2020 Computing in Cardiology最新文献_第2页

The Influence of Cardiac Ablation on the Electrophysiological Characterization of Rat Isolated Atrium: Preliminary Analysis 心脏消融对大鼠离体心房电生理特性的影响:初步分析

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.265

J. Paredes, S. Pollnow, O. Dössel, J. Salinet

{"title":"The Influence of Cardiac Ablation on the Electrophysiological Characterization of Rat Isolated Atrium: Preliminary Analysis","authors":"J. Paredes, S. Pollnow, O. Dössel, J. Salinet","doi":"10.22489/CinC.2020.265","DOIUrl":"https://doi.org/10.22489/CinC.2020.265","url":null,"abstract":"Atrial fibrillation (AF) is the most common cardiac arrhythmia seen in clinical practice and its treatment by antiarrhythmic drugs is still non-effective. Radiofrequency catheter ablation (RFA) has been widely accepted as a strategy to prevent AF by creating myocardial lesions to block the AF electrical wavefront propagation and eliminate arrhythmogenic tissue. In this study, we analyzed the electrophysiological impact of different RFA time duration strategies through a controlled animal protocol. Electrical activity of the isolated right atrium of rats, under different RFA time strategies on the epicardium, was acquired during 4 s on the endocardium by electrical Mapping (EM) and simultaneously on the endocardium by Optical Mapping (OM), respectively. Analyses were concentrated on both time and frequency domain, through analysis of sig-nal's morphology, local activation time, conduction velocity, dominant frequency (DF), and organization index (OI). The morphology of the optical and electrical signals was altered as the ablation time increased, making it difficult to identify activation times. Moreover, DF and OI decreased with increasing ablation time implied in fragmented electrograms. Through the characterization of traditional metrics applied to the electrical and optical data, it was possible to identify important changes, in time and frequency, inside the ablated regions.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"48 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":"123769467","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

Bag of Tricks for Electrocardiogram Classification With Deep Neural Networks 用深度神经网络进行心电图分类的技巧包

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.328

Seonwoo Min, Hyun-Soo Choi, Hyeongrok Han, Minji Seo, Jinkook Kim, Junsang Park, Sunghoon Jung, I. Oh, Byunghan Lee, Sungroh Yoon

引用次数: 10

Robust Atrial Ectopic Beat Classification From Surface ECG Using Second-Order Blind Source Separation 基于二阶盲源分离的体表心电图鲁棒心房异位心跳分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.473

Yingjing Feng, C. Roney, M. Hocini, S. Niederer, E. Vigmond

引用次数: 1

Can Laplacian Eigenmaps Be Used for Differentiation Between Healthy Subjects and Patients With Corrected Tetralogy of Fallot? 拉普拉斯特征映射可以用于区分健康人与法洛四联症患者吗?

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.119

B. Jacobs, Amalia Villa Gómez, Jonathan Moeyersons, S. Huffel, R. Willems, C. Varon

{"title":"Can Laplacian Eigenmaps Be Used for Differentiation Between Healthy Subjects and Patients With Corrected Tetralogy of Fallot?","authors":"B. Jacobs, Amalia Villa Gómez, Jonathan Moeyersons, S. Huffel, R. Willems, C. Varon","doi":"10.22489/CinC.2020.119","DOIUrl":"https://doi.org/10.22489/CinC.2020.119","url":null,"abstract":"Tetralogy of Fallot (ToF) is a congenital structural heart disease. While early diagnosis and corrective surgery allow most patients to live normal lives, some patients slowly deteriorate. The current inability to quantify the deterioration and predict these events prompts a data driven approach. Laplacian Eigenmaps (LEs) are a dimensionality reduction technique that can be used to project multi-lead ECGs onto a lower dimensional space. This pilot study aims to evaluate the ability of LEs to characterize deterioration of ToF patients. A general LE model is constructed, based on the 12-lead ECG recordings of 20 healthy controls. A set of distance metrics are developed to quantify the overall changes between different ECG recordings within this LE model. Statistically significant differences between control and ToF subjects were observed for most of the distance metrics. The analysis of changes over time in ToF patients indicates a general trend of increased distance over time in all the metrics, which can be related to a worsening condition. This indicates the relevance of LEs in multi-lead ECG processing, particularly for deterioration analysis.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"130 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":"122642877","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

A Novel Approach Based on Spatio-temporal Features and Random Forest for Scar Detection Using Cine Cardiac Magnetic Resonance Images 基于时空特征和随机森林的心脏磁共振图像疤痕检测新方法

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.050

S. Moccia, A. Cagnoli, C. Martini, Giuseppe Moscogiuri, M. Pepi, E. Frontoni, G. Pontone, E. Caiani

引用次数: 2

Human Ventricular Modelling and Simulation of Drug Action on Electrophysiology and Contraction 药物对人体心室电生理和收缩作用的建模和模拟

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.386

F. Margara, Z. Wang, A. Bueno-Orovio, B. Rodríguez

{"title":"Human Ventricular Modelling and Simulation of Drug Action on Electrophysiology and Contraction","authors":"F. Margara, Z. Wang, A. Bueno-Orovio, B. Rodríguez","doi":"10.22489/CinC.2020.386","DOIUrl":"https://doi.org/10.22489/CinC.2020.386","url":null,"abstract":"Drug safety and efficacy assessment remains as one of the biggest challenges in both preclinical and clinical drug development. Cardiac adverse outcomes may emerge even though they did not occur in early stages of drug development. Among them, the prediction of drug action on cardiac contraction and electrophysiology is especially complex. Human in-silico drug trials constitute a powerful methodology for their investigation and can integrate and augment biophysically detailed experimental information. In this study, we present an integrated modelling and simulation framework for the simultaneous assessment of electrophysiological and contractile effects of drug action in human cardiac function. We analyse both pure potassium and calcium channels blockers, given their prevailing use in clinical practice. Simulation results demonstrate the positive inotropic effect of potassium blockers, with the potential occurrence of contractile abnormalities triggered by repolarisation abnormalities, and the dose-dependent negative inotropic effect of calcium blockers. This study demonstrates the translational and preclinical potential of human-based in-silico drug trials to investigate drug-induced effects on human cardiac electromechanical function.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"100 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":"122913811","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

Computational Analysis of Vulnerability to Reentry in Acute Myocardial Ischemia 急性心肌缺血再入易损性的计算分析

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.241

Edison F. Carpio, J. Gomez, José F. Rodríguez-Matas, B. Trénor, José M. Ferrrero

引用次数: 1

Supervised Classification of Ventricular Abnormal Potentials in Intracardiac Electrograms 心内心电图中心室异常电位的监督分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.397

Giulia Baldazzi, M. Orrù, M. Matraxia, G. Viola, D. Pani

{"title":"Supervised Classification of Ventricular Abnormal Potentials in Intracardiac Electrograms","authors":"Giulia Baldazzi, M. Orrù, M. Matraxia, G. Viola, D. Pani","doi":"10.22489/CinC.2020.397","DOIUrl":"https://doi.org/10.22489/CinC.2020.397","url":null,"abstract":"Ventricular abnormal potentials (VAPs) identification is a challenging issue, since they constitute the ablation targets in substrate-guided mapping and ablation procedures for ventricular tachycardia (VT) treatment. In this work, two approaches for the supervised classification of VAPs in bipolar intracardiac electrograms are evaluated and compared. To this aim, 954 bipolar electrograms were retrospectively annotated by an expert cardiologist. All signals were acquired from six patients affected by post-ischemic VT by the CARTO3 system at the San Francesco Hospital (Nuoro, Italy) during routine procedures. The first classification approach was based on a support vector machine trained and tested on four different features, extracted from both the time and time-scale domain, to identify physiological and abnormal potentials. Conversely, in order to assess the significance of the first approach and its features, in the second approach all the samples constituting a time-domain segment of each bipolar electrogram were given as input to a feed-forward artificial neural network. In both cases, the accuracy in VAPs and physiological potentials identification exceeded 79%, suggesting their efficacy and the possibility of VAPs automatic recognition without identifying peculiar features.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"239 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":"114599307","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

Sleep-Wake Classification for Home Monitoring of Sleep Apnea Patients 睡眠呼吸暂停患者家庭监测的睡眠-觉醒分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.147

Dorien Huysmans, Eva Heffinck, I. Castro, Margot Deviaene, Pascal Borzée, B. Buyse, D. Testelmans, S. Huffel, C. Varon

{"title":"Sleep-Wake Classification for Home Monitoring of Sleep Apnea Patients","authors":"Dorien Huysmans, Eva Heffinck, I. Castro, Margot Deviaene, Pascal Borzée, B. Buyse, D. Testelmans, S. Huffel, C. Varon","doi":"10.22489/CinC.2020.147","DOIUrl":"https://doi.org/10.22489/CinC.2020.147","url":null,"abstract":"Sleep apnea is a common sleep disorder, whose diagnosis can strongly benefit from home-based screening. As the total sleep time is essential to assess the sleep apnea severity, a sleep-wake classifier was developed based on heart rate and respiration. These two signals were selected as they can be measured using unobtrusive sensors. A 1D convolutional neural network (CNN) was designed to classify 30s epochs of tachograms and respiratory inductance plethysmography (RIP) signals. The input based on beat-to-beat variability allows the use of different sensor types. A dataset of 56 patients with an apnea-hypopnea index (AHI) below 10 was used to train and validate the network. This CNN was applied to an independent test set of ECG and RIP signals of 25 subjects. Of these, 8 subjects were simultaneously monitored using an unobtrusive capacitive-coupled ECG (ccECG) sensor integrated in a mattress. Artefact removal and data correction was performed on this acquired data. The performance on the independent dataset of ECG and RIP is comparable to state-of-the-art, with κ = 0.48. However, application on the ccECG data resulted in a drop in performance, with κ = 0.30. This was caused by a low amount of remaining wake epochs after data cleaning. Importantly, the network classified 30s segments of sleep apnea patients, without relying on past or future information for feature extraction.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"16 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":"121839757","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

Automated Atrial Fibrillation Source Detection Using Shallow Convolutional Neural Networks 基于浅卷积神经网络的房颤源自动检测

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.385

Isac N. Lira, Pedro Marinho R. de Oliveira, Walter Freitas, V. Zarzoso

引用次数: 1