2021 Computing in Cardiology (CinC)最新文献

Compensation of Model Errors in Electrocardiographic Imaging Using Bayesian Estimation 基于贝叶斯估计的心电图成像模型误差补偿

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662933

Furkan Aldemir, Y. S. Dogrusoz

{"title":"Compensation of Model Errors in Electrocardiographic Imaging Using Bayesian Estimation","authors":"Furkan Aldemir, Y. S. Dogrusoz","doi":"10.23919/cinc53138.2021.9662933","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662933","url":null,"abstract":"Bayesian Maximum a Posteriori (MAP) estimation has been successfully applied to electrocardiographic imaging (ECGI). However, in most studies, MAP deals only with the measurement noise and ignores the forward model errors. In this study, we incorporated model uncertainty in the MAP formulation to improve the inverse reconstructions. Measured electrograms (EGM) from the University of Utah were used to form training and test datasets. Body surface potential (BSP) measurements were simulated at 30 dB SNR. The inverse problem was solved using MAP estimation. The training dataset was used to define the prior probability function (pdf). Both the measurement noise and model error were assumed to be uncorrelated with the EGMs. Model error was introduced as shift in the heart position and scaling of the heart size. Three model error pdfs were considered: no compensation (model error is assumed as zero in the solution); model error is modeled as independent and identically distributed (IID) and correlated across leads (CORR). For IID and CORR, pdf was estimated based on all geometry disturbances. Results were evaluated using spatial (sCC) and temporal (tCC) correlation coefficients. These results showed that including model errors in the MAP formulation, even in a simple form such as the IID, improved the reconstructions over ignoring them.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"11 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":"115005733","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

In-Silico Data Based Machine Learning Technique Predicts Premature Ventricular Contraction Origin Coordinates 基于计算机数据的机器学习技术预测室性早搏原点坐标

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662896

Andony Arrieula, H. Cochet, P. Jaïs, M. Haïssaguerre, N. Zemzemi, M. Potse

{"title":"In-Silico Data Based Machine Learning Technique Predicts Premature Ventricular Contraction Origin Coordinates","authors":"Andony Arrieula, H. Cochet, P. Jaïs, M. Haïssaguerre, N. Zemzemi, M. Potse","doi":"10.23919/cinc53138.2021.9662896","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662896","url":null,"abstract":"Premature ventricular contraction (PVC) can induce ventricular tachycardia or ventricular fibrillation. Drug-resistant PVCs can be cured by catheter ablation, but the accurate localization that this requires can be difficult and time-consuming. An accurate pre-procedural estimate of the origin could make the procedure more efficient. We propose a machine-learning method for accurate pre-procedural origin estimation. It uses a database of paced 12-lead ECGs with known pacing locations and presents its results on an imaging-based model of the patient. The method was tested using 7 realistic heart-torso models with hundreds of PVCs everywhere in the ventricles. We found that increasing the number of patients in the training database increased the accuracy of the predictions. The optimal number of pacing sites per patient in the training dataset was about 25, resulting in a prediction error around 15 mm. We conclude that our method gives a good indication to clinicians to efficiently start a pace-mapping during a catheter ablation procedure. It can be complemented with an intra-procedural method that uses the patient's own paced beats to refine the prediction.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"47 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":"115117312","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

Skin Segmentation for Imaging Photoplethysmography Using a Specialized Deep Learning Approach 基于深度学习方法的皮肤分割成像光容积脉搏波

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662682

Matthieu Scherpf, Hannes Ernst, Leo Misera, H. Malberg, Martin Schmidt

引用次数: 1

Leveraging Period-Specific Variations in ECG Topology for Classification Tasks 利用ECG拓扑的周期特定变化进行分类任务

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662895

Paul Samuel P. Ignacio

引用次数: 3

Automatic Diagnosis of Cardiac Disease from Twelve-Lead and Reduced-Lead ECGs Using Multilabel Classification 利用多标签分类从12导联和低导联心电图中自动诊断心脏病

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662899

P. Sundararajan, Kevin Moses, C. Potes, S. Parvaneh

引用次数: 0

U-Net Neural Network for Locating Midpoint of Insertion Zone of Transcatheter Aortic Valves in CTA Images 经导管主动脉瓣CTA图像插入区中点定位的U-Net神经网络

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662743

Eduardo Mineo, A. Assunção, T. Morais, S.F.C. Camara, H. Ribeiro, J. Sims, C. Nomura

引用次数: 0

Swarm Decomposition Enhances the Discrimination of Cardiac Arrhythmias in Varied-Lead ECG Using ResNet-BiLSTM Network Activations 利用ResNet-BiLSTM网络激活，群分解增强了多导联心电图心律失常的识别

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662742

M. Alkhodari, G. Apostolidis, Charilaos A. Zisou, L. Hadjileontiadis, A. Khandoker

{"title":"Swarm Decomposition Enhances the Discrimination of Cardiac Arrhythmias in Varied-Lead ECG Using ResNet-BiLSTM Network Activations","authors":"M. Alkhodari, G. Apostolidis, Charilaos A. Zisou, L. Hadjileontiadis, A. Khandoker","doi":"10.23919/cinc53138.2021.9662742","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662742","url":null,"abstract":"The standard screening tool for cardiac arrhythmias remains to be the 12-lead electrocardiography (ECG). Despite carrying rich information about different types of arrhythmias, it is considered bulky, high-cost, and often hard to use. In this study, we sought to investigate the efficiency of using 6-lead, 4-lead, 3 -lead, and 2-lead ECG in discriminating between 26 arrhythmia types and compare them with the standard 12-lead ECG. as part of PhysioNet/Computing in Cardiology 2021 Challenge. Our team, Care4MyHeart, developed a deep learning approach based on residual convolutional neural networks and Bi-directional long short term memory (ResNet-BiLSTM) to extract deep-activated features from ECG oscillatory components obtained using a novel swarm decomposition (SWD) algorithm. Alongside age and sex, these automated features were combined with hand-crafted features from heart rate variability and SWD components for training and classification. Our approach achieved a challenge score of 0.45, 0.43, 0.44, 0.43, and 0.42 using 10-fold cross-validation using the training set and 0.25, 0.23, 0.24, 0.22, and 0.20 using the hidden test set for 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead, respectively. Our team was ranked the 31/38 with an average all-lead test score of 0.22.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"23 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":"115194675","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

Electrocardiographic Imaging of Sinus Rhythm in Pig Hearts Using Bayesian Maximum A Posteriori Estimation 猪心脏窦性心律的贝叶斯最大后验估计心电图成像

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662881

Y. S. Dogrusoz, R. Dubois, E. Abell, M. Cluitmans, L. Bear

{"title":"Electrocardiographic Imaging of Sinus Rhythm in Pig Hearts Using Bayesian Maximum A Posteriori Estimation","authors":"Y. S. Dogrusoz, R. Dubois, E. Abell, M. Cluitmans, L. Bear","doi":"10.23919/cinc53138.2021.9662881","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662881","url":null,"abstract":"Background: Electrocardiographic imaging (ECGI) has potential to guide physicians to plan treatment strategies. Previously, Bayesian maximum a posteriori (MAP) estimation has been successfully applied to solve this inverse problem for paced data. In this study, we evaluate its effectiveness using experimental data in reconstructing sinus rhythm. Methods: Four datasets from Langendorff-perfused pig hearts, suspended in a human-shaped torso-tank, were used. Each experiment included 3–5 simultaneous electrogram (EGM) and body surface potential (BSP) recordings of 10 beats, in baseline and under dofetilide and pinacidil perfusion. Bayesian MAP estimation and Tikhonov regularization were used to solve the inverse problem. Prior models in MAP were generated using beats from the same recording but excluding the test beat. Pearson's correlation was used to evaluate EGM reconstructions, activation time (AT) maps, and gradient of ATs. Results: In almost all quantitative evaluations and qualitative comparisons of AT maps and epicardial breakthrough sites, MAP outperformed substantially better than Tikhonov regularization. Conclusion: These preliminary results showed that with a “good” prior model, MAP improves over Tikhonov regularization in terms of preventing misdiagnosis of conduction abnormalities associated with arrhythmogenic substrates and identifying epicardial breakthrough sites.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"30 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":"114462697","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

Hydroxychloroquine's Influence on Hypoxic and Hypokalemic ventricle: An Insilico Perspective 羟基氯喹对低氧和低钾心室的影响:一个独立的视角

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662782

P. Priya, Srinivasan Jayaraman

{"title":"Hydroxychloroquine's Influence on Hypoxic and Hypokalemic ventricle: An Insilico Perspective","authors":"P. Priya, Srinivasan Jayaraman","doi":"10.23919/cinc53138.2021.9662782","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662782","url":null,"abstract":"Hydroxychloroquine (HCQ) has been widely used, irrespective of pre reported cardiotoxicity. This demands further investigation on the mechanisms of HCQ interaction under hypoxia without and with a pro-arrhythmic comor-bidity like hypokalemia in the ventricular tissue as well as its effects when excited with premature beats (PBs) to understand the possibility of arrhythmic occurrence. This is made possible by configuring a 2D transmural anisotropic ventricular tissue model consisting of endocardial, mid-myocardial and epicardial myocytes for mild and severe hypoxia, hypokalemia and HCQ conditions. Results show that along with a QT interval reduction, low amplitude or T-wave inversion is observed in mild and severe hypoxia conditions respectively. No significant adverse effect of HCQ is observed in both cases. Under hypokalemia, mild hypoxia creates notched T-waves. Including HCQ has the effect of increasing the QT interval and T-peak. In presence of PBs, arrhythmia is generated only in presence of hypokalemia. Further, severe hypoxia causes inverted T-waves and a shortened QT-interval in hypokalemic comor-bid configuration. In presence of PBs, reentry is created only on addition of hypokalemia. When treated with HCQ, no notable changes occurred. This in-silico ventricular model indicates that HCQ treatment has no significant adverse effect in presence of hypokalemia and hypoxia, except in the combination of mild hypoxia with hypokalemia condition where it initiated a re-entrant arrhythmia pattern. These results could help guide treatment with HCQ.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"41 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":"117024925","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

Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads 基于自适应导联注意和输入卷积网络的心电异常识别

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662806

Tomáš Vičar, Petra Novotna, Jakub Hejc, O. Janousek, M. Ronzhina

引用次数: 2