Computing in cardiology最新文献_第9页

Noninvasive Fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013. 无创胎儿心电图：2013 年 PhysioNet/Computing in Cardiology Challenge。

Computing in cardiology Pub Date : 2013-03-01

Ikaro Silva, Joachim Behar, Reza Sameni, Tingting Zhu, Julien Oster, Gari D Clifford, George B Moody

{"title":"Noninvasive Fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013.","authors":"Ikaro Silva, Joachim Behar, Reza Sameni, Tingting Zhu, Julien Oster, Gari D Clifford, George B Moody","doi":"","DOIUrl":"","url":null,"abstract":"The PhysioNet/CinC 2013 Challenge aimed to stimulate rapid development and improvement of software for estimating fetal heart rate (FHR), fetal interbeat intervals (FRR), and fetal QT intervals (FQT), from multichannel recordings made using electrodes placed on the mother's abdomen. For the challenge, five data collections from a variety of sources were used to compile a large standardized database, which was divided into training, open test, and hidden test subsets. Gold-standard fetal QRS and QT interval annotations were developed using a novel crowd-sourcing framework. The challenge organizers used the hidden test subset to evaluate 91 open-source software entries submitted by 53 international teams of participants in three challenge events, estimating FHR, FRR, and FQT using the hidden test subset, which was not available for study by participants. Two additional events required only user-submitted QRS annotations to evaluate FHR and FRR estimation accuracy using the open test subset available to participants. The challenge yielded a total of 91 open-source software entries. The best of these achieved average estimation errors of 187bpm2 for FHR, 20.9 ms for FRR, and 152.7 ms for FQT. The open data sets, scoring software, and open-source entries are available at PhysioNet for researchers interested on working on these problems.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"40 ","pages":"149-152"},"PeriodicalIF":0.0,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4230703/pdf/nihms582730.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32818118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty Visualization in Forward and Inverse Cardiac Models. 心脏正逆模型的不确定性可视化。

Computing in cardiology Pub Date : 2013-01-01

Brett M Burton, Burak Erem, Kristin Potter, Paul Rosen, Chris R Johnson, Dana H Brooks, Rob S Macleod

{"title":"Uncertainty Visualization in Forward and Inverse Cardiac Models.","authors":"Brett M Burton, Burak Erem, Kristin Potter, Paul Rosen, Chris R Johnson, Dana H Brooks, Rob S Macleod","doi":"","DOIUrl":"","url":null,"abstract":"Quantification and visualization of uncertainty in cardiac forward and inverse problems with complex geometries is subject to various challenges. Specific to visualization is the observation that occlusion and clutter obscure important regions of interest, making visual assessment difficult. In order to overcome these limitations in uncertainty visualization, we have developed and implemented a collection of novel approaches. To highlight the utility of these techniques, we evaluated the uncertainty associated with two examples of modeling myocardial activity. In one case we studied cardiac potentials during the repolarization phase as a function of variability in tissue conductivities of the ischemic heart (forward case). In a second case, we evaluated uncertainty in reconstructed activation times on the epicardium resulting from variation in the control parameter of Tikhonov regularization (inverse case). To overcome difficulties associated with uncertainty visualization, we implemented linked-view windows and interactive animation to the two respective cases. Through dimensionality reduction and superimposed mean and standard deviation measures over time, we were able to display key features in large ensembles of data and highlight regions of interest where larger uncertainties exist.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"40 ","pages":"57-60"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221850/pdf/nihms551351.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32803931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012. 预测ICU患者住院死亡率:PhysioNet/计算心脏病学挑战赛2012。

Computing in cardiology Pub Date : 2012-01-01

Ikaro Silva, George Moody, Daniel J Scott, Leo A Celi, Roger G Mark

{"title":"Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012.","authors":"Ikaro Silva, George Moody, Daniel J Scott, Leo A Celi, Roger G Mark","doi":"","DOIUrl":"","url":null,"abstract":"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 contrast, the focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality. The data used for the challenge consisted of 5 general descriptors and 36 time series (measurements of vital signs and laboratory results) from the first 48 hours of the first available ICU stay of 12,000 adult patients from the MIMIC II database. The challenge was organized as two events: event 1 measured performance of a binary classifier, and event 2 measured performance of a risk estimator. The score of event 1 was the lower of sensitivity and positive predictive value. The score for event 2 was a range-normalized Hosmer-Lemeshow statistic. A baseline algorithm (using SAPS-1) obtained event 1 and 2 scores of 0.3125 and 68.58 respectively. Most participants submitted entries that outperformed the baseline algorithm. The top final scores for events 1 and 2 were 0.5353 and 17.88 respectively.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"39 ","pages":"245-248"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3965265/pdf/nihms432772.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32217202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ICU Outcome Predictions using Physiologic Trends in the First Two Days. 利用头两天的生理趋势预测重症监护室的结果。

Computing in cardiology Pub Date : 2012-01-01

Mehmet Kayaalp

{"title":"ICU Outcome Predictions using Physiologic Trends in the First Two Days.","authors":"Mehmet Kayaalp","doi":"","DOIUrl":"","url":null,"abstract":"Aims: This study aims to accurately predict patient mortality in the ICU. Given all physiologic measurements in the first 48 hours of the ICU stay, the Bayesian model of the study predicts outcome with a posterior probability.Methods: This study modeled the outcome as a binary random variable dependent on trends of daily physiologic measures of the patient, where trends were conditionally independent given the outcome. A two-day trend is a sequence of two discrete values, one for each day. Each value (low, medium, high or unmeasured) is a function of the arithmetic mean of that measure on the corresponding day.Results: The prediction performance of the model was measured as the minimum of sensitivity and positive predictive values. The model yielded a score of 0.39 along with a Hosmer-Lemeshow H statistic of 36, which measures calibration. The perfect scores would be 1.0 and 0, respectively.Conclusion: The prediction performance of the study was an improvement over the established ICU scoring metric SAPS-I, whose score was 0.32. Calibration of the model outputs was comparable to that of SAPS-I.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":"977-980"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3607431/pdf/nihms435794.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40231716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Action Potential Propagation Through Tissue Lacking Gap Junctions: Application to Engrafted Cells in Myocardial Infarcts. 缺乏间隙连接的组织中动作电位的传播:在心肌梗死移植细胞中的应用。

Computing in cardiology Pub Date : 2011-09-01

Niels F Otani

引用次数: 0

A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series. 基于心率和血压时间序列的重症监护低血压发作预测器。

Computing in cardiology Pub Date : 2011-03-22

J Lee, Rg Mark

引用次数: 0

Point Process Heart Rate Variability Assessment during Sleep Deprivation. 睡眠剥夺期间的点过程心率变异性评估。

Computing in cardiology Pub Date : 2010-12-31

L Citi, Eb Klerman, En Brown, R Barbieri