scholarly journals Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations

2019 ◽  
Vol 28 (2) ◽  
pp. 761-777 ◽  
Author(s):  
Berna Haktanirlar Ulutas ◽  
N. Fırat Özkan ◽  
Rafał Michalski
Keyword(s):  
Data Analysis ◽  
Eye Tracking ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Visual Quality ◽  
Quality Inspection ◽  
Tracking Data

scholarly journals Intelligent Data Analysis to Model and Understand Live Cell Time-lapse Sequences

2012 ◽  
Vol 51 (04) ◽  
pp. 332-340 ◽  
Author(s):  
A. Paterson ◽  
M. Ashtari ◽  
D. Ribé ◽  
G. Stenbeck ◽  
A. Tucker
Keyword(s):  
Data Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Lapse ◽  
Living Cells ◽  
Live Cell ◽  
Intelligent Data Analysis ◽  
Stable States ◽  
Experimental Conditions

SummaryBackground: One important aspect of cellular function, which is at the basis of tissue homeostasis, is the delivery of proteins to their correct destinations. Significant advances in live cell microscopy have allowed tracking of these pathways by following the dynamics of fluorescently labelled proteins in living cells.Objectives: This paper explores intelligent data analysis techniques to model the dynamic behavior of proteins in living cells as well as to classify different experimental conditions.Methods: We use a combination of decision tree classification and hidden Markov models. In particular, we introduce a novel approach to “align” hidden Markov models so that hidden states from different models can be cross-compared.Results: Our models capture the dynamics of two experimental conditions accurately with a stable hidden state for control data and multiple (less stable) states for the experimental data recapitulating the behaviour of particle trajectories within live cell time-lapse data.Conclusions: In addition to having successfully developed an automated framework for the classification of protein transport dynamics from live cell time-lapse data our model allows us to understand the dynamics of a complex trafficking pathway in living cells in culture.

scholarly journals Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

2021 ◽  
Author(s):  
Janet H. Hsiao ◽  
Hui Lan ◽  
Yueyuan Zheng ◽  
Antoni B. Chan
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov ◽  
Movement Patterns ◽  
Movement Analysis ◽  
Background Information ◽  
Eye Movement Analysis

AbstractThe eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

scholarly journals Big Data Analysis of Electronic Health Records: Clinically interpretable representations of older adult inpatient trajectories using time-series numerical data and Hidden Markov Models

2021 ◽  
Author(s):  
Maria Herrero-Zazo ◽  
Victoria L Keevil ◽  
Vince Taylor ◽  
Helen Street ◽  
Afzal N Chaudhry ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Electronic Health Records ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Big Data Analysis ◽  
Health Records ◽  
State Spaces

The implementation of Electronic Health Records (EHR) in UK hospitals provides new opportunities for clinical 'big data' analysis. The representation of observations routinely recorded in clinical practice is the first step to use these data in several research tasks. Anonymised data were extracted from 11 158 first emergency admission episodes (AE) in older adults. Irregular records from 23 laboratory blood tests and vital signs were normalized and regularised into daily bins and represented as numerical multivariate time-series (MVTS). Unsupervised Hidden Markov Models (HMM) were trained to represent each day of each AE as one of 17 state spaces. The visual clinical interpretation of these states showed remarkable differences between patients who died at the end of the AE and those who were discharged. All states had marked features that allowed their clinical interpretation and differentiation between those associated with the patients' disease burden, their physiological response to this burden or the stage of admission. The most evident relationships with hold-out clinical information were also confirmed by Chi-square tests, with two states strongly associated with inpatient mortality (IM) and 12 states (71%) associated with at least one admission diagnosis. The potential of these data representations on prediction of hospital outcomes was also explored using Logistic Regression (LR) and Random Forest (RF) models, with higher prediction performance observed when models were trained with MVTS data compared to HMM state spaces. However, the outputs of generative and discriminative analyses were complementary. For example, highest ranking features of the best performing RF model for IM (ROC-AUC 0.851) resembled the laboratory blood test and vital sign variables characterising the 'Early Inflammatory Response-like' state, itself strongly associated with IM. These results provide evidence of the capability of generative models to extract biological signals from routinely collected clinical data and their potential to represent interpretable patients' trajectories for future research in hypothesis generation or prediction modelling.

scholarly journals Modern Computational Techniques for the HMMER Sequence Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiandong Meng ◽  
Yanqing Ji
Keyword(s):  
Data Analysis ◽  
Sequence Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Hardware Acceleration ◽  
Performance Comparison ◽  
Computational Techniques ◽  
Software And Hardware ◽  
Computing Platforms

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

Sign in / Sign up

Export Citation Format

Share Document