computer Sciences University of California Berkeley CA 94720 USA Physics Institute of São Carlos University of São Paulo CEP: 13566‐590 São Carlos SP Brazil Joint Graduate Program in Bioengineering University of California Berkeley CA 94720 USA

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Patient-specific pipeline to create virtual endoscopic third ventriculostomy scenarios

Patient-specific pipeline to create virtual endoscopic third...

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21st Medicine Meets Virtual Reality Conference, NextMed/MMVR 2014

作者： Armstrong, Ryan Eagleson, Roy De Ribaupierre, Sandrine Biomedical Engineering Graduate Program University of Western Ontario 1151 Richmond St. London ON Canada Department of Clinical Neurological Sciences University of Western Ontario Canada Department of Electrical and Computer Engineering University of Western Ontario Canada

ISBN: (纸本)9781614993742

Graphically realistic, haptics-enabled, virtual reality surgical simulations are becoming increasingly popular training tools for neurosurgical procedures and surgical skills. Compared with traditional training models, virtual simulations are safe, versatile, consistent, and are relatively cost effective. Most simulations are deficient, however, in representing the diverse anatomical variations that occur clinically. In this work, we describe the design and implementation of a pipeline to create patient-specific surgical scenarios for simulator-based training of the endoscopic third ventriculostomy: the procedure of choice for treating obstructive hydrocephalus. © 2014 The authors and IOS Press. All rights reserved.

关键词： Virtual reality

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Correction: Liquid-capped encoded microcapsules for multiplex assays

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Lab on a chip 2017年第4期17卷 738页

作者： Younghoon Song Yunjin Jeong Taehong Kwon Daewon Lee Dong Yoon Oh Tae-Joon Park Junhoi Kim Jiyun Kim Sunghoon Kwon Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Department of Electrical and Computer Science Seoul National University Seoul 151-742 Republic of Korea. Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Interdisciplinary Program of Bioengineering Seoul National University Seoul 151-742 Republic of Korea. Nano Systems Institute Seoul National University Seoul 151-742 Republic of Korea. Department of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan 44919 Republic of Korea. Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Department of Electrical and Computer Science Seoul National University Seoul 151-742 Republic of Korea and Interdisciplinary Program of Bioengineering Seoul National University Seoul 151-742 Republic of Korea and Nano Systems Institute Seoul National University Seoul 151-742 Republic of Korea and Seoul National University Hospital Biomedical Research Institute Seoul National University Hospital Seoul 151-742 Republic of Korea and Quantamatrix Inc. Seoul 151-742 Republic of Korea.

Correction for 'Liquid-capped encoded microcapsules for multiplex assays' by Younghoon Song et al., Lab Chip, 2017, DOI: .

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