CVRG Analytical Services

As an NHLBI resource, the CVRG is committed to the delivery of tools, deployed as analytic services on the grid, for analyzing cardiovascular data. Three different sets of tools are under development or have been deployed. These are: 1) an ECG data analytic service; 2) heart shape and motion analysis services; and 3) multi-scale biomarker discovery services.

ECG Data Analytic Service

The CVRG Portal provides an interface by which users may upload and analyze ECG data. This is known as the ECG Portlet. Two data formats are currently accepted. The first is the proprietary data format (RDT) used by Norav Instruments Inc. The second is the WaveForm DataBase (WFDB) format used in the Physionet Project. Files may be uploaded individually or in batches. Two ECG data analysis algorithms are available. The first is a suite of algorithms developed by Dr. Ron Berger at Johns Hopkins University. [See Berger, R.D., Kasper, E.K., Baughman K.L., Marban E., Calkins H., Tomaselli G.F. (1997) Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. Circulation. 96(5):1557-1565 and Berger, R.D. (2003). QT Variability. J. Electrocardiol. 36: 83-87].

The second is a semi-automated algorithm developed as part of the Physionet competition by Chesnokov et al, which is described at the Physionet website. Data may be passed into these algorithms, the results are retrieved by the portlet, a subset of analysis results is displayed, and users may download all numeric results and import them into an Excel spreadsheet.

More detailed information can be found on the CVRG Wiki ECG Analytical Services Overview.

SWIFT system to accelerate ECG data processing:

Swift is a system for the rapid and reliable specification, execution, and management of large-scale science and engineering workflows. The CVRG team is integrating SWIFT into the ECG analytical services developed, to decrease the processing time of the algorithms.

Heart Shape and Motion Analysis Services:

The Large Deformation Diffeomorphic Metric Mapping (LDDMM) algorithm is the most widely used algorithm for analyzing anatomic shape and it’s variation [see Beg, M.F., Miller, M.I., Trouve, A., Younes, L. (2005). Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision 61, 139-157.]. It has been used extensively in brain research to discover shape-based biomarkers that are signatures of brain disease. We have recently used the LDDMM algorithm to study shape changes, assessed using MR and CT imagery collected from ex-vivo and in-vivo hearts, that are signatures of heart disease collected from ex-vivo and in-vivo hearts. [See Beg, M. F., Helm, P. A., McVeigh, E., Miller, M. I. and Winslow, R. L. (2004). Computational Cardiac Anatomy Using MRI. Magn. Reson. Med., 52(5): 1167 and Helm, P. et al (2006). Circ. Res. 98(1): 125-132].

Users may access the LDDMM algorithm using MRI Studio. MRI Studio is a Windows application that supports image volume visualization, diffusion tensor calculations, fiber tracking and editing, region of interest selection, and diffeomorphic mapping using the LDDMM algorithm. Computations may be performed using CVRG compute resources. Interested users should contact the CVRG team at cvrgbeta@gmail.com.

Visit the CVRG Wiki for more information.

Multi-Scale Biomarker Discovery Services:

Translational CV research more often than not involves the collection and analysis of what we refer to as multi-scale data sets (SNP, mRNA expression, protein expression, imaging, ECG, clinical, etc) for each patient in large cohorts. A challenge is to analyze such multi-scale data sets to discover biomarkers that are predictive of disease risk and treatment. Algorithms for biomarker discovery are available through the Biomarker discovery portlet. Algorithms include the K-TSP family of classifiers, known to be useful when the number of analysis features is large relative to the number of datasets. [See Geman D, d'Avignon C, Naiman DQ, Winslow RL. (2004) Classifying gene expression profiles from pairwise mRNA comparisons. Stat. Appl. Genet. Mol. Biol., 3(1): Article 19 and Xu L, Geman D, Winslow RL. (2007) Large-scale integration of cancer microarray data identifies a robust common cancer signature. BMC Bioinformatics. 8:275.].

Visit the CVRG Wiki for more information.