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检索条件"任意字段=Conference on Computing in Cardiology"

共 1032 条记录，以下是81-90 订阅

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Evaluation of the Changes in RR and QT Circadian Rhythms in Bedridden Subjects

Evaluation of the Changes in RR and QT Circadian Rhythms in ...

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computing in cardiology conference (CinC)

作者： Solbiati, Sarah Paglialonga, Alessia Costantini, Lorenzo Caiani, Enrico G. Natl Res Council Italy CNR Inst Elect Comp & Telecommun Engn IEIIT Milan Italy Politecn Milan Elect Informat & Bioengn Dept Pzza L da Vinci 32 I-20133 Milan Italy Azienda Sanit Locale Lecce Cardiovasc Dept Lecce Italy

ISBN: (纸本)9781728173825

Prolonged bed rest (BR), often related to hospitalization, chronic diseases and ageing, as well as hospitalized COVid-19 patients, induces reduced functional capacity in multiple body systems and rhythms dysregulation, possibly leading to cardiovascular deconditioning and increased arrhythmogenic risk. In this study, we analyzed 24h Holter ECGs collected from 10 healthy subjects before, during, and after a 10-day BR, aiming at assessing the decline of the circadian rhythms of RR and ventricular repolarization intervals. Our results proved that a 10-day BR induced changes in the characteristics of cardiac circadian rhythms, in terms of midline value, oscillation amplitude and acrophase, offering a first insight for the formulation of appropriate countermeasures for improving homeostasis maintenance in hospitalized patients.

关键词： cardiology

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AF Classification from a Short Single Lead ECG Recording: the PhysioNet/computing in cardiology Challenge 2017 44

AF Classification from a Short Single Lead ECG Recording: th...

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44th computing in cardiology conference (CinC)

作者： Clifford, Gari D. Liu, Chengyu Moody, Benjamin Lehman, Li-Wei H. Silva, Ikaro Li, Qiao Johnson, A. E. Mark, Roger G. Emory Univ Dept Biomed Informat Atlanta GA 30322 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA Southeast Univ Sch Instrument Sci & Engn Nanjing Jiangsu Peoples R China MIT Inst Med Engn & Sci Cambridge MA 02139 USA

ISBN: (纸本)9781538645550;9781538666302

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 training set and 3,658 in the private hidden test set. Due to the high degree of inter-expert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants' algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.

关键词： Electrocardiography Heart beat Noise measurement Training Classification algorithms Lead cardiology Heart beat cardiology Noise measurement Sorting algorithm Electrocardiography Functional training Lead ecg lead Training alachlor Professional personnel

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Bayesian Voting of Multiple Annotators for Improved QT Interval Estimation

Bayesian Voting of Multiple Annotators for Improved QT Inter...

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40th Annual Meeting on computing in cardiology (CinC)

作者： Zhu, Tingting Johnson, Alistair E. W. Behar, Joachim Clifford, Gari D. Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

ISBN: (纸本)9781479908844

Human bias and significant intra-and inter-observer variance exist in electrocardiogram QT interval evaluation. A Bayesian approach (BA) with an informative prior, that combines measures from multiple humans or algorithms as well as contextual information (such as heart rate and signal quality) was developed for inferring the true QT length. The developed method is compared to the mean and median voting approaches by computing the root-mean-square (RMS) error between the computed QT lengths and the reference annotations provided by the 2006 PhysioNet/computing in cardiology Challenge. The BA with features can reduces the human RMS error of QT estimates to 6.04ms and 13.97ms for automated algorithms, out-performing the results in the Challenge of 6.67ms and 16.34ms respectively. For three annotators, the BA had a 10.7% improvement over the next best voting strategy for manual annotations, and 14.4% for automated algorithms. For large numbers of annotators, the BA estimates became approximately equal to the best-performing annotator.

关键词： cardiology

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Fitting Membrane Resistance in Single Cardiac Myocytes reduces Variability in Parameters 41

Fitting Membrane Resistance in Single Cardiac Myocytes reduc...

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41st computing in cardiology conference (CinC)

作者： Kaur, Jaspreet Nygren, Anders Vigmond, Edward J. Univ Calgary Calgary AB T2N 1N4 Canada Univ Bordeaux 1 F-33405 Talence France

ISBN: (纸本)9781479943463

Mathematical models of single cardiac myocytes have a valuable role in driving progress in cardiac physiology and in exploring the electro physiological mechanisms underlying heart function. Most of these models are used to mimic the results of experimentally observed biological phenomena measured in animal models, and can also provide quantitative insights into natural processes. Adjusting parameters in an ionic model to reproduce experimental behaviour is difficult. Mostly, researchers fit the only the net current to reproduce an action potential (AP) shape. However, even with an excellent AP match in the single cell, tissue behaviour can be vastly different. We hypothesize that this uncertainty can be reduced by additionally jitting R-m.

关键词： bioelectric potentials biological tissues biomedical measurement biomembranes cardiology cellular biophysics data analysis electric current medical computing partial differential equations pattern matching physiological models AP shape reproduction action potential reproduction animal model measurement biological phenomena measurement cardiac physiology electrophysiological mechanism experimental behaviour reproduction heart function ionic model parameter adjustment mathematical model membrane resistance fitting net current fitting parameter variability reduction single cardiac myocyte model single cell AP matching tissue behaviour Abstracts Biological system modeling Computational modeling Genetics MIMICs Biological system modeling biomedical measurement Bioelectric potentials cellular biophysics Millimeter wave monolithic integrated circuits medical computing cardiac functions physiological models Cardiac Myocytes Membrane, biological Pattern matching TISSUES (BIOLOGY) electric current Computational modeling cardiology

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Encoding the Electrocardiogram Details in the Host Record's Bandgap for Authorization-Dependent ECG Quality 41

Encoding the Electrocardiogram Details in the Host Record's ...

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41st computing in cardiology conference (CinC)

作者： Augustyniak, Piotr AGH Univ Sci & Technol 30 Mickiewicz Ave PL-30059 Krakow Poland

ISBN: (纸本)9781479943463

In this paper we propose using the ECG bandgap storage area for coding high frequency details of the signal. Authorization-dependent access to these details allows privileged users for high precision analysis, while regular users with an access to standard resolution signal are still able to correctly calculate essential parameters. The proposed algorithm performs limited ECG interpretation, analysis of bandgap properties and respective adjustment of supplementary details. Depending of selected coding bit depth, the algorithm slightly affects the ECG (PRD = 0.51% for 5 bps) or the supplementary details (PRD = 1.16% for 1 bps) The storage space is sufficient for accommodate oversampling details of 50 ms enhancing the signal in selected area (e.g. QRS-end for VLP).

关键词： authorisation electrocardiography encoding medical signal processing signal sampling ECG bandgap storage area QRS VLP authorization-dependent ECG quality authorization-dependent access coding bit depth electrocardiogram detail encoding signal oversampling Abstracts cardiology Electrocardiography Information services Photonic band gap Physiology Time-frequency analysis Electrocardiography coding asparaginase/prednisone/vincristine signal sampling Information Services medical signal processing Photonic band gap Authorization Time frequency analysis cardiology Band gap Physiology storage spaces quick release bit depth

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ECG Classification Based on Time and Frequency Domain Features Using Random Forests 44

ECG Classification Based on Time and Frequency Domain Featur...

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44th computing in cardiology conference (CinC)

作者： Kropf, Martin Hayn, Dieter Schreier, Guenter Charite Virchow Klinikum Berlin Germany AIT Graz Austria Graz Univ Technol Graz Austria

ISBN: (纸本)9781538645550;9781538666302

We present a combined method of classical signal analysis and machine learning algorithms for the automated classification of 1-lead ECG recordings, which was developed in the course of the computing in cardiology Challenge 2017. To classify ECG recordings into the four classes as defined for the Challenge (normal, suspicious to AF, suspicious to other arrhythmia, noise) we used MATLABand a set of algorithms for detection of beats, wave point detection on detected beats, quality evaluation of the detection, averaging of beats, beat classification, rhythm classification and many more. We extracted a variety of features from both time and frequency domain etc. as input features for the classifier. A total of 380 features were used to train a Random Forest - based classifier (bagged decision trees). Since classes for the Challenge were severely unbalanced, weights based on the class distribution were applied. To train the classifier and for our internal evaluation we used cross-validation on all available ECGS from the training-set. 10-fold cross-validated F-1 score on the training set is 0.83. Final F-1 score from the official challenge evaluation on the enhanced dataset is 0.81, which is quite close to the other top performing algorithms.

关键词： Electrocardiography Feature extraction Classification algorithms Machine learning algorithms Training Predictive models cardiology Machine learning algorithms Electrocardiography Sorting algorithm cardiology Prediction models Feature extraction Functional training Frequency-domain analysis Signal analysis Classifiers electrochemical grinding Beat generation

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Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/computing in cardiology Challenge 2016 43

Classification of Normal/Abnormal Heart Sound Recordings: th...

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43rd computing in cardiology conference (CinC)

作者： Clifford, Gari D. Liu, Chengyu Moody, Benjamin Springer, David Silva, Ikaro Li, Qiao Mark, Roger G. Emory Univ Dept Biomed Informat Atlanta GA 30322 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA MIT Inst Med Engn & Sci Cambridge MA 02139 USA Univ Oxford Dept Engn Sci Inst Biomed Engn Oxford England

ISBN: (纸本)9781509008957

In the past few decades heart sound signals (i.e., phonocardiograms or PCGs) have been widely studied. Automated heart sound segmentation and classification techniques have the potential to screen for pathologies in a variety of clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of a large and open database of heart sound recordings. The PhysioNet/ computing in cardiology (CinC) Challenge 2016 addresses this issue by assembling the largest public heart sound database, aggregated from eight sources obtained by seven independent research groups around the world. The database includes 4,430 recordings taken from 1,072 subjects, totalling 233,512 heart sounds collected from both healthy subjects and patients with a variety of conditions such as heart valve disease and coronary artery disease. These recordings were collected using heterogeneous equipment in both clinical and nonclinical (such as in-home visits). The length of recording varied from several seconds to several minutes. Additional data provided include subject demographics (age and gender), recording information (number per patient, body location, and length of recording), synchronously recorded signals (such as ECG), sampling frequency and sensor type used. Participants were asked to classify recordings as normal, abnormal, or not possible to evaluate (noisy/uncertain). The overall score for an entry was based on a weighted sensitivity and specificity score with respect to manual expert annotations. A brief description of a baseline classification method is provided, including a description of open source code, which has been provided in association with the Challenge. The open source code provided a score of 0.71 (Se=0.65 Sp=0.76). During the official phase of the competition, a total of 48 teams submitted 348 open source entries, with a highest score of 0.86 (Se=0.94 Sp=0.78).

关键词： cardiology

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Analysis of Pressure Gradient Across Aortic Stenosis with Massively Parallel Computational Simulations 41

Analysis of Pressure Gradient Across Aortic Stenosis with Ma...

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41st computing in cardiology conference (CinC)

作者： Randles, Amanda Draeger, Erik Michor, Franziska Lawrence Livermore Natl Lab Livermore CA USA Dana Farber Canc Inst Boston MA 02115 USA

ISBN: (纸本)9781479943463

Coarctation of the aorta (CoA) is one of the most common congenital heart defects in the United States, and despite treatment, patients have a decrease in life expectancy. Computational fluid dynamics simulations can provide the physician with a non-invasive method to measure the pressure gradient. With HARVEY, a massively parallel hemodynamics application, patient specific simulations can be conducted of large regions of the vasculature. The pressure across the stenosis is impacted by flow from nearby vessels. The purpose of this study was to study the impact of including these distal vessels in the simulation on the resulting pressure measurements. Computational fluid dynamic simulations were conducted in three subsets of one patient's vasculature. We demonstrate up to a 29% difference in calculated pressure gradient based on the number of vessels included in the simulation. These initial results are positive but need to be substantiated with further patient studies.

关键词： blood vessels cardiology computational fluid dynamics flow simulation haemodynamics medical computing medical disorders HARVEY aorta coarctation aortic stenosis computational fluid dynamics simulations congenital heart defects massively parallel computational simulations parallel hemodynamics application patient specific simulations pressure gradient analysis pressure measurements vasculature Biomedical imaging Computational modeling Geometry Heart Mathematical model Pressure measurement Solid modeling Pressure measurement Aortic Coarctation Congenital Heart Defects pressure gradients aortic stenosis Blood Vessels computational fluid dynamics medical computing medical disorders entity modeling Biomedical imaging Hemodynamics flow simulation Computational modeling cardiology

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Two Aspects of Cardiac Alternans - Difference and Correlation between Them 43

Two Aspects of Cardiac Alternans - Difference and Correlatio...

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43rd computing in cardiology conference (CinC)

作者： Wang, Wei Whittaker, Dominic G. Ni, Haibo Wang, Kuanquan Zhang, Henggui Univ Manchester Manchester Lancs England Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9781509008957

Cardiac alternans are observed as action potential duration (APD) alternans and/or Ca2+ transient (CaT) alternans. This study aims to investigate the mechanism underlying the genesis of the two types of alternans, their difference and correlation. Two human ventricle models were used for generating cardiac alternans. APD restitution and CaT-SR content curves were constructed to investigate possible different mechanisms. Results showed that alternans produced by the ORd model sustained with significant CaT but no obvious APD alternans, whereas alternans generated by the TP model manifested as obvious APD but insignificant CaT alternans. CaT alternans in the ORd model was correlated with a much steeper CaT-SR content curve, while the APD alternans in the TP model was correlated with a steeper APD restitution curve. Furthermore, I-CaL clamp simulations showed the magnitude of I-CaL was crucial for generating the CaT alternans as a large I-CaL could change its alternating status. Our results suggest that each of the APD or the CaT alternans has its own primary mechanism, and sustained CaT alternans is not necessarily related to APD alternans.

关键词： cardiology

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Data preprocessing and mortality prediction: the Physionet/CinC 2012 challenge revisited 41

Data preprocessing and mortality prediction: the Physionet/C...

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41st computing in cardiology conference (CinC)

作者： Johnson, Alistair E. W. Kramer, Andrew A. Clifford, Gari D. Univ Oxford Oxford OX1 2JD England Cerner Corp Vienna VA USA Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9781479943463

The Physionet/CinC 2012 challenge focused on improving patient specific mortality predictions in the intensive care unit. While most of the focus in the challenge was on applying sophisticated machine learning algorithms, little attention was paid to the preprocessing performed on the data a priori. We compare four standard pre-processing methods with a novel Box-Cox outlier rejection technique and analyze their effect on machine learning classifiers for predicting the mortality of ICU patients. The best machine learning model utilized the proposed preprocessing method and achieved an AUROC of 0.848. In general, the AUROC of models using our novel preprocessing method increased, and this increase was as much as 0.02 in some cases. Furthermore, the use of preprocessing improved the performance of regression models to a higher level than that of non-linear techniques such as random forests. We demonstrate that proper preprocessing of the data prior to use in a prognostic model can significantly improve performance. This improvement can be even greater than that provided by more complex non-linear machine learning algorithms.

关键词： cardiology data mining learning (artificial intelligence) medical computing regression analysis AUROC Box-Cox outlier rejection technique ICU patient mortality prediction Physionet-CinC 2012 challenge area under the receiver operating characteristic complex nonlinear machine learning algorithms computing in cardiology intensive care unit machine learning classifiers prognostic model random forests regression model performance sophisticated machine learning algorithms standard pre-processing methods Data models Data preprocessing Feature extraction Heart rate Predictive models Support vector machines Training

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