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Pair-wise clustering of large scale Granger causality index matrices for revealing communities

Axel Wismüller, Mahesh B. Nagarajan, Herbert Witte, Britta Pester, Lutz Leistritz, Robert C. Molthen, John B. Weaver
2014 Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging  
The analysis of large ensembles of time series is a fundamental challenge in different domains of biomedical image processing applications, specifically in the area of functional MRI data processing.  ...  An important aspect of such analysis is the ability to reconstruct community network structures based on interactive behavior between different nodes of the network which are captured in such time series  ...  Martina Hasenjaeger at University of Bielefeld, Germany for providing the implementation of the TMP clustering algorithm.  ... 
doi:10.1117/12.2044340 pmid:29170584 pmcid:PMC5697795 fatcat:owgfneynf5h7raug5lhhpspzza

Bag-of-words representation for biomedical time series classification

Jin Wang, Ping Liu, Mary F.H. She, Saeid Nahavandi, Abbas Kouzani
2013 Biomedical Signal Processing and Control  
Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its  ...  The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series.  ...  Similar to the codebook construction in image and video analysis, we cluster all the local segments from training time series using k-means clustering to construct the codebook.  ... 
doi:10.1016/j.bspc.2013.06.004 fatcat:gzk5vace2fhkbga62c5cgdirga

CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

Li Chen, Tsung-Han Chan, Peter L. Choyke, Elizabeth M. C. Hillman, Chong−Yung Chi, Zaver M. Bhujwalla, Ge Wang, Sean S. Wang, Zsolt Szabo, Yue Wang
2011 Computer applications in the biosciences : CABIOS  
CAM-CM (Convex Analysis of Mixtures -Compartment Modeling) signal deconvolution tool has been developed to automatically identify pure-volume pixels located at the corners of the clustered pixel time series  ...  CAM-CM can dissect complex tissues into regions with differential tracer kinetics at pixel-wise resolution and provide a systems biology tool for defining imaging signatures predictive of phenotypes.  ...  ACKNOWLEDGEMENTS This work is supported in part by the National Institutes of Health, under Grants EB000830, EB008627 and HHSN261200800001E.  ... 
doi:10.1093/bioinformatics/btr436 pmid:21785131 pmcid:PMC3167053 fatcat:6qzntlambfgdjjmec3xuv6daxm

Investigating the use of mutual information and non-metric clustering for functional connectivity analysis on resting-state functional MRI

Xixi Wang, Mahesh B. Nagarajan, Anas Z. Abidin, Adora DSouza, Susan K. Hobbs, Axel Wismüller, Barjor Gimi, Robert C. Molthen
2015 Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging  
For this purpose, pairwise mutual information (MI) was extracted from all pixel time series within the brain on restingstate fMRI data.  ...  Keywords Resting-state functional MRI; functional connectivity analysis; mutual information; non-metric clustering; topographic mapping of proximity X.  ...  This work is embedded in our group's endeavor to expedite 'big data' analysis in biomedical imaging by means of advanced pattern recognition and machine learning methods for computational radiology, e.g  ... 
doi:10.1117/12.2082565 pmid:29200591 pmcid:PMC5704732 dblp:conf/mibam/WangNADHW15 fatcat:wb2uoa73sba2jmracvnv5nly5m

Applications of functional data analysis: A systematic review

Shahid Ullah, Caroline F Finch
2013 BMC Medical Research Methodology  
Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data.  ...  public health and biomedical problems.  ...  Acknowledgements The study was funded (at least in part) through the Early Career Researcher development funding program at the University of Ballarat. Professor  ... 
doi:10.1186/1471-2288-13-43 pmid:23510439 pmcid:PMC3626842 fatcat:vd26vv3aijejnobppau7gmdl6y

Biomedical Signal Processing and Modeling Complexity of Living Systems 2013

Carlo Cattani, Radu Badea, Sheng-Yong Chen, Maria Crisan
2013 Computational and Mathematical Methods in Medicine  
The focus of this special issue is the mathematical analysis and modeling of time series in living systems and biomedical signals.  ...  pathologies. (3) Physiological signals usually come as 1D time series or 2D images.  ... 
doi:10.1155/2013/173469 fatcat:w67ec2bppvh33hfgawkvyeu6ki

1982 Index - IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-4

1982 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Hull, Jonathan J., + , T-PAMI Sep 82520-530 Time series graphical considerations in time series analysis. Kedem, Benjamin, T-PAMISep 82 493499 Time series; cf.  ...  Levine, Barry, T-PAMIJan 82 25-34 Autoregressive moving-average processes ARMA modeling of time series. Cadzow, James A., T-PAMI Mar 82 124-128 graphical considerations in time series analysis.  ... 
doi:10.1109/tpami.1982.4767328 fatcat:22qk4hl7drdafkirhz5ow6l27q

Large-scale Granger causality analysis on resting-state functional MRI

Adora M. D'Souza, Anas Zainul Abidin, Lutz Leistritz, Axel Wismüller, Barjor Gimi, Andrzej Krol
2016 Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging  
By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at  ...  This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system  ...  This work is embedded in our group's endeavor to expedite 'big data' analysis in biomedical imaging by means of advanced machine learning and pattern recognition methods for computational radiology and  ... 
doi:10.1117/12.2217264 pmid:29170585 pmcid:PMC5697152 dblp:conf/mibam/DSouzaALW16 fatcat:oe5u6twzhverbckgxkkrr6yyw4

Nonlinear functional connectivity network recovery in the human brain with mutual connectivity analysis (MCA): convergent cross-mapping and non-metric clustering

Axel Wismüller, Anas Z. Abidin, Adora M. D'Souza, Xixi Wang, Susan K. Hobbs, Lutz Leistritz, Mahesh B. Nagarajan, Barjor Gimi, Robert C. Molthen
2015 Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging  
Termed mutual connectivity analysis (MCA), this framework involves two steps, the first of which is to evaluate the pair-wise cross-prediction performance between fMRI pixel time series within the brain  ...  However, our results on whole-slice fMRI analysis demonstrate that MCA-based model-free recovery of regions associated with the primary motor cortex and supplementary motor area are in close agreement  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.  ... 
doi:10.1117/12.2082124 pmid:29367796 pmcid:PMC5777339 dblp:conf/mibam/WismullerADWHLN15 fatcat:zowmbw7o5rdlpbsiq2lfvzfdjy

Biomedical Signal Processing and Modeling Complexity of Living Systems

Carlo Cattani, Radu Badea, Shengyong Chen, Maria Crisan
2012 Computational and Mathematical Methods in Medicine  
The focus of this special issue is the mathematical analysis and modeling of time series in living systems and biomedical signals.  ...  Biomedical signals extract information from the complex phenomena being measured, which are typically a time series having both a regular and random components.  ...  The focus of this special issue is the mathematical analysis and modeling of time series in living systems and biomedical signals.  ... 
doi:10.1155/2012/298634 pmid:23304236 pmcid:PMC3529883 fatcat:alkf4bk33jemblj2ggxgw3quua

A method to determine regional mechanical left ventricular dyssynchrony based on high temporal resolution short axis SSFP cine images

Jonathan Suever, Brandon K Fornwalt, Michael Lloyd, John N Oshinski
2012 Journal of Cardiovascular Magnetic Resonance  
A time-series of radial motion curves relative to the center of mass was generated for each radial location and over each slice.  ...  This analysis was repeated for every point throughout the LV and time shift values were projected onto the standard AHA 17-segment model to create a mechanical dyssynchrony map ( Figure 1C ).  ... 
doi:10.1186/1532-429x-14-s1-w18 pmcid:PMC3305707 fatcat:xgdm4uijqrcubm4n4w4lotnbiq

Nonlinear Projective Techniques To Extract Artifacts In Biomedical Signals

Elmar Wolfgang Lang, Kurt Stadlthanner, Ana Teixeira, Ana Maria Tom
2006 Zenodo  
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006  ...  But many biomedical signals represent one dimensional time series.  ...  Clearly projective subspace techniques are not available for one dimensional time series to suppress noise contributions, hence time series analysis techniques often rely on embedding a one dimensional  ... 
doi:10.5281/zenodo.53491 fatcat:w5nlgv4ucfbhhiyfxb35bksyjm

LEVEL SET BASED CLUSTERING FOR ANALYSIS OF FUNCTIONAL MRI DATA

D.R. Bathula, X. Papademetris, J.S. Duncan
2007 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
The correlation coefficient is used to quantify similarity in time series of adjacent voxels.  ...  We present a level set based clustering technique to detect activation regions from functional brain images using contextual information.  ...  Box-car time series was designed for the active voxels and the inactive voxels had time series of zero amplitude.  ... 
doi:10.1109/isbi.2007.356877 pmid:20216927 pmcid:PMC2834251 dblp:conf/isbi/BathulaPD07 fatcat:tmkzxuyifbgmhmhukhl2al7c24

Investigating changes in brain network properties in HIV-associated neurocognitive disease (HAND) using mutual connectivity analysis (MCA)

Anas Zainul Abidin, Adora M. D'Souza, Mahesh B. Nagarajan, Axel Wismüller, Barjor Gimi, Andrzej Krol
2016 Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging  
Each node is represented by the average time series of the voxels of that region.  ...  We test this hypothesis by capturing the functional interdependence of 90 brain network nodes using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.  ... 
doi:10.1117/12.2217317 pmid:29170586 pmcid:PMC5697155 dblp:conf/mibam/AbidinDNW16a fatcat:5vjz3qgt6ncubbuzywydwkdpu4

Investigating the spatial and temporal interactions in resting-state fMRI with total activation

F. Isik Karahanoglu, Dimitri Van De Ville
2014 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)  
Resting-state functional magnetic resonance imaging (fMRI) has become an important tool to study the spontaneous brain fluctuations.  ...  Especially, in terms of network analysis, the intrinsic brain activations have been shown to exhibit some characteristic spatial patterns referred to as resting-state networks.  ...  by EPFL BMI-HU collaboration grant, Center for Biomedical Imaging (CIBM) of the Geneva-Lausanne Universities, and National Center of Competence in Research.  ... 
doi:10.1109/isbi.2014.6867939 dblp:conf/isbi/KarahanogluV14 fatcat:xy5kpb3amzfftnkyqjucglv2te
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