A New Approach to Multidimensional Pattern Matching

Coordinated Science Laboratory was formerly known as Control Systems Laborator

A new LMS algorithm for analysis of atrial fibrillation signals

Background A biomedical signal can be defined by its extrinsic features (x-axis and y-axis shift and scale) and intrinsic features (shape after normalization of extrinsic features). In this study, an LMS algorithm utilizing the method of differential steepest descent is developed, and is tested by normalization of extrinsic features in complex fractionated atrial electrograms (CFAE). Method Equations for normalization of x-axis and y-axis shift and scale are first derived. The algorithm is implemented for real-time analysis of CFAE acquired during atrial fibrillation (AF). Data was acquired at a 977 Hz sampling rate from 10 paroxysmal and 10 persistent AF patients undergoing clinical electrophysiologic study and catheter ablation therapy. Over 24 trials, normalization characteristics using the new algorithm with four weights were compared to the Widrow-Hoff LMS algorithm with four tapped delays. The time for convergence, and the mean squared error (MSE) after convergence, were compared. The new LMS algorithm was also applied to lead aVF of the electrocardiogram in one patient with longstanding persistent AF, to enhance the F wave and to monitor extrinsic changes in signal shape. The average waveform over a 25 s interval was used as a prototypical reference signal for matching with the aVF lead. Results Based on the derivation equations, the y-shift and y-scale adjustments of the new LMS algorithm were shown to be equivalent to the scalar form of the Widrow-Hoff LMS algorithm. For x-shift and x-scale adjustments, rather than implementing a long tapped delay as in Widrow-Hoff LMS, the new method uses only two weights. After convergence, the MSE for matching paroxysmal CFAE averaged 0.46 ± 0.49μV2/sample for the new LMS algorithm versus 0.72 ± 0.35μV2/sample for Widrow-Hoff LMS. The MSE for matching persistent CFAE averaged 0.55 ± 0.95μV2/sample for the new LMS algorithm versus 0.62 ± 0.55μV2/sample for Widrow-Hoff LMS. There were no significant differences in estimation error for paroxysmal versus persistent data. From all trials, the mean convergence time was approximately 1 second for both algorithms. The new LMS algorithm was useful to enhance the electrocardiogram F wave by subtraction of an adaptively weighted prototypical reference signal from the aVF lead. The extrinsic weighting over 25 s demonstrated that time-varying functions such as patient respiration could be identified and monitored. Conclusions A new LMS algorithm was derived and used for normalization of the extrinsic features in CFAE and for electrocardiogram monitoring. The weighting at convergence provides an estimate of the degree of similarity between two signals in terms of x-axis and y-axis shift and scale. The algorithm is computationally efficient with low estimation error. Based on the results, proposed applications include monitoring of extrinsic and intrinsic features of repetitive patterns in CFAE, enhancement of the electrocardiogram F wave and monitoring of time-varying signal properties, and to quantitatively characterize mechanistic differences in paroxysmal versus persistent AF

Adaptive statistical recognition of hand-printed Telugu characters

A brief description of statistical and syntactic pattern matching techniques is presented with an emphasis on statistical techniques. The characteristics of the Telugu script are described. A subset of 16 characters, which are both easy and hard to recognize, is selected for the dictionary of standard characters. A weighted linear difference polynomial of features is used to recognize Telugu characters. The features were Fourier descriptors of projection profiles and cross sections taken in various directions. Algorithms for obtaining the projection profiles cross sections and adaptive learning method are presented. The system was trained and tested with a set of 8 nano-written samples of each of 16 different Telugu characters. More than 90% of the 123 samples were correctly recognized by the system. Results of numerous trials examining the different features and classification techniques are discussed

Model driven segmentation and the detection of bone fractures

Bibliography: leaves 83-90.The introduction of lower dosage image acquisition devices and the increase in computational power means that there is an increased focus on producing diagnostic aids for the medical trauma environment. The focus of this research is to explore whether geometric criteria can be used to detect bone fractures from Computed Tomography data. Conventional image processing of CT data is aimed at the production of simple iso-surfaces for surgical planning or diagnosis - such methods are not suitable for the automated detection of fractures. Our hypothesis is that through a model-based technique a triangulated surface representing the bone can be speedily and accurately produced. And, that there is sufficient structural information present that by examining the geometric structure of this representation we can accurately detect bone fractures. In this dissertation we describe the algorithms and framework that we built to facilitate the detection of bone fractures and evaluate the validity of our approach

Determination of Left Ventricular Contours: A Probabilistic Algorithm Derived from Angiographic Images

journal articleBiomedical Informatic

Effects of forensically-relevant facial concealment on acoustic and perceptual properties of consonants

This thesis offers a thorough investigation into the effects of forensically-relevant facial concealment on speech acoustics and perception. Specifically, it explores the extent to which selected acoustic-phonetic and auditory-perceptual properties of consonants are affected when the talker is wearing ‘facewear’ while speaking. In this context, the term ‘facewear’ refers to the various types of face-concealing garments and headgear that are worn by people in common daily communication situations; for work and leisure, or as an expression of religious, social and cultural affiliation (e.g. surgical masks, motorcycle helmets, ski and cycling masks, or full-face veils such as the niqāb). It also denotes the face or head coverings that are typically used as deliberate (visual) disguises during the commission of crimes and in situations of public disorder (e.g. balaclavas, hooded sweatshirts, or scarves). The present research centres on the question: does facewear influence the way that consonants are produced, transmitted, and perceived? To examine the effects of facewear on the acoustic speech signal, various intensity, spectral, and temporal properties of spoken English consonants were measured. It was found that facewear can considerably alter the acoustic-phonetic characteristics of consonants. This was likely to be the result of both deliberate and involuntary changes to the talker’s speech productions, and of sound energy absorption by the facewear material. The perceptual consequences of the acoustic modifications to speech were assessed by way of a consonant identification study and a talker discrimination study. The results of these studies showed that auditory-only and auditory-visual consonant intelligibility, as well as the discrimination of unfamiliar talkers, may be greatly compromised when the observer’s judgements are based on ‘facewear speech’. The findings reported in this thesis contribute to our understanding of how auditory and visual information interact during natural speech processing. Furthermore, the results have important practical implications for legal cases in which speech produced through facewear is of pivotal importance. Forensic speech scientists are therefore advised to take the possible effects of facewear on speech into account when interpreting the outcome of their acoustic and auditory analyses of evidential speech recordings, and when evaluating the reliability of earwitness testimony

Spectral control of viscous alignment for deformation invariant image matching

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 55-57).We present a new approach to deformation invariant image matching. Our approach retains the broad range of linear and nonlinear deformations that viscous alignment methods can model, but introduces a selectivity that is necessary for recognition. Our method models viscous kernels with an over-complete filter basis. The basis is parameterized with a single scalar parameter, the spectral radius r, which selects deformations ranging in complexity from tranlations to "turbulence." The spectral radius is used for cascaded alignment starting from low deformation frequencies and finishing with high deformation frequencies. Cascaded alignment makes deformation invariant matching for recognition feasible and efficient. Because spectral radii map directly to deformation complexity, their contributions are selectively weighed to calculate the template-target similarity. In this way, our model can distinguish deformations by their relevance to recognition, without losing the flexibility of viscous alignment for handling nonlinear deformations. Our approach is applied to recognize flexible bodies of animals, and results indicate that the method is very promising.by Christopher Minzer Yang.M.Eng

DEFORM'06 - Proceedings of the Workshop on Image Registration in Deformable Environments

Preface These are the proceedings of DEFORM'06, the Workshop on Image Registration in Deformable Environments, associated to BMVC'06, the 17th British Machine Vision Conference, held in Edinburgh, UK, in September 2006. The goal of DEFORM'06 was to bring together people from different domains having interests in deformable image registration. In response to our Call for Papers, we received 17 submissions and selected 8 for oral presentation at the workshop. In addition to the regular papers, Andrew Fitzgibbon from Microsoft Research Cambridge gave an invited talk at the workshop. The conference website including online proceedings remains open, see http://comsee.univ-bpclermont.fr/events/DEFORM06. We would like to thank the BMVC'06 co-chairs, Mike Chantler, Manuel Trucco and especially Bob Fisher for is great help in the local arrangements, Andrew Fitzgibbon, and the Programme Committee members who provided insightful reviews of the submitted papers. Special thanks go to Marc Richetin, head of the CNRS Research Federation TIMS, which sponsored the workshop. August 2006 Adrien Bartoli Nassir Navab Vincent Lepeti

Activation of hypoxia-inducible factor signaling modulates the RNA protein interactome in Caenorhabditis elegans

Acute kidney injury (AKI) shows a rising incidence especially in the elderly above the age of 65. AKI does not only lead to an acute impairment of renal function but also comes with a strongly increased risk of adverse outcome including mortality. Since there are no therapies for AKI prevention measures this would be of utmost importance. However, clinically established specific interventions protecting the kidney are not available. Prevention of kidney damage can be addressed by the concept of preconditioning which exploits the fact that damaging stimuli at a sublethal dose can activate cellular protection programs that increase resistance to future stressors. One of these preconditioning protocols is based on the activation of the hypoxia signaling pathway which has been shown to prevent AKI in animal models. However, the underlying mechanisms are still unknown hampering translation to the clinical setting. A recent study highlighted the importance of RNA binding proteins (RBPs) and hinted towards differences in RNA-protein binding upon exposure to hypoxia. In the last decade, the list of known and putative RBPs has been increasing in size and complexity across species. Thanks to the development of techniques that allow crosslinking of RNA to interacting proteins followed by both RNA pulldown and mass spectrometry (RNA interactome capture). However, little is still known about the molecular function of many RBPs and their global dynamics in different conditions. In this study, we chose C. elegans as a model organism to further dissect the complex biological question of (1) how hypoxia-inducible factor signaling modulates the RNA protein interactome and (2) how these RBPs may impact on stress resistance. Performing RNA interactome capture in wild-type and vhl-1 mutant worms we identified 1354 RBPs 270 out of which had not been described before. Among these, we found 30 RBPs to be overrepresented in vhl-1 mutant and 50 RBPs in wild-type worms. A comparison of the proteome in both strains showed that all but one of these are not differentially regulated on the level of protein abundance pointing towards differences in RNA-binding capacity. Lifespan extension in the nematode reflects increased stress resistance. To enable screening of this phenotype after knockdown of RBP candidates in C. elegans, we established the automated lifespan machine. Using this approach, we could show longevity induced by knockdown several of these RBPs. Our results will significantly add to the understanding of the RBPome in the nematode and its modulation by HIF-signaling