2021 Computing in Cardiology (CinC)最新文献_第8页

Sensitivity Analysis and Parameter Identification of a Cardiovascular Model in Aortic Stenosis 主动脉瓣狭窄心血管模型的敏感性分析及参数识别

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662851

M. Taconné, V. Rolle, K. Owashi, V. Panis, A. Hubert, V. Auffret, E. Galli, Alfredo I. Hernández, E. Donal

引用次数: 0

Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation 基于拍形态和图像变形特征提取的短心电信号病理预测

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662714

J. V. Prehn, Svetoslav Ivanov, G. Nalbantov

{"title":"Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation","authors":"J. V. Prehn, Svetoslav Ivanov, G. Nalbantov","doi":"10.23919/cinc53138.2021.9662714","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662714","url":null,"abstract":"Automated detection of key cardiac pathologies in reduced-lead ECGs is an enabling factor in applying ECG analysis on a larger scale. The PhysioNet/Computing in Cardiology Challenge 2021 identifies a set of key cardiac pathologies and challenges us with the task to automatically detect them. Critical to this task is the extraction of features from these ECGs which, combined, mark the presence of one or more of these key cardiac pathologies. Methodology: algorithms were devised to automatically extract features based on the definitions as used in medical practice, beat morphology and image deformation. A binary classifier for each key cardiac pathology was trained using these features, extracted from the labeled ECGs from The Challenge. The binary classifiers were combined into a multi-label classifier by learning thresholds on the scores of the binary classifiers using Bayesian optimization in a cross-validation setting. Results: our contribution submitted for evaluation achieved a challenge metric score of 0.28, 0.31, 0.32, 0.28 and 0.23 placing us (team DSC) 29, 25, 25, 28 and 28 out of 39 teams which submitted an official entry on 12-, 6-,4-, 3- and 2-lead test datasets respectively.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125725262","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

Classifying Different Dimensional ECGs Using Deep Residual Convolutional Neural Networks 基于深度残差卷积神经网络的不同维数脑电图分类

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662704

Wenjie Cai, Fanli Liu, Xuan Wang, Bo-Ming Xu, Yao-Chin Wang

{"title":"Classifying Different Dimensional ECGs Using Deep Residual Convolutional Neural Networks","authors":"Wenjie Cai, Fanli Liu, Xuan Wang, Bo-Ming Xu, Yao-Chin Wang","doi":"10.23919/cinc53138.2021.9662704","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662704","url":null,"abstract":"Introduction: The electrocardiogram (ECG) is the most common diagnostic tool for screening cardiovascular diseases. PhysioNet/Computing in Cardiology Challenge 2021 aims to classify cardiac abnormalities from twelve-lead, six-lead, four-lead, three-lead, and two-lead ECGs. Methods: ECGs were downsampled to 250 Hz and then applied with a bandpass filter to reduce noise. The unscored label named VEB was transformed to PVC. The ECGs labeled as AF in the Ningbo Database were relabeled as AFL or AF. All ECGs were randomly shuffled and divided into a training set and a validation set at 4:1. Five models based on a deep residual convolutional neural network were proposed to make classification from different dimensions of ECGs. A novel loss calculation method was proposed to balance the different labeling tendency of different source data sets. Results: Our team, USST_Med, received an official test score of 0.54, 0.52, 0.50, 0.51, and 0.50 on twelve-lead, six-lead, four-lead, three-lead, and two-lead ECG test sets, respectively. The scores are ranked 5th, 3rd, 7th, 5th and 7th, respectively. Conclusion: The proposed models performed well on classifying ECGs and have potential for clinical application.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125835936","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: 3

Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification 用于变导联心电分类的深度残差网络鲁棒和任务感知训练

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662739

Hansheng Ren, Miao Xiong, Bryan Hooi

引用次数: 4

A Real-Time Digital Pacemaker Pulse Detection Algorithm 一种实时数字起搏器脉冲检测算法

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662885

Haoyu Jiang, Mimi Hu, Junbiao Hong, Yijing Li, Xianliang He

引用次数: 0

Investigating the Robustness of Deep Learning to Electrocardiogram Noise 研究深度学习对心电图噪声的鲁棒性

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662862

Jenny Venton

引用次数: 2

First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG 12导联心电图自我监督预训练的第一步

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662748

Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström, Thomas Bo Schön

{"title":"First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG","authors":"Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström, Thomas Bo Schön","doi":"10.23919/cinc53138.2021.9662748","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662748","url":null,"abstract":"Self-supervised learning is a paradigm that extracts general features which describe the input space by artificially generating labels from the input without the need for explicit annotations. The learned features can then be used by transfer learning to boost the performance on a downstream task. Such methods have recently produced state of the art results in natural language processing and computer vision. Here, we propose a self-supervised learning method for 12-lead electrocardiograms (ECGs). For pretraining the model we design a task to mask out subsegements of all channels of the input signals and try to predict the actual values. As the model architecture, we use a U-ResNet containing an encoder-decoder structure. We test our method by self-supervised pretraining on the CODE dataset and then transfer the learnt features by finetuning on the PTB-XL and CPSC benchmarks to evaluate the effect of our method in the classification of 12-leads ECGs. The method does provide modest improvements in performance when compared to not using pretraining. In future work we will make use of these ideas in smaller dataset, where we believe it can lead to larger performance gains.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121698647","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: 2

Deep Learning Based Classification of True/False Arrhythmia Alarms in the Intensive Care Unit 基于深度学习的重症监护病房心律失常真假报警分类

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662874

Jackie H Boynton, Byung Suk Lee

引用次数: 0

Spatial Relationship Between Atrial Fibrillation Drivers and the Presence of Repetitive Conduction Patterns Using Recurrence Analysis on In-Silico Models 利用计算机模型递归分析心房颤动驱动因素与重复传导模式存在的空间关系

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662692

Victor Gonçalves Marques, A. Gharaviri, Simone Pezzuto, P. Bonizzi, S. Zeemering, U. Schotten

{"title":"Spatial Relationship Between Atrial Fibrillation Drivers and the Presence of Repetitive Conduction Patterns Using Recurrence Analysis on In-Silico Models","authors":"Victor Gonçalves Marques, A. Gharaviri, Simone Pezzuto, P. Bonizzi, S. Zeemering, U. Schotten","doi":"10.23919/cinc53138.2021.9662692","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662692","url":null,"abstract":"Catheter ablation treatment for atrial fibrillation (AF) is still suboptimal, possibly due to the difficulty to identify AF drivers. Recurrence analysis can be used to detect and eventually locate repetitive patterns that tend to be generated by AF drivers. In this study, we aimed to understand the spatial relationship between repetitiveness in recurrence analysis and rotor positions in an in-silico AF model. AF was simulated in a detailed three-dimensional model of the atria considering different degrees of endomysial fibrosis (0% and 70%). Rotors driving AF were tracked based on phase singularities obtained from transmembrane potentials. Activation-phase signals calculated from electrograms (4×4 electrode grid, 3 mm spacing) were used for recurrence analysis. Intervals with and without long-lasting sources inside the electrode coverage area were determined; the recurrence in both groups of intervals was quantified and compared with each other by calculating the recurrence rate (RR) per AF cycle length. RRs were lower during intervals with sources for both 0% and 70% fibrosis groups (0.56 [0.36;0.85] vs. 0.90 [0.80;0.97], $p < 0.001$ and 0.73 [0.41;0.84] vs. 0.87 [0.76;0.92], $p < 0.001$, respectively). These results indicate that recurrences are found in the area adjacent to the sources but not on the sources themselves, thus suggesting that recurrence analysis could contribute to guide ablation therapy.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126473429","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

Data Augmentation for Discrimination of Atrial Flutter Mechanism Using 12-Lead Surface Electrocardiogram 12导联体表心电图鉴别心房扑动机制的数据增强

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662957

Muhammad Usman Gul, K. Kadir, Muhammad Haziq Kamarul Azman

{"title":"Data Augmentation for Discrimination of Atrial Flutter Mechanism Using 12-Lead Surface Electrocardiogram","authors":"Muhammad Usman Gul, K. Kadir, Muhammad Haziq Kamarul Azman","doi":"10.23919/cinc53138.2021.9662957","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662957","url":null,"abstract":"In the previous study, the atrial flutter mechanism (i.e., Focal or Macroreentrant) was differentiated from the standard 12-lead ECG by the variability of the cycle length of visible successive P-waves (between the R-R waves). This study aims to help researchers reduce imbalances through two different techniques, especially in atrial flutter. Besides, early detection of the AFL mechanism can increase the efficacy of invasive elimination. The proposed model has been extracted several features derived from statistical analysis of the intervals of successive atrial rhythm. Forty-eight patients were undergone endoscopic catheter ablation for the identifications of the AFL mechanism. Two different techniques, SMOTE and Smoothed-Bootstrap, have been used to augment and re-balance the dataset. The synthetic data generated by Smoothed-Bootstrap has been much closer to the original dataset and relatively better than SMOTE technique. The performance has been evaluated by three linear classifiers Linear Discriminant Analysis (LDA), Logistic Regression (LOG), and Support Vector Machine (SVM). The LOG classifier achieved its average performance with accuracy, specificity, sensitivity, 71.08%, 77.13%, and 65.12%, respectively. Smoothed-Bootstrap is a suitable technique in AFL cases to minimize the imbalance issue. The variability in cycle length of consecutive P-waves from the surface ECG has differentiated the Focal AFLfrom Macrorrentrant AFL.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121469397","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