2020 Computing in Cardiology最新文献

Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network 基于心电矩阵和卷积神经网络的房颤自动检测

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.170

R. Salinas-Martínez, J. D. Bie, Nicoletta Marzocchi, F. Sandberg

引用次数: 4

Influence of Gradient and Smoothness of Atrial Wall Thickness on Initiation and Maintenance of Atrial Fibrillation 心房壁厚度梯度和平滑度对房颤发生和维持的影响

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.261

L. Azzolin, G. Luongo, S. R. Ventura, J. Saiz, O. Dössel, A. Loewe

{"title":"Influence of Gradient and Smoothness of Atrial Wall Thickness on Initiation and Maintenance of Atrial Fibrillation","authors":"L. Azzolin, G. Luongo, S. R. Ventura, J. Saiz, O. Dössel, A. Loewe","doi":"10.22489/CinC.2020.261","DOIUrl":"https://doi.org/10.22489/CinC.2020.261","url":null,"abstract":"This work uses a highly detailed computational model of human atria to investigate the effect of spatial gradient and smoothing of atrial wall thickness on inducibility and maintenance of atrial fibrillation (AF) episodes. An atrial model with homogeneous thickness (HO) was used as baseline for the generation of different atrial models including either a low (LG) or high thickness gradient between left/right atrial free wall and the other regions. Since the model with high spatial gradient presented non-natural sharp edges between regions, either 1 (HG1) or 2 (HG2) Laplacian smoothing iterations were applied. Arrhythmic episodes were initiated using a rapid pacing protocol and long-living rotors were detected and tracked over time. Thresholds optimised with receiver operating characteristic analysis were used to define high gradient/curvature regions. Greater spatial gradients increased the atrial model inducibility and unveiled additional regions vulnerable to maintain AF drivers. In the models with heterogeneous wall thickness (LG, HG2 and HG1), 73.5 ± 8.7% of the long living rotors were found in areas within 1.5mm from nodes with high thickness gradient, and 85.0 ± 3.4% in areas around high endocardial curvature. These findings promote wall thickness gradient and endocardial curvature as measures of AF vulnerability.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115423112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Automatic Detection of Characteristic Waves in Electrocardiogram 心电图特征波的自动检测

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.174

L. Billeci, L. Bachi, M. Varanini

引用次数: 0

Beat-to-beat P-wave Variability Increases From Paroxysmal to Persistent Atrial Fibrillation 从阵发性心房颤动到持续性心房颤动，搏动间p波变异性增加

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.205

Rita Laureanti, S. Zeemering, M. Zink, V. Corino, A. Auricchio, L. Mainardi, U. Schotten

{"title":"Beat-to-beat P-wave Variability Increases From Paroxysmal to Persistent Atrial Fibrillation","authors":"Rita Laureanti, S. Zeemering, M. Zink, V. Corino, A. Auricchio, L. Mainardi, U. Schotten","doi":"10.22489/CinC.2020.205","DOIUrl":"https://doi.org/10.22489/CinC.2020.205","url":null,"abstract":"Atrial fibrillation (AF) is known to worsen over time. Beat-to-beat P-wave variability is used to evaluate the risk of developing AF, but it has not been used to monitor arrhythmia progression in a comprehensive model. The aim of this study is to create a method to measure beat-to-beat P-wave variability to evaluate AF types. ECG recordings of 5 minutes were measured on 159 AF patients. The first three principal components (PCs) of the ECG signal were added to the analysis. The temporal beat-to-beat P-wave variability was assessed through the normalized Euclidean Distance and the Similarity Index. The spatial P-wave similarity was measured as the percentage of variance explained by the first 2 PCs. A binomial logistic regression model was built for each lead and parameter, with AF type as dependent variable. To assess variability due exclusively to the P-waves, we considered, as confounding factors, other sources of ECG-variability, such as the noise level, the variability of the RR series and of the heart axis. Both temporal (e.g. 0.94±0.12 for paroxysmal AF and 0.85±0.28 for persistent AF in lead I, p=0.001) and spatial P-wave similarities (95.35±3.29% for paroxysmal AF vs 94.44±4.14% for persistent AF, p=0.001) were significantly higher in paroxysmal than in persistent AF, suggesting them as promising tools to evaluate AF types.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124269877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Explaining Black-Box Automated Electrocardiogram Classification to Cardiologists 向心脏病专家解释黑匣子自动心电图分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.452

D. Oliveira, Antônio H. Ribeiro, João A. O. Pedrosa, Gabriela M. M. Paixão, A. L. Ribeiro, Wagner Meira

{"title":"Explaining Black-Box Automated Electrocardiogram Classification to Cardiologists","authors":"D. Oliveira, Antônio H. Ribeiro, João A. O. Pedrosa, Gabriela M. M. Paixão, A. L. Ribeiro, Wagner Meira","doi":"10.22489/CinC.2020.452","DOIUrl":"https://doi.org/10.22489/CinC.2020.452","url":null,"abstract":"In this work, we present a method to explain “end-to-end” electrocardiogram (ECG) signal classifiers, where the explanations were built along with seniors cardiologist to provide meaningful features to the final users. Our method focuses exclusively on automated ECG diagnosis and analyzes the explanation in terms of clinical accuracy for interpretability and robustness. The proposed method uses a noise-insertion strategy to quantify the impact of intervals and segments of the ECG signals on the automated classification outcome. An ECG segmentation method was applied to ECG tracings, to obtain: (1) Intervals, Segments and Axis; (2) Rate, and (3) Rhythm. Noise was added to the signal to disturb the ECG features in a realistic way. The method was tested using Monte Carlo simulation and the feature impact is estimated by the change in the model prediction averaged over 499 executions and a feature is defined as important if its mean value changes the result of the classifier. We demonstrate our method by explaining diagnoses generated by a deep convolutional neural network. The proposed method is particularly effective and useful for modern deep learning models that take raw data as input.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet 利用SE-ResNet从心电信号中分类心脏异常

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.281

Zhaowei Zhu, Han Wang, Tingting Zhao, Yangming Guo, Zhuoyang Xu, Zhuo Liu, Siqi Liu, Xiang Lan, Xingzhi Sun, Mengling Feng

引用次数: 23

Deep Multi-Label Multi-Instance Classification on 12-Lead ECG 12导联心电图的深度多标签多实例分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.095

Yingjing Feng, E. Vigmond

引用次数: 6

Unravelling the Mechanistic Links between Pro-Arrhythmia and Mechanical Function 揭示促心律失常与机械功能之间的机制联系

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.409

Hannah J. Smith, F. Margara, B. Rodríguez

{"title":"Unravelling the Mechanistic Links between Pro-Arrhythmia and Mechanical Function","authors":"Hannah J. Smith, F. Margara, B. Rodríguez","doi":"10.22489/CinC.2020.409","DOIUrl":"https://doi.org/10.22489/CinC.2020.409","url":null,"abstract":"Sudden cardiac death (SCD) from ventricular arrhythmias is a leading cause of mortality. Accurate arrhythmic risk stratification is vital for preventative clinical interventions. Ejection fraction (EF) is the primary metric used, but its accuracy is under debate, as many SCD cases exhibit preserved EF. Thus, identifying clear links between EF and arrhythmic risk is critical. Here, as a step forward, we investigate the ionic processes determining cellular pro-arrhythmic mechanisms and their relationship with active tension. A population of 2500 human ventricular electromechanical cellular models was created, and stimulated to produce pro-arrhythmic behaviour. We quantified their susceptibility to develop early afterdepolarizations (EADs) and action potential duration (APD) shortening, as key arrhythmic markers. The relationship between both arrhythmic markers and tension amplitude was found to be highly dependent on ionic mechanism. Variability in L-type calcium current was the primary determinant of active tension and arrhythmia susceptibility, alongside SERCA and hERG expression. Models with low tension could exhibit both high and low EAD susceptibility. APD shortening, however, displayed a weak positive correlation with active tension amplitude.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124989960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Utilization of Residual CNN-GRU With Attention Mechanism for Classification of 12-lead ECG 利用带有注意机制的残差CNN-GRU对12导联心电图进行分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.032

P. Nejedly, Adam Ivora, I. Viscor, J. Halámek, P. Jurák, F. Plesinger

{"title":"Utilization of Residual CNN-GRU With Attention Mechanism for Classification of 12-lead ECG","authors":"P. Nejedly, Adam Ivora, I. Viscor, J. Halámek, P. Jurák, F. Plesinger","doi":"10.22489/CinC.2020.032","DOIUrl":"https://doi.org/10.22489/CinC.2020.032","url":null,"abstract":"Cardiac diseases are the most common cause of death. The fully automated classification of the electrocardiogram (ECG) supports early capturing of heart disorders, and, consequently, may help to get treatment early. Here in this paper, we introduce a deep neural network for human ECG classification into 24 independent groups, for example, atrial fibrillation, 1st degree AV block, Bundle branch blocks, premature contractions, changes in the ST segment, normal sinus rhythm, and others. The network architecture utilizes a convolutional neural network with residual blocks, bidirectional Gated Recurrent Units, and an attention mechanism. The algorithm was trained and validated on the public dataset proposed by the PhysioNet Challenge 2020. The trained algorithm was tested using a hidden test set during the official phase of the challenge and obtained the challenge validation score of 0.659 as entries by the ISIBrno team. The final testing scores were 0.847, 0.195, −0.006, and 0.122, for testing sets I, II, III, and full test set, respectively. We have obtained 30th place out of 41 teams in the official ranking.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125862547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Effects of the ECG Sampling Frequency on the Multiscale Entropy of Heart Rate Variability 心电采样频率对心率变异性多尺度熵的影响

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.088

P. Castiglioni, A. Faini

{"title":"Effects of the ECG Sampling Frequency on the Multiscale Entropy of Heart Rate Variability","authors":"P. Castiglioni, A. Faini","doi":"10.22489/CinC.2020.088","DOIUrl":"https://doi.org/10.22489/CinC.2020.088","url":null,"abstract":"It is known that the spectral analysis of heart rate variability requires an ECG sampling frequency Fs>1 00 Hz with parabolic interpolation to refine the R peak if Fs<250Hz. By contrast, the effects of quantization errors in Multiscale Entropy (MSE) analysis due to low Fs have never been evaluated systematically. Our aim is thus to describe the effects of low Fs and parabolic interpolation on MSE. We considered 21 ECG recordings of 10’ duration sampled at 500Hz (reference). We decimated the ECG to simulate Fs between 250 and 50Hz, we extracted the tachograms without and with parabolic interpolation and estimated MSE at scales between 1 beat (=SampEn) and 50 beats. The estimates were expressed as the percentage of the reference and the error was quantified by the interquartile range (IQR) of their distribution. SampEn showed high sensitivity to Fs with IQR > 1 0% at 250Hz and >16% at 167Hz; however, the parabolic interpolation dramatically decreased the IQR below 2% up to Fs=71Hz. The MSE estimates at larger scales were less sensitive to Fs with IQR≤2% even at Fs=50Hz. Thus the ECG sampling rate is more critical for SampEn than for MSE at larger scales and interpolation procedures are required when Fs<250Hz.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126141289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0