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Phonocardiogram segmentation by using Hidden Markov Models

Lima, C. S.; Cardoso, Manuel J.
Fonte: ACTA Press Publicador: ACTA Press
Tipo: Conferência ou Objeto de Conferência
Publicado em 16/02/2007 Português
Relevância na Pesquisa
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This paper is concerned to the segmentation of heart sounds by using state of art Hidden Markov Models technology. Concerning to several heart pathologies the analysis of the intervals between the first and second heart sounds is of utmost importance. Such intervals are silent for a normal subject and the presence of murmurs indicate certain cardiovascular defects and diseases. While the first heart sound can easily be detected if the ECG is available, the second heart sound is much more difficult to be detected given the low amplitude and smoothness of the T-wave. In the scope of this segmentation difficulty the well known non-stationary statistical properties of Hidden Markov Models concerned to temporal signal segmentation capabilities can be adequate to deal with this kind of segmentation problems. The feature vectors are based on a MFCC based representation obtained from a spectral normalisation procedure, which showed better performance than the MFCC representation alone in an Isolated Speech Recognition framework. Experimental results were evaluated on data collected from five different subjects, using CardioLab system and a Dash family patient monitor. The ECG leads I, II and III and an electronic stethoscope signal were sampled at 977 samples per second.

Selective MMIE training of hidden Markov models for cardiac arrhythmia classification

Lima, C. S.; Cardoso, Manuel J.
Fonte: World Association for Chinese Biomedical Engineers Publicador: World Association for Chinese Biomedical Engineers
Tipo: Conferência ou Objeto de Conferência
Publicado em /07/2007 Português
Relevância na Pesquisa
98.67641%
Centre Algoritmi; This paper is concerned to the cardiac arrhythmia classification by using Hidden Markov Models. The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, and two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF) and atrial flutter (AFL). The approach followed in this paper is based on the supposition that atrial fibrillation, atrial flutter and normal beats are morphologically similar except that the former does not exhibit the P wave, while the later exhibits several P waves following the QRS. Regarding to the HMM modelling this can mean that these three classes can be modelled by HMM’s of similar topology and sharing some similar parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMI (Maximum Mutual Information) training, and saving computational resources at run-time decoding. This paper also shows that the similarities among normal, atrial fibrillation and atrial flutter beats, which main difference is the lack or repetitions of the P wave...

Automatic segmentation of the second cardiac sound by using wavelets and hidden Markov models

Lima, C. S.; Barbosa, Daniel
Fonte: IEEE-EMBS Publicador: IEEE-EMBS
Tipo: Conferência ou Objeto de Conferência
Publicado em /08/2008 Português
Relevância na Pesquisa
99.01221%
This paper is concerned with the segmentation of the second heart sound (S2) of the phonocardiogram (PCG), in its two acoustic events, aortic (A2) and pulmonary (P2) components. The aortic valve (A2) usually closes before the pulmonary valve (P2) and the delay between these two events is known as “split” and is typically less than 30 miliseconds. S2 splitting, reverse splitting or reverse occurrence of components A2 and P2 are the most important aspects regarding cardiac diagnosis carried out by the analysis of S2 cardiac sound. An automatic technique, based on discrete wavelet transform and hidden Markov models, is proposed in this paper to segment S2, to estimate de order of occurrence of A2 and P2 and finally to estimate the delay between these two components (split). A discrete density hidden Markov model (DDHMM) is used for phonocardiogram segmentation while embedded continuous density hidden Markov models are used for acoustic models, which allows segmenting S2. Experimental results were evaluated on data collected from five different subjects, using CardioLab system and a Dash family patient monitor. The ECG leads I, II and III and an electronic stethoscope signal were sampled at 977 samples per second.; Centre Algoritmi

Hidden Markov models applied to a subsequence of the Xylella fastidiosa genome

Silva,Cibele Q. da
Fonte: Sociedade Brasileira de Genética Publicador: Sociedade Brasileira de Genética
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2003 Português
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99.05918%
Dependencies in DNA sequences are frequently modeled using Markov models. However, Markov chains cannot account for heterogeneity that may be present in different regions of the same DNA sequence. Hidden Markov models are more realistic than Markov models since they allow for the identification of heterogeneous regions of a DNA sequence. In this study we present an application of hidden Markov models to a subsequence of the Xylella fastidiosa DNA data. We found that a three-state model provides a good description for the data considered.

Factorial Hidden Markov Models

Ghahramani, Zoubin; Jordan, Michael I.
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Formato: 7 p.; 198365 bytes; 244196 bytes; application/postscript; application/pdf
Português
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98.6623%
We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.

Interval-valued Hidden Markov models for recognizing personality traits in social exchanges in open multiagent systems

Dimuro, Gra??aliz Pereira; Costa, Ant??nio Carlos da Rocha; Gon??alves, Luciano Vargas; Hubner, Alexandre
Fonte: Universidade Federal do Rio Grande Publicador: Universidade Federal do Rio Grande
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
98.55367%
This paper presents an application of Interval-valued Hidden Markov Models to the modelling of agent personality traits in multiagent systems. The agents??? behaviors are modeled as probabilistic transitions functions, where interval-valued probabilities are used to express the uncertainty in determining those probabilities. The model of regulation of social exchanges is based on the concept of equilibrium supervisor, which is able to recommend the best exchanges for the agents to perform in order to achieve the equilibrium of the system.

Interval-Valued Hidden Markov Models for Recognizing Personality Traits in Social Exchanges in Open Multiagent Systems

Dimuro, Gra??aliz Pereira; Costa, Ant??nio Carlos da Rocha; Gon??alves, Luciano Vargas; Hubner, Alexandre
Fonte: Universidade Federal do Rio Grande Publicador: Universidade Federal do Rio Grande
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
98.55367%
This paper presents an application of Interval-valued Hidden Markov Models to the modelling of agent personality traits in multiagent systems. The agents??? behaviors are modeled as probabilistic transitions functions, where interval-valued probabilities are used to express the uncertainty in determining those probabilities. The model of regulation of social exchanges is based on the concept of equilibrium supervisor, which is able to recommend the best exchanges for the agents to perform in order to achieve the equilibrium of the system.

Alignment of time course microarray data with hidden Markov models.

Robinson, Sean
Fonte: Universidade de Adelaide Publicador: Universidade de Adelaide
Tipo: Tese de Doutorado
Publicado em //2012 Português
Relevância na Pesquisa
98.55367%
Time course microarray experiments allow for insight into biological processes by quantifying changes in gene expression over a time period of interest. This project is motivated by time course microarray data from an experiment conducted on grapevines over the development cycle of the grape berries at a number of different vineyards in South Australia. Although the under- lying biological process is the same at each vineyard, there are differences in the timing of the development cycle at different vineyards due to local conditions. The aim of this project is to construct a methodology to align the data from different vineyards in order to obtain a common representation of the gene expression over the development cycle of the grape berries for each gene. Hidden Markov models (HMMs) have been used to model time series data in a number of domains and have also been used to model time course microarray data. We review these applications in addition to the use of HMMs for particular alignment problems in genomic sequence data. We present an extension of HMMs and propose a novel alignment methodology based on this extension. We evaluate the proposed alignment methodology by applying it to simulated data prior to using it to align the grapevine data.; Thesis (M.Phil.) -- University of Adelaide...

Wavelet-Based Denoising Using Hidden Markov Models

Borran, Mohammad Jaber; Nowak, Robert David; Borran, Mohammad Jaber; Nowak, Robert David
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Conference paper
Português
Relevância na Pesquisa
98.77038%
Conference Paper; Hidden Markov models have been used in a wide variety of wavelet-based statistical signal processing applications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients and the correlation between the magnitudes of the wavelet coefficients within each scale and/or across the scales is captured by a Markov tree imposed on the (hidden) states of the mixture. This paper investigates correlations directly among the wavelet coefficient amplitudes (sign à magnitude), instead of magnitudes alone. Our theoretical analysis shows that the coefficients display significant correlations in sign as well as magnitude, especially near strong edges. We propose a new wavelet-based HMM structure based on mixtures of one-sided exponential densities that exploits both sign and magnitude correlations. We also investigate the application of this for denoising the signals corrupted by additive white Gaussian noise. Using some examples with standard test signals, we show that our new method can achieve better mean squared error, and the resulting denoised signals are generally much smoother.

Contextual Hidden Markov Models for Wavelet-domain Signal Processing

Crouse, Matthew; Baraniuk, Richard G.; Crouse, Matthew; Baraniuk, Richard G.
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Conference paper
Português
Relevância na Pesquisa
98.55367%
Conference Paper; Wavelet-domain hidden Markov models (HMMs) provide a powerful new approach for statistical modeling and processing of wavelet coefficients. In addition to characterizing the statistics of individual wavelet coefficients, HMMs capture some of the key interactions between wavelet coefficients. However, as HMMs model an increasing number of wavelet coefficient interactions, HMM-based signal processing becomes increasingly complicated. In this paper, we propose a new approach to HMMs based on the notion of context. By modeling wavelet coefficient inter-dependencies via contexts, we retain the approximation capabilities of HMMs, yet substantially reduce their complexity. To illustrate the power of this approach, we develop new algorithms for signal estimation and for efficient synthesis of nonGaussian, long-range-dependent network traffic.

Wavelet -Based Statistical Signal Processing using Hidden Markov Models

Crouse, Matthew; Nowak, Robert David; Baraniuk, Richard G.; Crouse, Matthew; Nowak, Robert David; Baraniuk, Richard G.
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
98.61032%
Journal Paper; Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the statistical dependencies and non-Gaussian Statistics encountered with real-world signals. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable probabilistic signal modes. Efficient Expectation Maximization algorithms are developed for fitting the HMMs to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMMs, we develop novel algorithms for signal denoising, classificaion, and detection.

Bayesian Tree-Structured Image Modeling using Wavelet-domain Hidden Markov Models

Romberg, Justin; Choi, Hyeokho; Baraniuk, Richard G.; Romberg, Justin; Choi, Hyeokho; Baraniuk, Richard G.
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
98.82703%
Journal Paper; Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training to fit an HMT model to a given data set (using the Expectation-Maximization algorithm, for example). In this paper, we greatly simplify the HMT model by exploiting the inherent self-similarity of real-world images. This simplified model specifies the HMT parameters with just nine meta-parameters (independent of the size of the image and the number of wavelet scales). We also introduce a Bayesian universal HMT (uHMT) that fixes these nine parameters. The uHMT requires no training of any kind. While extremely simple, we show using a series of image estimation/denoising experiments that these new models retain nearly all of the key image structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms other wavelet-based estimators in the current literature, both visually and in mean-square error.

Recognizing human activities from sensors using hidden Markov models constructed by feature selection techniques

Cilla, Rodrigo; Patricio Guisado, Miguel Ángel; García, Jesús; Berlanga, Antonio; Molina, José M.
Fonte: MDPI Publishing Publicador: MDPI Publishing
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em /02/2009 Português
Relevância na Pesquisa
98.77038%
In this paper a method for selecting features for Human Activity Recognition from sensors is presented. Using a large feature set that contains features that may describe the activities to recognize, Best First Search and Genetic Algorithms are employed to select the feature subset that maximizes the accuracy of a Hidden Markov Model generated from the subset. A comparative of the proposed techniques is presented to demonstrate their performance building Hidden Markov Models to classify different human activities using video sensors.; This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB and CAM MADRINET S-0505/TIC/0255.; 19 pages, 8 figures.-- This article belongs to the Special Issue "Sensor Algorithms".

Predicting market direction with hidden Markov models

Silva, Artur Pedro Antunes da
Fonte: Universidade Nova de Lisboa Publicador: Universidade Nova de Lisboa
Tipo: Dissertação de Mestrado
Publicado em /01/2015 Português
Relevância na Pesquisa
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This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.; UNL - NSBE

Cardiac arrhythmia detection by parameters sharing and MMIE training of hidden Markov models

Lima, C. S.; Cardoso, Manuel J.
Fonte: IEEE-EMBS Publicador: IEEE-EMBS
Tipo: Conferência ou Objeto de Conferência
Publicado em /08/2007 Português
Relevância na Pesquisa
98.8654%
This paper is concerned to the cardiac arrhythmia classification by using hidden Markov models and maximum mutual information estimation (MMIE) theory. The types of beat being selected are normal (N), premature ventricular contraction (V), and the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF). The approach followed in this paper is based on the supposition that atrial fibrillation and normal beats are morphologically similar except that the former does not exhibit the P wave. In fact there are more differences as the irregularity of the RR interval, but ventricular conduction in AF is normal in morphology. Regarding to the Hidden Markov Models (HMM) modelling this can mean that these two classes can be modelled by HMM's of similar topology and sharing some parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMIE training, and saving computational resources at run-time decoding. The algorithm performance was tested by using the MIT-BIH database. Better performance was obtained comparatively to the case where Maximum Likelihood Estimation training is used alone.; Centre Algoritmi

Asymptotic Smoothing errors for Hidden Markov Models

Shue, L; Anderson, Brian; De Bruyne, Franky
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
98.90024%
In this paper, the asymptotic smoothing error for hidden Markov models (HMMs) is investigated using hypothesis testing ideas. A family of HMMs is studied parametrised by a positive constant e, which is a measure of the frequency of change. Thus, when e ->

Face recognition using Hidden Markov Models

Samaria, Ferdinando Silvestro
Fonte: University of Cambridge; Department of Engineering; Trinity College Publicador: University of Cambridge; Department of Engineering; Trinity College
Tipo: Thesis; doctoral; PhD
Português
Relevância na Pesquisa
98.64869%
This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.

Word hypothesis of phonetic strings using hidden Markov models

Engbrecht, Jeffery W.
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
98.77038%
This thesis investigates a stochastic modeling approach to word hypothesis of phonetic strings for a speaker independent, large vocabulary, continuous speech recognition system. The stochastic modeling technique used is Hidden Markov Modeling. Hidden Markov Models (HMM) are probabilistic modeling tools most often used to analyze complex systems. This thesis is part of a speaker independent, large vocabulary, continuous speech understanding system under development at the Rochester Institute of Technology Research Corporation. The system is primarily data-driven and is void of complex control structures such as the blackboard approach used in many expert systems. The software modules used to implement the HMM were created in COMMON LISP on a Texas Instruments Explorer II workstation. The HMM was initially tested on a digit lexicon and then scaled up to a U.S. Air Force cockpit lexicon. A sensitivity analysis was conducted using varying error rates. The results are discussed and a comparison with Dynamic Time Warping results is made.

Lumpable Hidden Markov Models - Model Reduction and Reduced Complexity Filtering

White, Langford; Mahony, Robert; Brushe, Gary
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
99.03146%
This paper is concerned with filtering of hidden Markov processes (HMPs) which possess (or approximately possess) the property of lumpability. This property is a generalization of the property of lumpability of a Markov chain which has been previously addressed by others. In essence, the property of lumpability means that there is a partition of the (atomic) states of the Markov chain into aggregated sets which act in a similar manner as far as the state dynamics and observation statistics are concerned. We prove necessary and sufficient conditions on the HMP for exact lumpability to hold. For a particular class of hidden Markov models (HMMs), namely finite output alphabet models, conditions for lumpability of all HMPs representable by a specified HMM are given. The corresponding optimal filter algorithms for the aggregated states are then derived. The paper also describes an approach to efficient suboptimal filtering for HMPs which are approximately lumpable. By this we mean that the HMM generating the process may be approximated by a lumpable HMM. This approach involves directly finding a lumped HMM which approximates the original HMM well, in a matrix norm sense. An alternative approach for model reduction based on approximating a given HMM by an exactly lumpable HMM is also derived. This method is based on the alternating convex projections algorithm. Some simulation examples are presented which illustrate the performance of the suboptimal filtering algorithms.

Fast Convergence Identification of Hidden Markov Models using Risk-Sensitive Filters

Thorne, Jeremy; Moore, John
Fonte: Pergamon-Elsevier Ltd Publicador: Pergamon-Elsevier Ltd
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
98.93861%
In this paper we derive recursive risk-sensitive filters which may be used for both on-line and off-line identification of hidden Markov models (HMMs). The identification is achieved by first taking risk-sensitive conditional mean estimates of the number