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Dry-contact and noncontact biopotential electrodes: Methodological review

IEEE Reviews in Biomedical engineering 2010年 3卷 106-119页

作者： Chi, Yu Mike Jung, Tzyy-Ping Cauwenberghs, Gert Department of Electrical and Computer Engineering Jacobs School of Engineering University of California San Diego CA 92093 United States Swartz Center for Computational Neuroscience Institute for Neural Computation University of California San Diego CA 92093 United States Department of Bioengineering Jacobs School of Engineering University of California San Diego CA 92093 United States

Recent demand and interest in wireless, mobile-based healthcare has driven significant interest towards developing alternative biopotential electrodes for patient physiological monitoring. The conventional wet adhesive Ag/AgCl electrodes used almost universally in clinical applications today provide an excellent signal but are cumbersome and irritating for mobile use. While electrodes that operate without gels, adhesives and even skin contact have been known for many decades, they have yet to achieve any acceptance for medical use. In addition, detailed knowledge and comparisons between different electrodes are not well known in the literature. In this paper, we explore the use of dry/noncontact electrodes for clinical use by first explaining the electrical models for dry, insulated and noncontact electrodes and show the performance limits, along with measured data. The theory and data show that the common practice of minimizing electrode resistance may not always be necessary and actually lead to increased noise depending on coupling capacitance. Theoretical analysis is followed by an extensive review of the latest dry electrode developments in the literature. The paper concludes with highlighting some of the novel systems that dry electrode technology has enabled for cardiac and neural monitoring followed by a discussion of the current challenges and a roadmap going forward. © 2008 IEEE.

关键词： Electrocardiograms

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Development of Planar Microelectrode Array for Extracellular Characterization of Action Potentials in Sinoatrial Node Cells

Development of Planar Microelectrode Array for Extracellular...

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2011 International Symposium on Bioelectronics and Bioinformatics(第二届国际生物医学电子学与生物信息学学术会议 ISBB 2011)

作者： Xin Zhang Yulong Zhang Yingxin Li Richard D. Nelson John C. LaRue Neural Engineering Research Lab Z&L Creative Corporation Lewes DE 19958 USA and the Department of Pen-Tung Sah Micro-Nano Technology Research Center Xiamen University Fujian 361005 P. R. China Department of Physiology Temple University School of Medicine Philadelphia PA 19140 USA Department of Electrical Engineering and Computer Science University of California Irvine CA 9269 Mechanical and Aerospace Engineering Department University of California Irvine CA 92697 USA

In this research work we prototyped a planar microelectrode arrays that utilize SU-8 as the insulating material for non-invasive chronically characterization of action potentials from SA node cells. The microelectrode arrays are fabricated to form a microchannel based extracellular action potential characterization system which comprises a 2 by 50 linear microelectrode array for acquiring the action potentials with the principle of capacitive coupling and a full customized CMOS analog bio-signal readout ASIC for bio-signal conditioning and processing. The prototyped multi-channel mixed signal fully-customized CMOS biosensor analog front-end serves as the low magnitude action potential signal readout circuitry, which is comprised of analog signal buffers, high gain amplifier and control/interface. The concept of using epoxy-based photoresist SU-8 as the insulator instead of the conventional silicon nitride or silicon dioxide is due to the high insulation property and biocompatibility of the SU-8 material. This biocompatible planar microelectrode arrays with microchannels provides the biological scientists powerful tools for the long term non-invasive study of action potentials from SA node cells to screen libraries of chemical compounds for new drug candidates.

关键词： Arrays Microelectrodes Biosensors Insulators Electric potential

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Stochastic differential dynamic programming

Stochastic differential dynamic programming

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作者： Theodorou, Evangelos Tassa, Yuval Todorov, Emo Departments of Computer Science and Neuroscience Computational Learning and Motor Control Lab. University of Southern California United States Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Department of Computer Science and Engineering University of Washington Seattle WA United States

ISBN: (纸本)9781424474264

Although there has been a significant amount of work in the area of stochastic optimal control theory towards the development of new algorithms, the problem of how to control a stochastic nonlinear system remains an open research topic. Recent iterative linear quadratic optimal control methods iLQG [1], [2] handle control and state multiplicative noise while they are derived based on first order approximation of dynamics. On the other hand, methods such as Differential Dynamic Programming expand the dynamics up to the second order but so far they can handle nonlinear systems with additive noise. In this work we present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state and control multiplicative process noise, and proceed to derive the second-order expansion of the cost-to-go. We find the correction terms that arise from the stochastic assumption. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP. © 2010 AACC.

关键词： Additive noise

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The story of a single cell: Peeking into the semantics of spikes

The story of a single cell: Peeking into the semantics of sp...

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International Workshop on Cognitive Information Processing

作者： Kliper, Roi Serre, Thomas Weinshall, Daphna Nelken, Israel Interdisciplinary Center for Neural Computation Israel School of Computer Science and Engineering United States Department of Neurobiology Alexander Silberman Institute of Life Sciences Hebrew University of Jerusalem Jerusalem 91904 Israel Brown University Department of Cognitive and Linguistic Sciences United States

ISBN: (纸本)9781424464593

Traditionally, the modeling of sensory neurons has focused on the characterization and/or the learning of input-output relations. Motivated by the view that different neurons impose different partitions on the stimulus space, we propose instead to learn the structure of the stimulus space, as imposed by the cell, by learning a cell specific distance function or kernel. Metaphorically speaking, this direction attempts to bypass the syntactic question of "how the cell speaks", by focusing instead on the semantic and fundamental question of "what the cell says". Here we consider neural data from both the inferotemporal cortex (ITC) and the prefrontal cortex (PFC) of macaque monkeys. We learn a cell-specific distance function over the stimulus space as induced by the cell response;the goal is to learn a function such that the distance between stimuli is large when the responses they evoke are very different, and small when the responses they evoke are similar. Our main result shows that after training, when given new stimuli, our ability to predict their similarity to previously seen stimuli is significantly improved. We attempt to exploit this ability to predict the response of the cell to a novel stimuli using KNN over the learnt distances. Furthermore, using our learned kernel we obtain a partitioning of the stimulus space which is more similar to the partition induced by the cell responses as reveled by low dimension embedding, and thus, are able in some of the cases to peek at the semantic partition induced by the cell. © 2010 IEEE.

关键词： Cells

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Universal One-Way Hash Functions via Inaccessible Entropy

Universal One-Way Hash Functions via Inaccessible Entropy

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29th Annual International Conference on Theory and Applications of Cryptographic Techniques

作者： Haitner, Iftach Holenstein, Thomas Reingold, Omer Vadhan, Salil Wee, Hoeteck Microsoft Research New England United States Department of Computer Science ETH Zurich Switzerland Microsoft Research Silicon Valley United States Weizmann Institute of Science Israel School of Engineering and Applied Sci. Center for Research on Computation and Society Harvard University United States Queens College CUNY United States

ISBN: (纸本)9783642131899

This paper revisits the construction of Universal One-Way Hash Functions (UOWHFs) from any one-way function due to Rompel (STOC 1990). We give a simpler construction of UOWHFs, which also obtains better efficiency and security. The construction exploits a strong connection to the recently introduced notion of inaccessible entropy (Haitner et al. STOC 2009). With this perspective, we observe that a small tweak of any one-way function f is already a weak form of a UOWHF: Consider F(x, i) that outputs the i-bit long prefix of f(x). If F were a UOWHF then given a random a;and i it would be hard to come up with x' not equal x such that F(x, i) = F(x', i). While this may not be the case, we show (rather easily) that it is hard to sample x' with almost full entropy among all the possible such values of x'. The rest of our construction simply amplifies and exploits this basic property. With this and other recent works, we have that the constructions of three fundamental cryptographic primitives (Pseudorandom Generators, Statistically Hiding Commitments and UOWHFs) out of one-way functions are to a large extent unified. In particular, all three constructions rely on and manipulate computational notions of entropy in similar ways. Pseudorandom Generators rely on the well-established notion of pseudoentropy, whereas Statistically Hiding Commitments and UOWHEs rely on the newer notion of inaccessible entropy.

关键词： computational complexity cryptography hashing target collision-resistance one-way functions

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Stochastic Differential Dynamic Programming

Stochastic Differential Dynamic Programming

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American Control Conference

作者： Evangelos Theodorou Yuval Tassa Emo Todorov Computational Learning and Motor Control Lab Departments of Computer Science and Neuroscience University of Southern California Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Department of Computer Science and Engineering and the Department of Applied Mathematics University of Washington Seattle WA

ISBN: (纸本)9781424474264

关键词： dynamic programming theory of optimal control multiplicative noise Quartic stochastic derivative Additive noise correct entry dynamics Work area Handles

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The Limits of Two-Party Differential Privacy

The Limits of Two-Party Differential Privacy

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Annual IEEE Symposium on Foundations of computer science

作者： Andrew McGregor Ilya Mironov Toniann Pitassi Omer Reingold Kunal Talwar Salil Vadhan Department of Computer Science University of Massachusetts Amherst USA Microsoft Research Silicon Valley Department of Computer Science University of Toronto School of Engineering and Applied Sciences and Center for Research on Computation and Society Harvard University

We study differential privacy in a distributed setting where two parties would like to perform analysis of their joint data while preserving privacy for both datasets. Our results imply almost tight lower bounds on the accuracy of such data analyses, both for specific natural functions (such as Hamming distance) and in general. Our bounds expose a sharp contrast between the two-party setting and the simpler client-server setting (where privacy guarantees are one-sided). In addition, those bounds demonstrate a dramatic gap between the accuracy that can be obtained by differentially private data analysis versus the accuracy obtainable when privacy is relaxed to a computational variant of differential privacy. The first proof technique we develop demonstrates a connection between differential privacy and deterministic extraction from Santha-Vazirani sources. A second connection we expose indicates that the ability to approximate a function by a low-error differentially private protocol is strongly related to the ability to approximate it by a low communication protocol. (The connection goes in both directions).

关键词： Protocols Privacy Complexity theory Data privacy Hamming distance Sensitivity Additives

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Boosting and Differential Privacy

Boosting and Differential Privacy

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Annual IEEE Symposium on Foundations of computer science

作者： Cynthia Dwork Guy N. Rothblum Salil Vadhan Microsoft Research Limited Mountain View CA USA Center for Computational Intractability and Department of Computer Science Princeton University Princeton NJ USA School of Engineering and Applied Sciences and Center for Research on Computation and Society Harvard University Cambridge MA USA

Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacy-pre serving synopses of an input database. These are data structures that yield, for a given set Q of queries over an input database, reasonably accurate estimates of the responses to every query in Q, even when the number of queries is much larger than the number of rows in the database. Given a base synopsis generator that takes a distribution on Q and produces a "weak" synopsis that yields "good" answers for a majority of the weight in Q, our Boosting for Queries algorithm obtains a synopsis that is good for all of Q. We ensure privacy for the rows of the database, but the boosting is performed on the queries. We also provide the first synopsis generators for arbitrary sets of arbitrary low-sensitivity queries, i.e., queries whose answers do not vary much under the addition or deletion of a single row. In the execution of our algorithm certain tasks, each incurring some privacy loss, are performed many times. To analyze the cumulative privacy loss, we obtain an O(ε 2 ) bound on the expected privacy loss from a single e-differentially private mechanism. Combining this with evolution of confidence arguments from the literature, we get stronger bounds on the expected cumulative privacy loss due to multiple mechanisms, each of which provides e-differential privacy or one of its relaxations, and each of which operates on (potentially) different, adaptively chosen, databases.

关键词： Databases Privacy Boosting Data privacy Generators Accuracy Data structures

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Curvelet-based sampling for accurate and efficient multimodal image registration

Curvelet-based sampling for accurate and efficient multimoda...

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Medical Imaging 2009 - Image Processing

作者： Safran, M.N. Freiman, M. Werman, M. Joskowicz, L. Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Israel School of Engineering and Computer Science Hebrew University of Jerusalem Israel

ISBN: (纸本)9780819475107

We present a new non-uniform adaptive sampling method for the estimation of mutual information in multi-modal image registration. The method uses the Fast Discrete Curvelet Transform to identify regions along anatomical curves on which the mutual information is computed. Its main advantages of over other non-uniform sampling schemes are that it captures the most informative regions, that it is invariant to feature shapes, orientations, and sizes, that it is efficient, and that it yields accurate results. Extensive evaluation on 20 validated clinical brain CT images to Proton Density (PD) and T1 and T2-weighted MRI images from the public RIRE database show the effectiveness of our method. Rigid registration accuracy measured at 10 clinical targets and compared to ground truth measurements yield a mean target registration error of 0.68mm(std=0.4mm) for CT-PD and 0.82mm(std=0.43mm) for CT-T2. This is 0.3mm (1mm) more accurate in the average (worst) case than five existing sampling methods. Our method has the lowest registration errors recorded to date for the registration of CT-PD and CT-T2 images in the RIRE website when compared to methods that were tested on at least three patient datasets. © 2009 Copyright SPIE - The International Society for Optical engineering.

关键词： Magnetic resonance imaging

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On the reliability of clustering stability in the large sample regime - Supplementary material

On the reliability of clustering stability in the large samp...

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22nd Annual Conference on neural Information Processing Systems, NIPS 2008

作者： Shamir, Ohad Tishby, Naftali School of Computer Science and Engineering Hebrew University Jerusalem 91904 Israel Interdisciplinary Center for Neural Computation Hebrew University Jerusalem 91904 Israel

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