2020 Computing in Cardiology最新文献_第7页

Performance Comparison of Deep Learning Approaches for Left Atrium Segmentation From LGE-MRI Data 基于LGE-MRI数据的左心房分割深度学习方法的性能比较

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.306

D. Borra, Daniela Portas, A. Andalò, C. Fabbri, C. Corsi

{"title":"Performance Comparison of Deep Learning Approaches for Left Atrium Segmentation From LGE-MRI Data","authors":"D. Borra, Daniela Portas, A. Andalò, C. Fabbri, C. Corsi","doi":"10.22489/CinC.2020.306","DOIUrl":"https://doi.org/10.22489/CinC.2020.306","url":null,"abstract":"Quantification of viable left atrial (LA) tissue is a reliable information which should be used to support therapy selection in atrial fibrillation (AF) patients. Late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is employed for the non-invasive assessment of LA fibrotic tissue. Unfortunately, the analysis of LGE-MRI relies on manual tracing of LA boundaries. This task is time-consuming and prone to high inter-observer variability. Therefore, an automatic approach for LA wall detection would be very helpful. In this study, we compared the performance of different deep architectures - U-Net and attention U-Net (AttnU-Net) - and different loss functions - Dice loss (DL) and focal Tversky loss (FTL) to automatically detect LA boundaries from LGE-MRI data. In addition, AttnU-Net was trained without deep supervision (DS) and multi-scale inputs (MI), with DS and with DS+MI. No statistically significant differences were found training the networks with DL or FTL. U-Net was the best-performing algorithm overall, outperforming significantly AttnU-Net with a Dice Coefficient of 0.9015±0.0308 (mean ± standard deviation). However, no significant differences were found between U-Net and AttnU-Net DS/DS+MI. Based on these results, using a DL or FTL does not affect the performance and U-Net was the best-performing solution.","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":"116782873","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

Atrial Fibrillation Driver Localization From Body Surface Potentials Using Deep Learning 基于体表电位的深度学习心房颤动驱动定位

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.383

Miguel Angel Cámara-Vázquez, Adrián Oter-Astillero, I. Hernández-Romero, Miguel, Rodrigo, Eduardo Morgado-Reyes, S. Guillem, Ó. Barquero-Pérez

{"title":"Atrial Fibrillation Driver Localization From Body Surface Potentials Using Deep Learning","authors":"Miguel Angel Cámara-Vázquez, Adrián Oter-Astillero, I. Hernández-Romero, Miguel, Rodrigo, Eduardo Morgado-Reyes, S. Guillem, Ó. Barquero-Pérez","doi":"10.22489/CinC.2020.383","DOIUrl":"https://doi.org/10.22489/CinC.2020.383","url":null,"abstract":"Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns. Multipoint intracardiac mapping systems present a limited spatial resolution, which makes it difficult to identify AF drivers and ablation targets. These AF onset locations and drivers responsible for AF perpetuation are main targets for ablation procedures. Although noninvasive electrocardiographic imaging (ECGI) and inverse problem-based methods have been tested during AF conditions, they need an accurate mathematical modeling of atria and torso to get good results. In this work, we propose to model the location of AF drivers from body surface potentials (BPS) as a supervised classification problem. We used deep learning techniques to address the problem. We were able to correctly locate the 92% and 96% of drivers in the test and training sets, respectively (accuracy of 0.92 and 0.96), while the Cohen's Kappa was 0.89 for both sets. Therefore, proposed method can help to identify target regions for ablation using a noninvasive procedure as BSP mapping.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"89 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":"114290250","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

ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function 基于全局跳跃连接和自定义损失函数的卷积网络心电异常识别

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.189

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

引用次数: 11

Selected Features for Classification of 12-lead ECGs 12导联心电图分类的选择特征

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.061

M. Żyliński, G. Cybulski

引用次数: 2

Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation From the ECG 从心电图中寻找维持心房颤动的转子区域的机器学习

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.181

G. Luongo, L. Azzolin, M. Rivolta, T. Almeida, J. P. Martínez, D. Soriano, O. Dössel, R. Sassi, P. Laguna, A. Loewe

{"title":"Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation From the ECG","authors":"G. Luongo, L. Azzolin, M. Rivolta, T. Almeida, J. P. Martínez, D. Soriano, O. Dössel, R. Sassi, P. Laguna, A. Loewe","doi":"10.22489/CinC.2020.181","DOIUrl":"https://doi.org/10.22489/CinC.2020.181","url":null,"abstract":"Atrial fibrillation (AF) is the most frequent irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. The non-invasive localization of AF drivers can lead to improved personalized ablation strategy, suggesting pulmonary vein (PV) isolation or more complex extra-PV ablation procedures in case the driver is on other atrial regions. We used a Machine Learning approach to characterize and discriminate simulated single stable rotors (1R) location: PVs, left atrium (LA) excluding the PVs, and right atrium (RA), utilizing solely non-invasive signals (i.e., the 12-lead ECG). 1R episodes sustaining AF were simulated. 128 features were extracted from the signals. Greedy forward algorithm was implemented to select the best feature set which was fed to a decision tree classifier with hold-out cross-validation technique. All tested features showed significant discriminatory power, especially those based on recurrence quantification analysis (up to 80.9% accuracy with single feature classification). The decision tree classifier achieved 89.4% test accuracy with 18 features on simulated data, with sensitivities of 93.0%, 82.4%, and 83.3% for RA, LA, and PV classes, respectively. Our results show that a machine learning approach can potentially identify the location of 1R sustaining AF using the 12-lead ECG.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"79 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":"134016664","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

Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals 神经结构设计对12导联心电信号心脏异常分类的影响

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.161

Najmeh Fayyazifar, Selam T. Ahderom, D. Suter, A. Maiorana, G. Dwivedi

引用次数: 6

Sudden Cardiac Death Prediction in Chronic Heart Failure Patients by Periodic Repolarization Dynamics 周期性复极化动力学预测慢性心力衰竭患者心源性猝死

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.209

S. Palacios, I. Cygankiewicz, A. Luna, J. P. Martínez, E. Pueyo

{"title":"Sudden Cardiac Death Prediction in Chronic Heart Failure Patients by Periodic Repolarization Dynamics","authors":"S. Palacios, I. Cygankiewicz, A. Luna, J. P. Martínez, E. Pueyo","doi":"10.22489/CinC.2020.209","DOIUrl":"https://doi.org/10.22489/CinC.2020.209","url":null,"abstract":"Chronic heart failure (CHF) is a clinical syndrome associated with high mortality due to pump failure death (PFD) resulting from heart failure progression as well as to ventricular arrhythmias leading to sudden cardiac death (SCD). CHF involves autonomic nervous system imbalance, which is expected to be reflected in the electrocardiogram (ECG). Periodic Repolarization Dynamics (PRD) quantifies low-frequency oscillations in the T wave of the ECG and has been related to sympathetic modulation of ventricular repolarization. We assessed the capacity of PRD to predict PFD and SCD in a CHF population. 3-lead ECG recordings of 569 patients with symptomatic CHF were analyzed. PRD values were measured by analyzing 5-minute segments with 4-minute overlap. The minimum PRD value over the analyzed segments was assigned to each patient. PRD was higher in SCD victims than in PFD victims and than in survivors and non-cardiac death victims, although differences were not statistically significant. Low- and high-risk groups were defined by dichotomization according to median PRD in the population. Hazard ratio [95% Confidence Interval] for SCD from univariate Cox regression was 1.808 [1.031-3.169] deg (p=0.039). In conclusion, high PRD predicts SCD in a CHF population, with SCD victims presenting enhanced sympathetic-induced oscillations of ventricular repolarization.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"32 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":"134329399","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

Impact of Electrode Size on Electrogram Voltage in Healthy and Diseased Tissue 电极尺寸对健康和病变组织中电图电压的影响

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.146

D. Nairn, Daniel Hunyar, Jorge Sánchez, O. Dössel, A. Loewe

{"title":"Impact of Electrode Size on Electrogram Voltage in Healthy and Diseased Tissue","authors":"D. Nairn, Daniel Hunyar, Jorge Sánchez, O. Dössel, A. Loewe","doi":"10.22489/CinC.2020.146","DOIUrl":"https://doi.org/10.22489/CinC.2020.146","url":null,"abstract":"Atrial fibrillation can be treated using low voltage (LV) (amplitude of intracardiac electrogram < 0.5mV) targeted ablation. However, catheter characteristics can alter the voltage leading to changes in identified LV areas. This study evaluates the impact electrode size has on the voltage in healthy and diseased tissue. A realistic setup was generated of tissue, bath and two high conductivity electrodes, with centre to centre spacing of 2mm, placed in contact to the tissue and perpendicular to the planar wavefront. Simulations were performed varying the dimensions of the cubic electrodes from 0.2 to 1.6 mm in healthy tissue and including fibrosis in different locations. An inverse relationship was found between the electrode size and the voltage. When including epicardial fibrosis, a voltage decrease of 1 mV was found in electrodes. When fibrosis was placed closer to the electrodes, a morphological signal change was seen and a 9 mV drop in voltage for small electrodes. Large electrodes deliver smaller voltages. A fibrotic area on the epicardial side has a small influence on the voltage, which was not amplified by increasing electrode size. Endocardial fibrosis delivers significantly smaller voltages than healthy tissue. Little difference in the voltage was seen between large electrodes (> 1 mm) in diseased tissue. Electrode size needs to be accounted for when determining LV areas using different catheters.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"599 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":"133374647","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

Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks 用深度卷积神经网络检测不同长度多通道心电图记录的心律失常

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.339

M. Sallem, Amina Ghrissi, Adnen Saadaoui, V. Zarzoso

引用次数: 2

Evaluation of Severity of Cardiac Ischemia Using In Silico ECG Computed From 2D Reaction Diffusion Model 用二维反应扩散模型计算心电图评价心脏缺血严重程度

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.033

S. Loeffler, J. Starobin

引用次数: 0