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Showing 1 to 12 of 89 entries
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The Consortium for Electrocardiographic Imaging.

Computing in cardiology

Coll-Font J, Dhamala J, Potyagaylo D, Schulze WH, Tate JD, Guillem MS, van Dam P, Dossel O, Brooks DH, Macleod RS.
PMID: 28451592
Comput Cardiol (2010). 2016 Sep;43:325-328. Epub 2017 Mar 02.

Electrocardiographic imaging (ECGI) has recently gained attention as a viable diagnostic tool for reconstructing cardiac electrical activity in normal hearts as well as in cardiac arrhythmias. However, progress has been limited by the lack of both standards and unbiased...

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Coll-Font J, Dhamala J, Potyagaylo D, et al. The Consortium for Electrocardiographic Imaging. Comput Cardiol (2010). 2017;43:325-328
Coll-Font, J., Dhamala, J., Potyagaylo, D., Schulze, W. H., Tate, J. D., Guillem, M. S., van Dam, P., Dossel, O., Brooks, D. H., & Macleod, R. S. (2016). The Consortium for Electrocardiographic Imaging. Computing in cardiology, 43325-328.
Coll-Font, Jaume, et al. "The Consortium for Electrocardiographic Imaging." Computing in cardiology vol. 43 (2016): 325-328.
Coll-Font J, Dhamala J, Potyagaylo D, Schulze WH, Tate JD, Guillem MS, van Dam P, Dossel O, Brooks DH, Macleod RS. The Consortium for Electrocardiographic Imaging. Comput Cardiol (2010). 2016 Sep;43:325-328. Epub 2017 Mar 02. PMID: 28451592; PMCID: PMC5404701.
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LightWAVE: Waveform and Annotation Viewing and Editing in a Web Browser.

Computing in cardiology

Moody GB.
PMID: 26525640
Comput Cardiol (2010). 2013 Sep;40:17-20.

This paper describes LightWAVE, recently-developed open-source software for viewing ECGs and other physiologic waveforms and associated annotations (event markers). It supports efficient interactive creation and modification of annotations, capabilities that are essential for building new collections of physiologic signals...

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Moody GB. LightWAVE: Waveform and Annotation Viewing and Editing in a Web Browser. Comput Cardiol (2010). 2013;40:17-20
Moody, G. B. (2013). LightWAVE: Waveform and Annotation Viewing and Editing in a Web Browser. Computing in cardiology, 4017-20.
Moody, George B. "LightWAVE: Waveform and Annotation Viewing and Editing in a Web Browser." Computing in cardiology vol. 40 (2013): 17-20.
Moody GB. LightWAVE: Waveform and Annotation Viewing and Editing in a Web Browser. Comput Cardiol (2010). 2013 Sep;40:17-20. PMID: 26525640; PMCID: PMC4627786.
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A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series.

Computing in cardiology

Lee J, Mark R.
PMID: 21874161
Comput Cardiol (2010). 2011 Mar 22;2010(26):81-84.

In the intensive care unit (ICU), prompt therapeutic intervention to hypotensive episodes (HEs) is a critical task. Advance alerts that can prospectively identify patients at risk of developing an HE in the next few hours would be of considerable...

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Lee J, Mark R. A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series. Comput Cardiol (2010). 2011;2010(26):81-84
Lee, J., & Mark, R. (2011). A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series. Computing in cardiology, 2010(26), 81-84.
Lee, J, and Mark, Rg. "A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series." Computing in cardiology vol. 2010,26 (2011): 81-84.
Lee J, Mark R. A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series. Comput Cardiol (2010). 2011 Mar 22;2010(26):81-84. PMID: 21874161; PMCID: PMC3162312.
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A Comparison of IIR and Wavelet Filtering for Noise Reduction of the ECG.

Computing in cardiology

Sørensen J, Johannesen L, Grove U, Lundhus K, Couderc JP, Graff C.
PMID: 22068831
Comput Cardiol (2010). 2010 Sep 26;37:489-492.

This study compares the ability to preserve information and reduce noise contaminants on the ECG for five wavelet filters and three IIR filters. Two 3-lead Holter ECGs were used. White Gaussian Noise was added to the first ECG in...

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Sørensen J, Johannesen L, Grove U, et al. A Comparison of IIR and Wavelet Filtering for Noise Reduction of the ECG. Comput Cardiol (2010). 2010;37:489-492
Sørensen, J., Johannesen, L., Grove, U., Lundhus, K., Couderc, J. P., & Graff, C. (2010). A Comparison of IIR and Wavelet Filtering for Noise Reduction of the ECG. Computing in cardiology, 37489-492.
Sørensen, Js, et al. "A Comparison of IIR and Wavelet Filtering for Noise Reduction of the ECG." Computing in cardiology vol. 37 (2010): 489-492.
Sørensen J, Johannesen L, Grove U, Lundhus K, Couderc JP, Graff C. A Comparison of IIR and Wavelet Filtering for Noise Reduction of the ECG. Comput Cardiol (2010). 2010 Sep 26;37:489-492. PMID: 22068831; PMCID: PMC3134259.
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Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012.

Computing in cardiology

Silva I, Moody G, Scott DJ, Celi LA, Mark RG.
PMID: 24678516
Comput Cardiol (2010). 2012;39:245-248.

Acuity scores, such as APACHE, SAPS, MPM, and SOFA, are widely used to account for population differences in studies aiming to compare how medications, care guidelines, surgery, and other interventions impact mortality in Intensive Care Unit (ICU) patients. By...

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Silva I, Moody G, Scott DJ, et al. Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012. Comput Cardiol (2010). 2012;39:245-248
Silva, I., Moody, G., Scott, D. J., Celi, L. A., & Mark, R. G. (2012). Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012. Computing in cardiology, 39245-248.
Silva, Ikaro, et al. "Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012." Computing in cardiology vol. 39 (2012): 245-248.
Silva I, Moody G, Scott DJ, Celi LA, Mark RG. Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012. Comput Cardiol (2010). 2012;39:245-248. PMID: 24678516; PMCID: PMC3965265.
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AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017.

Computing in cardiology

Clifford GD, Liu C, Moody B, Lehman LH, Silva I, Li Q, Johnson AE, Mark RG.
PMID: 29862307
Comput Cardiol (2010). 2017 Sep;44. doi: 10.22489/CinC.2017.065-469. Epub 2018 Apr 05.

The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings performed by patients. A total of 12,186 ECGs were used: 8,528 in the public...

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Clifford GD, Liu C, Moody B, et al. AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017. Comput Cardiol (2010). 2018;44doi: 10.22489/CinC.2017.065-469.
Clifford, G. D., Liu, C., Moody, B., Lehman, L. H., Silva, I., Li, Q., Johnson, A. E., & Mark, R. G. (2017). AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017. Computing in cardiology, 44. https://doi.org/10.22489/CinC.2017.065-469
Clifford, Gari D, et al. "AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017." Computing in cardiology vol. 44 (2017). doi: https://doi.org/10.22489/CinC.2017.065-469
Clifford GD, Liu C, Moody B, Lehman LH, Silva I, Li Q, Johnson AE, Mark RG. AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017. Comput Cardiol (2010). 2017 Sep;44. doi: 10.22489/CinC.2017.065-469. Epub 2018 Apr 05. PMID: 29862307; PMCID: PMC5978770.
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QRS Loop Folding Phenomenon in Vectorcardiogram of Healthy Individuals.

Computing in cardiology

Sedaghat G, Kabir MM, Tereshchenko LG.
PMID: 28944247
Comput Cardiol (2010). 2016 Sep;43:645-648. Epub 2017 Mar 02.

Recently, we developed a novel approach to study the rapid and sudden changes in the direction of ventricular activation called folding phenomenon. In order to better understand this phenomenon, we were interested in studying the variation of the orientation...

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Sedaghat G, Kabir MM, Tereshchenko LG. QRS Loop Folding Phenomenon in Vectorcardiogram of Healthy Individuals. Comput Cardiol (2010). 2017;43:645-648
Sedaghat, G., Kabir, M. M., & Tereshchenko, L. G. (2016). QRS Loop Folding Phenomenon in Vectorcardiogram of Healthy Individuals. Computing in cardiology, 43645-648.
Sedaghat, Golriz, et al. "QRS Loop Folding Phenomenon in Vectorcardiogram of Healthy Individuals." Computing in cardiology vol. 43 (2016): 645-648.
Sedaghat G, Kabir MM, Tereshchenko LG. QRS Loop Folding Phenomenon in Vectorcardiogram of Healthy Individuals. Comput Cardiol (2010). 2016 Sep;43:645-648. Epub 2017 Mar 02. PMID: 28944247; PMCID: PMC5606210.
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Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?.

Computing in cardiology

Bhalodia R, Subramanian A, Morris A, Cates J, Whitaker R, Kholmovski E, Marrouche N, Elhabian S.
PMID: 32632370
Comput Cardiol (2010). 2019;46. doi: 10.22489/cinc.2019.200. Epub 2020 Feb 24.

Evidence suggests that the shape of left atrium appendages (LAA) is a primary indicator in predicting stroke for patients diagnosed with atrial fibrillation (AF). Statistical shape modeling tools used to represent (i.e., parameterize) the underlying LAA variability are of...

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Bhalodia R, Subramanian A, Morris A, et al. Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?. Comput Cardiol (2010). 2020;46doi: 10.22489/cinc.2019.200.
Bhalodia, R., Subramanian, A., Morris, A., Cates, J., Whitaker, R., Kholmovski, E., Marrouche, N., & Elhabian, S. (2019). Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?. Computing in cardiology, 46. https://doi.org/10.22489/cinc.2019.200
Bhalodia, Riddhish, et al. "Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?." Computing in cardiology vol. 46 (2019). doi: https://doi.org/10.22489/cinc.2019.200
Bhalodia R, Subramanian A, Morris A, Cates J, Whitaker R, Kholmovski E, Marrouche N, Elhabian S. Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?. Comput Cardiol (2010). 2019;46. doi: 10.22489/cinc.2019.200. Epub 2020 Feb 24. PMID: 32632370; PMCID: PMC7338006.
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Detecting Ischemic Stress to the Myocardium Using Laplacian Eigenmaps and Changes to Conduction Velocity.

Computing in cardiology

Good WW, Erem B, Coll-Font J, Brooks DH, MacLeod RS.
PMID: 29930952
Comput Cardiol (2010). 2017 Sep;44. doi: 10.22489/CinC.2017.269-417. Epub 2018 Apr 05.

The underlying pathophysiology of ischemia and its electrocardiographic consequences are poorly understood, resulting in unreliable diagnosis of this disease. This limited knowledge of underlying mechanisms suggests a data driven approach, which seeks to identify patterns in the ECG that...

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Good WW, Erem B, Coll-Font J, et al. Detecting Ischemic Stress to the Myocardium Using Laplacian Eigenmaps and Changes to Conduction Velocity. Comput Cardiol (2010). 2018;44doi: 10.22489/CinC.2017.269-417.
Good, W. W., Erem, B., Coll-Font, J., Brooks, D. H., & MacLeod, R. S. (2017). Detecting Ischemic Stress to the Myocardium Using Laplacian Eigenmaps and Changes to Conduction Velocity. Computing in cardiology, 44. https://doi.org/10.22489/CinC.2017.269-417
Good, Wilson W, et al. "Detecting Ischemic Stress to the Myocardium Using Laplacian Eigenmaps and Changes to Conduction Velocity." Computing in cardiology vol. 44 (2017). doi: https://doi.org/10.22489/CinC.2017.269-417
Good WW, Erem B, Coll-Font J, Brooks DH, MacLeod RS. Detecting Ischemic Stress to the Myocardium Using Laplacian Eigenmaps and Changes to Conduction Velocity. Comput Cardiol (2010). 2017 Sep;44. doi: 10.22489/CinC.2017.269-417. Epub 2018 Apr 05. PMID: 29930952; PMCID: PMC6007991.
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A Unified Pipeline for ECG Imaging Testing.

Computing in cardiology

Tate J, van Dam E, Good W, Bergquist J, van Dam P, MacLeod R.
PMID: 32201705
Comput Cardiol (2010). 2019 Sep;46. doi: 10.22489/cinc.2019.437. Epub 2020 Feb 24.

The Consortium for ECG Imaging (CEI) has formed several collaborative projects to evaluate and improve technical aspects of Electrocardiographic Imaging (ECGI), but these efforts are not yet implemented into an integrated software framework. We developed a framework to unify...

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Tate J, van Dam E, Good W, et al. A Unified Pipeline for ECG Imaging Testing. Comput Cardiol (2010). 2020;46doi: 10.22489/cinc.2019.437.
Tate, J., van Dam, E., Good, W., Bergquist, J., van Dam, P., & MacLeod, R. (2019). A Unified Pipeline for ECG Imaging Testing. Computing in cardiology, 46. https://doi.org/10.22489/cinc.2019.437
Tate, Jess, et al. "A Unified Pipeline for ECG Imaging Testing." Computing in cardiology vol. 46 (2019). doi: https://doi.org/10.22489/cinc.2019.437
Tate J, van Dam E, Good W, Bergquist J, van Dam P, MacLeod R. A Unified Pipeline for ECG Imaging Testing. Comput Cardiol (2010). 2019 Sep;46. doi: 10.22489/cinc.2019.437. Epub 2020 Feb 24. PMID: 32201705; PMCID: PMC7083590.
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Reconstructing Cardiac Wave Dynamics From Myocardial Motion Data.

Computing in cardiology

Beam CB, Linte CA, Otani NF.
PMID: 34056029
Comput Cardiol (2010). 2020 Sep;47. doi: 10.22489/CinC.2020.216. Epub 2021 Feb 10.

Various models exist to predict the active stresses and membrane potentials within cardiac muscle tissue. However, there exist no methods to reliably measure active stresses, nor do there exist ways to measure transmural membrane potentials that are suitable for...

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Beam CB, Linte CA, Otani NF. Reconstructing Cardiac Wave Dynamics From Myocardial Motion Data. Comput Cardiol (2010). 2021;47doi: 10.22489/CinC.2020.216.
Beam, C. B., Linte, C. A., & Otani, N. F. (2020). Reconstructing Cardiac Wave Dynamics From Myocardial Motion Data. Computing in cardiology, 47. https://doi.org/10.22489/CinC.2020.216
Beam, Christopher B, et al. "Reconstructing Cardiac Wave Dynamics From Myocardial Motion Data." Computing in cardiology vol. 47 (2020). doi: https://doi.org/10.22489/CinC.2020.216
Beam CB, Linte CA, Otani NF. Reconstructing Cardiac Wave Dynamics From Myocardial Motion Data. Comput Cardiol (2010). 2020 Sep;47. doi: 10.22489/CinC.2020.216. Epub 2021 Feb 10. PMID: 34056029; PMCID: PMC8159184.
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High-Capacity Cardiac Signal Acquisition System for Flexible, Simultaneous, Multidomain Acquisition.

Computing in cardiology

Zenger B, Bergquist JA, Good WW, Steadman B, MacLeod RS.
PMID: 33969144
Comput Cardiol (2010). 2020 Sep;47. doi: 10.22489/cinc.2020.188. Epub 2021 Feb 10.

Capturing cardiac electrical propagation or electrocardiographic images demands simultaneous, multidomain recordings of electrocardiographic signals with adequate spatial and temporal resolution. Available systems can be cost-prohibitive or lack the necessary flexibility to capture signals from the heart and torso. We...

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Zenger B, Bergquist JA, Good WW, et al. High-Capacity Cardiac Signal Acquisition System for Flexible, Simultaneous, Multidomain Acquisition. Comput Cardiol (2010). 2021;47doi: 10.22489/cinc.2020.188.
Zenger, B., Bergquist, J. A., Good, W. W., Steadman, B., & MacLeod, R. S. (2020). High-Capacity Cardiac Signal Acquisition System for Flexible, Simultaneous, Multidomain Acquisition. Computing in cardiology, 47. https://doi.org/10.22489/cinc.2020.188
Zenger, Brian, et al. "High-Capacity Cardiac Signal Acquisition System for Flexible, Simultaneous, Multidomain Acquisition." Computing in cardiology vol. 47 (2020). doi: https://doi.org/10.22489/cinc.2020.188
Zenger B, Bergquist JA, Good WW, Steadman B, MacLeod RS. High-Capacity Cardiac Signal Acquisition System for Flexible, Simultaneous, Multidomain Acquisition. Comput Cardiol (2010). 2020 Sep;47. doi: 10.22489/cinc.2020.188. Epub 2021 Feb 10. PMID: 33969144; PMCID: PMC8106231.
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Showing 1 to 12 of 89 entries