2020 Computing in Cardiology最新文献_第8页

Recursive Model Identification for the Analysis of Cardiovascular Autonomic Modulation During Epileptic Seizure 递归模型辨识分析癫痫发作时心血管自主神经调节

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.206

Quentin Gillardin, V. Rolle, Anca Nica, A. Biraben, Benoît Martin, Alfredo I. Hernández

{"title":"Recursive Model Identification for the Analysis of Cardiovascular Autonomic Modulation During Epileptic Seizure","authors":"Quentin Gillardin, V. Rolle, Anca Nica, A. Biraben, Benoît Martin, Alfredo I. Hernández","doi":"10.22489/CinC.2020.206","DOIUrl":"https://doi.org/10.22489/CinC.2020.206","url":null,"abstract":"Significant cardio-respiratory fluctuations are often observed during and after an epileptic seizure event. The mechanisms underlying these acute modifications are considered to be involved in sudden and unexpected death in epilepsy (SUDEP). We hypothesize that these acute events are mediated by specific dynamics of the autonomic nervous system (ANS). However, the evaluation of the ANS during seizures remains particularly challenging, mainly due to the lack of observability. Computational modelling could help to override these limitations, to assess ANS modulation and to evaluate this hypothesis. In this study, we propose and apply a recursive identification algorithm of a system-level model of the autonomic modulation of the sino-atrial node, integrating a Tikhonov regularization, in order to assess sympathetic and parasympathetic activities during ictal tachy-bradycardia events. We evaluate the feasibility of the method on heart rate (HR) data from 4 seizures observed in the same patient. After parameter optimization and identification we were able to reproduce observed HR data with a maximum root mean squared error equals to 1.7bpm. The estimated autonomic series show sympathetic activation and parasympathetic inhibition at the seizure onset, and a massive vagal discharge as the leading factor to ictal bradycardia.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"23 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":"114477738","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

Will Genetic Data Significantly Change Cardiovascular Risk Prediction in Daily Practice? 基因数据会显著改变日常心血管风险预测吗?

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.481

W. Young, J. Ramírez, S. Duijvenboden, A. Tinker, P. Lambiase, P. Munroe, M. Orini

引用次数: 0

Breakthrough Wave Detection in a 3D Computer Model of Atrial Endo-Epicardial Dissociation 心房心外膜内游离三维计算机模型的突破波检测

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.425

Eric Irakoze, V. Jacquemet

{"title":"Breakthrough Wave Detection in a 3D Computer Model of Atrial Endo-Epicardial Dissociation","authors":"Eric Irakoze, V. Jacquemet","doi":"10.22489/CinC.2020.425","DOIUrl":"https://doi.org/10.22489/CinC.2020.425","url":null,"abstract":"Experimental and clinical mapping of atrial fibrillation has revealed the occurrence of breakthrough activation patterns. These focal waves have been associated with endo-epicardial (endo-epi) dissociation and three-dimensional anatomical structures. To assess breakthrough detection techniques in computer models of atrial fibrillation, we created a 3D cubic-mesh atrial model with locally controllable endo-epi dissociation. In this model, epi and endo layers were electrically coupled only at randomly-distributed discrete connection sites. Eighteen endo-epi connection patterns were generated. Dedicated finite-difference numerical methods were developed to handle these discontinuities in conduction. These configurations were designed to generate breakthroughs at predictable locations. We developed a breakthrough detection algorithm based on full-resolution activation maps of both the epi- and endocardial surfaces. Wave tracking was used to calculate the lifespan of breakthroughs. Non-propagating passive responses and breakthroughs with too short lifespan were eliminated. The approach was manually and automatically validated in 48 episodes of fibrillation in models with varying number of endo-epi connections.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"8 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":"117049391","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

Identification of Ablation Sites in Persistent Atrial Fibrillation Based on Spatiotemporal Dispersion of Electrograms Using Machine Learning 基于电图时空弥散的持续性心房颤动消融部位的机器学习识别

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.221

Amina Ghrissi, F. Squara, J. Montagnat, V. Zarzoso

引用次数: 0

Multi-Class Classification of Pathologies Found on Short ECG Signals 短心电信号病理的多类别分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.071

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

{"title":"Multi-Class Classification of Pathologies Found on Short ECG Signals","authors":"G. Nalbantov, Svetoslav Ivanov, J. V. Prehn","doi":"10.22489/CinC.2020.071","DOIUrl":"https://doi.org/10.22489/CinC.2020.071","url":null,"abstract":"The ability to detect several key cardiac pathologies simultaneously, based on ECG signals, is key towards establishing a real-world application of AI models in cardiology. Such a multi-label classification task requires not only well-performing binary classification models, but also a way to combine such models into an overall classification modeling structure. We have approached this task using materials from Classification of 12-1ead ECGs for the PhysioNet/Computing in Cardiology Challenge 2020. Duplicate ECG strips have been removed. An annotation tool for labeling ECG wave points and intervals/templates has been created in MATLAB®, and used for labeling pathological intervals, as well as noisy intervals and inconsistencies between the ECG data and the pre-assigned labels. Several one-vs-rest binary classifiers were built, where morphological features specific to each pathology had been generated from the signals. The binary classifiers were augmented by a multi-class classifier using an Error Correcting Output Codes (ECOC) methodology. Our approach achieved a challenge validation score of 0.616, and full test score of 0.194, placing us 23 (team DSC) out of 41 in the official ranking.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"46 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":"121389961","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

Multi-Label Classification of 12-lead ECGs by Using Residual CNN and Class-Wise Attention 基于残差CNN和分类注意的12导联心电图多标签分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.285

Yang Liu, Kuanquan Wang, Yongfeng Yuan, Qince Li, Yacong Li, Yongpeng Xu, Henggui Zhang

引用次数: 3

Proarrhythmia in KCNJ2 E299V-linked Short QT Syndrome: A Simulation Study KCNJ2 e299v相关短QT综合征的心律失常原:一项模拟研究

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.432

Cunjin Luo, Tong Liu, Ying He, Kuanquan Wang, Henggui Zhang

引用次数: 0

More Reliable Remote Heart Rate Measurement by Signal Quality Indexes 基于信号质量指标的更可靠的远程心率测量

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.165

Hannes Ernst, H. Malberg, Martin Schmidt

引用次数: 3

An Impedance-based Algorithm to Detect Ventilations During Cardiopulmonary Resuscitation 一种基于阻抗的心肺复苏通气检测算法

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.325

X. Jaureguibeitia, U. Irusta, E. Aramendi, He Wang, A. Idris

{"title":"An Impedance-based Algorithm to Detect Ventilations During Cardiopulmonary Resuscitation","authors":"X. Jaureguibeitia, U. Irusta, E. Aramendi, He Wang, A. Idris","doi":"10.22489/CinC.2020.325","DOIUrl":"https://doi.org/10.22489/CinC.2020.325","url":null,"abstract":"Cardiopulmonary resuscitation (CPR) is a core therapy to treat out-of-hospital cardiac arrest (OHCA). Thoracic impedance (TI) can be used to assess ventilations during CPR, but the signal is also affected by chest compression (CC) artifacts. This study presents a method for TI-based ventilation detection during concurrent manual CCs. Data from 152 OHCA patients were analyzed. A total of 423 TI segments of at least 60 s during ongoing CCs were extracted. True ventilations were annotated using the capnogram. The final dataset comprised 1210 min of TI recordings and 9665 ground truth ventilations. A three-stage detection algorithm was developed. First, the TI signal was filtered to obtain ventilation waveforms, including a least mean squares filter to remove artifacts due to CCs. Potential ventilations were then identified with a heuristic detector and characterized by a set of 16 features. These were finally fed to a random forest classifier to discriminate between true ventilations and false positives. Patients were split into 100 distinct training (70%) and test (30%) partitions. The median (interquartile range) sensitivity, PPV and F-score were 83.9 (70.2-91.2) %, 86.1 (75.0-93.3) % and 84.3 (72.1-91.4) %. Our method would allow feedback on ventilation rates as well as surrogate measures of insufflated air volume during CPR.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"114 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":"128277540","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

Analysis of T Wave Nonlinear Dynamics for Serum Potassium Monitoring in End-Stage Renal Disease Patients 终末期肾病患者血钾监测的T波非线性动力学分析

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.461

S. Srinivasan, Hassaan A. Bukhari, P. Laguna, C. Sánchez, E. Pueyo

{"title":"Analysis of T Wave Nonlinear Dynamics for Serum Potassium Monitoring in End-Stage Renal Disease Patients","authors":"S. Srinivasan, Hassaan A. Bukhari, P. Laguna, C. Sánchez, E. Pueyo","doi":"10.22489/CinC.2020.461","DOIUrl":"https://doi.org/10.22489/CinC.2020.461","url":null,"abstract":"Non-invasive estimation of serum potassium, [K+], is of major importance to prevent associated risks, but current ambulatory estimation methods are limited. We investigated changes in T wave nonlinear dynamics by quantifying a divergence-related marker ψ on electrocardiograms (ECGs) from 15 end-stage renal disease (ESRD) patients undergoing hemodialysis (HD) and we assessed the relationship between ψ and [K+]. ECGs from 22 simulated transmural ventricular fibers were additionally calculated. In ESRD patients, ψ took the largest values at the beginning and end of the HD session, corresponding to the highest and lowest [K+] values. The median correlation coefficient over patients between the change in ψ and the change in [K+] was 0.92 and decreased to 0.74 after controlling for the effects of [Ca2+] and heart rate. These associations were, however, highly patient-dependent. Both the strength and variability of the ψ -[K+] relationship was reproduced in the simulations, with the variability explained by differences in transmural heterogeneities: 10% variations in the proportion of epicardial and midmyocardial cells led to more than 10% and 8% changes in ψ, respectively. In conclusion, changes in the nonlinear dynamics of the ECG T waves can be related to [K+] variations in ESRD patients, despite the high inter-individual variability.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"13 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":"124563683","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: 4