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Sequential Pattern mining of Longitudinal Adverse Events after Left Ventricular Assist Device Implant

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arXiv 2019年

作者： Movahedi, Faezeh Kormos, Robert L. Lohmueller, Lisa Seese, Laura Kanwar, Manreet Murali, Srinivas Zhang, Yiye Padman, Rema Antaki, James F. Department of Electrical and Computer Engineering Swanson School of Enginering University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center Heart and Vascular Institute PittsburghPA United States School of Computer Science Carnegie Mellon University PittsburghPA United States Cardiovascular Institute at Allegheny General Hospital PittsburghPA United States University of Cornell University Weill Cornell Medical School New York NY United States Carnegie Mellon University Heinz College PittsburghPA United States Biomedical Engineering Department University of Cornell University IthacaNY United States

Left ventricular assist devices (LVADs) are an increasingly common therapy for patients with advanced heart failure. However, implantation of the LVAD increases the risk of stroke, infection, bleeding, and other serious adverse events (AEs). Most post-LVAD AEs studies have focused on individual AEs in isolation, neglecting the possible interrelation, or causality between AEs. This study is the first to conduct an exploratory analysis to discover common sequential chains of AEs following LVAD implantation that are correlated with important clinical outcomes. This analysis was derived from 58,575 recorded AEs for 13,192 patients in International Registry for Mechanical Circulatory Support (INTERMACS) who received a continuousflow LVAD between 2006 and 2015. The pattern mining procedure involved three main steps: (1) creating a bank of AE sequences by converting the AEs for each patient into a single, chronologically sequenced record, (2) grouping patients with similar AE sequences using hierarchical clustering, and (3) extracting temporal chains of AEs for each group of patients using Markov modeling. The mined results indicate the existence of seven groups of sequential chains of AEs, characterized by common types of AEs that occurred in a unique order. The groups were identified as: GRP1: Recurrent bleeding, GRP2: Trajectory of device malfunction & explant, GRP3: Infection, GRP4: Trajectories to transplant, GRP5: Cardiac arrhythmia, GRP6: Trajectory of neurological dysfunction & death, and GRP7: Trajectory of respiratory failure, renal dysfunction & death. These patterns of sequential post-LVAD AEs disclose potential interdependence between AEs and may aid prediction, and prevention, of subsequent AEs in future studies. Copyright © 2019, The Authors. All rights reserved.

关键词： Trajectories

Design, Development and Control of a Three Flexible-Fingers Gripper Based On Hand Gesture

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Design, Development and Control of a Three Flexible-Fingers ...

RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Rasoul Sadeghian Pooyan Sedigh Parastoo Azizinezhad Shahrooz Shahin Mehdi Tale Masouleh Human and Robot Interaction Laboratory Tehran Iran Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran Faculty of Electrical and Computer Science Robotic Engineering Tehran North Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

In this paper, the process of design, and construction of a flexible gripper with three fingers is presented. The fingers of the proposed gripper are fabricated using a 3D printing device. The gripper is controlled remotely by an autonomous glove which is designed to help people with disability. In order to ameliorate the movement accuracy of the gripper a Fuzzy-PID controller is implemented on the system. The result of experimental tests demonstrate a decrease of the gripper movement error to less than 2 degrees in average. An ultrasonic sensor is set on the gripper's body to improve the grasping process by detecting the distance of the object to the gripper. The fabricated fingers are flexible and provides the ability of grasping the objects with irregular shapes. The experimental test approves the significant performance of the gripper in term of grasping complex shape objects.

关键词： Grippers Grasping Flexible printed circuits Robot sensing systems Shape Kalman filters

Nonlinear dipole inversion (ndi) enables quantitative susceptibility mapping (qsm) without parameter tuning

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arXiv 2019年

作者： Polak, Daniel Chatnuntawech, Itthi Yoon, Jaeyeon Iyer, Siddharth Srinivasan Lee, Jongho Bachert, Peter Adalsteinsson, Elfar Setsompop, Kawin Bilgic, Berkin Department of Physics and Astronomy Heidelberg University Heidelberg Germany Department of Radiology A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Siemens Healthcare GmbH Erlangen Germany National Nanotechnology Center Pathum Thani Thailand Department of Electrical and Computer Engineering Laboratory for Imaging Science and Technology Seoul National University Seoul Korea Republic of Department of Electronical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Medical Physics in Radiology Heidelberg Germany Harvard Medical School BostonMA United States Massachusetts Institute of Technology Harvard-MIT Health Sciences and Technology CambridgeMA United States

We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques. In addition to avoiding over-smoothing that these techniques often suffer from, we also obviate the need for parameter selection. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations, and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

Corrigendum to: Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion

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Cerebral cortex (New York, N.Y. : 1991) 2021年第8期31卷 3974页

作者： Ru Kong Jingwei Li Csaba Orban Mert R Sabuncu Hesheng Liu Alexander Schaefer Nanbo Sun Xi-Nian Zuo Avram J Holmes Simon B Eickhoff B T Thomas Yeo Department of Electrical and Computer Engineering ASTAR-NUS Clinical Imaging Research Centre Singapore Institute for Neurotechnology and Memory Networks Program National University of Singapore Singapore. School of Electrical and Computer Engineering Cornell University Ithaca NY USA. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. CAS Key Laboratory of Behavioral Sciences and Research Center for Lifespan Development of Brain and Mind (CLIMB) Institute of Psychology Beijing China. University of Chinese Academy of Sciences Beijing China. Department of Psychology Yale University New Haven CT USA. Institute for Systems Neuroscience Medical Faculty Heinrich-Heine University Düsseldorf Düsseldorf Germany. Institute of Neuroscience and Medicine Brain & Behaviour (INM-7) Research Center Jülich Jülich Germany. Centre for Cognitive Neuroscience DukeNUS Medical School Singapore. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore.

Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided CNN

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arXiv 2018年

作者： Wang, Puyang Patel, Vishal M. Hacihaliloglu, Ilker Department of Electrical and Computer Engineering Rutgers University PiscatawayNJ United States Department of Biomedical Engineering Rutgers University Piscataway United States Department of Radiology Rutger Robert Wood Johnson Medical School New BrunswickNJ United States

Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this work, a multi-feature guided convolutional neural network (CNN) architecture is proposed for simultaneous enhancement, segmentation, and classification of bone surfaces from US data. The proposed CNN consists of two main parts: a pre-enhancing net, that takes the concatenation of B-mode US scan and three filtered image features for the enhancement of bone surfaces, and a modified U-net with a classification layer. The proposed method was validated on 650 in vivo US scans collected using two US machines, by scanning knee, femur, distal radius and tibia bones. Validation, against expert annotation, achieved statistically significant improvements in segmentation of bone surfaces compared to state-of-the-art. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

Overview of quantitative susceptibility mapping using deep learning - Current status, challenges and opportunities

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arXiv 2019年

作者： Jung, Woojin Bollmann, Steffen Lee, Jongho Laboratory for Imaging Science and Technology Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of ARC Training Centre for Innovation in Biomedical Imaging Technology University of Queensland Building 57 of University Dr St Lucia BrisbaneQLD4072 Australia Centre for Advanced Imaging University of Queensland Building 57 of University Dr St LuciaQLD4072 Australia

Quantitative susceptibility mapping (QSM) has gained broad interests in the field by extracting biological tissue properties, predominantly myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source-inversion. Current state of the art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring iterative optimization nor the choice of regularization parameters. Here, we review the current status of deep learning based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM. Copyright © 2019, The Authors. All rights reserved.

关键词： Parameterization

Author Correction: Frontotemporal coordination predicts working memory performance and its local neural signatures

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Nature communications 2021年第1期12卷 3811页

作者： Ehsan Rezayat Mohammad-Reza A Dehaqani Kelsey Clark Zahra Bahmani Tirin Moore Behrad Noudoost School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. dehaqani@ut.ac.ir. Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran. dehaqani@ut.ac.ir. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. Department of Biomedical Engineering Tarbiat Modares University Tehran Iran. Department of Neurobiology Stanford University Stanford CA USA. Howard Hughes Medical Institute Stanford University Stanford CA USA. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. behrad.noudoost@utah.edu.

Forecasting intracranial hypertension using multi-scale waveform metrics

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arXiv 2019年

作者： Hüser, Matthias Kündig, Adrian Karlen, Walter De Luca, Valeria Jaggi, Martin Biomedical Informatics Group Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zürich Zürich8008 Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Machine Learning & Optimization Lab EPFL Lausanne1015 Switzerland

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time serie1s, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain. Copyright © 2019, The Authors. All rights reserved.

关键词： Morphology

A Scalable Optimization Mechanism for Pairwise based Discrete Hashing

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arXiv 2018年

作者： Shi, Xiaoshuang Xing, Fuyong Zhang, Zizhao Sapkota, Manish Guo, Zhenhua Yang, Lin J. Crayton Pruitt Family Department of Biomedical Engineering University of Florida GainesvilleFL United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States Department of Computer Science and Engineering University of Florida GainesvilleFL United States Graduate School at Shenzhen Tsinghua University Shenzhen Guodong China

Maintaining the pair similarity relationship among originally high-dimensional data into a low-dimensional binary space is a popular strategy to learn binary codes. One simiple and intutive method is to utilize two identical code matrices produced by hash functions to approximate a pairwise real label matrix. However, the resulting quartic problem is difficult to directly solve due to the non-convex and non-smooth nature of the objective. In this paper, unlike previous optimization methods using various relaxation strategies, we aim to directly solve the original quartic problem using a novel alternative optimization mechanism to linearize the quartic problem by introducing a linear regression model. Additionally, we find that gradually learning each batch of binary codes in a sequential mode, i.e. batch by batch, is greatly beneficial to the convergence of binary code learning. Based on this significant discovery and the proposed strategy, we introduce a scalable symmetric discrete hashing algorithm that gradually and smoothly updates each batch of binary codes. To further improve the smoothness, we also propose a greedy symmetric discrete hashing algorithm to update each bit of batch binary codes. Moreover, we extend the proposed optimization mechanism to solve the non-convex optimization problems for binary code learning in many other pairwise based hashing algorithms. Extensive experiments on benchmark single-label and multi-label databases demonstrate the superior performance of the proposed mechanism over recent state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking