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https://www.readbyqxmd.com/read/28409487/scanpath-modeling-and-classification-with-hidden-markov-models
#1
Antoine Coutrot, Janet H Hsiao, Antoni B Chan
How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, can be used to infer observer-related (e.g., task at hand) and stimuli-related (e.g., image semantic category) information. However, eye movements are complex signals and many of these studies rely on limited gaze descriptors and bespoke datasets. Here, we provide a turnkey method for scanpath modeling and classification...
April 13, 2017: Behavior Research Methods
https://www.readbyqxmd.com/read/28306515/decentralized-safety-concept-for-closed-loop-controlled-intensive-care
#2
Jan Kühn, Christian Brendle, André Stollenwerk, Martin Schweigler, Stefan Kowalewski, Thorsten Janisch, Rolf Rossaint, Steffen Leonhardt, Marian Walter, Rüdger Kopp
This paper presents a decentralized safety concept for networked intensive care setups, for which a decentralized network of sensors and actuators is realized by embedded microcontroller nodes. It is evaluated for up to eleven medical devices in a setup for automated acute respiratory distress syndrome (ARDS) therapy. In this contribution we highlight a blood pump supervision as exemplary safety measure, which allows a reliable bubble detection in an extracorporeal blood circulation. The approach is validated with data of animal experiments including 35 bubbles with a size between 0...
April 1, 2017: Biomedizinische Technik. Biomedical Engineering
https://www.readbyqxmd.com/read/28288333/metric-learning-for-parkinsonian-identification-from-imu-gait-measurements
#3
Fabio Cuzzolin, Michael Sapienza, Patrick Esser, Suman Saha, Miss Marloes Franssen, Johnny Collett, Helen Dawes
Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10m walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation...
February 27, 2017: Gait & Posture
https://www.readbyqxmd.com/read/28268618/decoding-speech-using-the-timing-of-neural-signal-modulation
#4
Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja
Brain-machine interfaces (BMIs) have great potential for applications that restore and assist communication for paralyzed individuals. Recently, BMIs decoding speech have gained considerable attention due to their potential for high information transfer rates. In this study, we propose a novel decoding approach based on hidden Markov models (HMMs) that uses the timing of neural signal changes to decode speech. We tested the decoder's performance by predicting vowels from electrocorticographic (ECoG) data of three human subjects...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268482/scoring-sequences-of-hippocampal-activity-using-hidden-markov-models
#5
Etienne Ackermann, Caleb Kemere
We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states - virtual place fields - in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence of states naturally lead to the development of a two component sequence score in which the sequential and contextual information are decoupled...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268307/using-respiratory-signals-for-the-recognition-of-human-activities
#6
Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov
Human activity recognition through wearable sensors is becoming integral to health monitoring and other applications. Typically, human activity is captured through signals from inertial sensors, while signals from other sensors have been utilized less frequently. In this study, we explored the feasibility of classifying human activities by analyzing the temporal information of respiratory signals through hidden Markov models (HMMs). Left-to-right HMMs were trained for five activities: sedentary, walking, eating, talking, and cigarette smoking...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28267626/bayesian-switching-factor-analysis-for-estimating-time-varying-functional-connectivity-in-fmri
#7
Jalil Taghia, Srikanth Ryali, Tianwen Chen, Kaustubh Supekar, Weidong Cai, Vinod Menon
There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000)...
March 3, 2017: NeuroImage
https://www.readbyqxmd.com/read/28226798/decoding-speech-using-the-timing-of-neural-signal-modulation
#8
Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang
Brain-machine interfaces (BMIs) have great potential for applications that restore and assist communication for paralyzed individuals. Recently, BMIs decoding speech have gained considerable attention due to their potential for high information transfer rates. In this study, we propose a novel decoding approach based on hidden Markov models (HMMs) that uses the timing of neural signal changes to decode speech. We tested the decoder's performance by predicting vowels from electrocorticographic (ECoG) data of three human subjects...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226656/scoring-sequences-of-hippocampal-activity-using-hidden-markov-models
#9
Etienne Ackermann, Caleb Kemere, Etienne Ackermann, Caleb Kemere, Etienne Ackermann, Caleb Kemere
We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states - virtual place fields - in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence of states naturally lead to the development of a two component sequence score in which the sequential and contextual information are decoupled...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226466/using-respiratory-signals-for-the-recognition-of-human-activities
#10
Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov, Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov, Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov
Human activity recognition through wearable sensors is becoming integral to health monitoring and other applications. Typically, human activity is captured through signals from inertial sensors, while signals from other sensors have been utilized less frequently. In this study, we explored the feasibility of classifying human activities by analyzing the temporal information of respiratory signals through hidden Markov models (HMMs). Left-to-right HMMs were trained for five activities: sedentary, walking, eating, talking, and cigarette smoking...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28224501/modeling-movement-primitives-with-hidden-markov-models-for-robotic-and-biomedical-applications
#11
Michelle Karg, Dana Kulić
Movement primitives are elementary motion units and can be combined sequentially or simultaneously to compose more complex movement sequences. A movement primitive timeseries consist of a sequence of motion phases. This progression through a set of motion phases can be modeled by Hidden Markov Models (HMMs). HMMs are stochastic processes that model time series data as the evolution of a hidden state variable through a discrete set of possible values, where each state value is associated with an observation (emission) probability...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224499/finding-rna-protein-interaction-sites-using-hmms
#12
Tao Wang, Jonghyun Yun, Yang Xie, Guanghua Xiao
RNA-binding proteins play important roles in the various stages of RNA maturation through binding to its target RNAs. Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Several Hidden Markov model-based (HMM) approaches have been suggested to identify protein-RNA binding sites from CLIP-Seq datasets. In this chapter, we describe how HMM can be applied to analyze CLIP-Seq datasets, including the bioinformatics preprocessing steps to extract count information from the sequencing data before HMM and the downstream analysis steps following peak-calling...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224498/differential-gene-expression-dex-and-alternative-splicing-events-ase-for-temporal-dynamic-processes-using-hmms-and-hierarchical-bayesian-modeling-approaches
#13
Sunghee Oh, Seongho Song
In gene expression profile, data analysis pipeline is categorized into four levels, major downstream tasks, i.e., (1) identification of differential expression; (2) clustering co-expression patterns; (3) classification of subtypes of samples; and (4) detection of genetic regulatory networks, are performed posterior to preprocessing procedure such as normalization techniques. To be more specific, temporal dynamic gene expression data has its inherent feature, namely, two neighboring time points (previous and current state) are highly correlated with each other, compared to static expression data which samples are assumed as independent individuals...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224496/computationally-tractable-multivariate-hmm-in-genome-wide-mapping-studies
#14
Hyungwon Choi, Debashis Ghosh, Zhaohui Qin
Hidden Markov model (HMM) is widely used for modeling spatially correlated genomic data (series data). In genomics, datasets of this kind are generated from genome-wide mapping studies through high-throughput methods such as chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq). When multiple regulatory protein binding sites or related epigenetic modifications are mapped simultaneously, the correlation between data series can be incorporated into the latent variable inference in a multivariate form of HMM, potentially increasing the statistical power of signal detection...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224495/hidden-markov-models-in-bioinformatics-snv-inference-from-next-generation-sequence
#15
Jiawen Bian, Xiaobo Zhou
The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224494/modelling-chip-seq-data-using-hmms
#16
Veronica Vinciotti
Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this chapter, we show how hidden Markov models can be used for the analysis of data generated by ChIP-seq experiments. We show how a hidden Markov model can naturally account for spatial dependencies in the ChIP-seq data, how it can be used in the presence of data from multiple ChIP-seq experiments under the same biological condition, and how it naturally accounts for the different IP efficiencies of individual ChIP-seq experiments...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224491/predicting-alpha-helical-transmembrane-proteins-using-hmms
#17
Georgios N Tsaousis, Margarita C Theodoropoulou, Stavros J Hamodrakas, Pantelis G Bagos
Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical transmembrane proteins utilizing different algorithmic techniques...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224490/predicting-beta-barrel-transmembrane-proteins-using-hmms
#18
Georgios N Tsaousis, Stavros J Hamodrakas, Pantelis G Bagos
Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224489/application-of-hidden-markov-models-in-biomolecular-simulations
#19
Saurabh Shukla, Zahra Shamsi, Alexander S Moffett, Balaji Selvam, Diwakar Shukla
Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224488/hmms-in-protein-fold-classification
#20
Christos Lampros, Costas Papaloukas, Themis Exarchos, Dimitrios I Fotiadis
The limitation of most HMMs is their inherent high dimensionality. Therefore we developed several variations of low complexity models that can be applied even to protein families with a few members. In this chapter we present these variations. All of them include the use of a hidden Markov model (HMM), with a small number of states (called reduced state-space HMM), which is trained with both amino acid sequence and secondary structure of proteins whose 3D structure is known and it is used for protein fold classification...
2017: Methods in Molecular Biology
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