International Conference on Computing in Cardiology最新文献

Deep Learning for Ventricular Arrhythmia Prediction Using Fibrosis Segmentations on Cardiac MRI Data 基于心脏MRI数据纤维化分割的室性心律失常深度学习预测

International Conference on Computing in Cardiology Pub Date : 2022-12-31 DOI: 10.22489/cinc.2022.191

"Florence E. van Lieshout, Roel Klein, Martin Kolk, Kylian van Geijtenbeek, Romy Vos, S. Ruipérez-Campillo, R. Feng, B. Deb, Prasanth Ganesan, R. Knops, I. Išgum, S. Narayan, E. Bekkers, B. D. Vos, F. Tjong

{"title":"Deep Learning for Ventricular Arrhythmia Prediction Using Fibrosis Segmentations on Cardiac MRI Data","authors":"\"Florence E. van Lieshout, Roel Klein, Martin Kolk, Kylian van Geijtenbeek, Romy Vos, S. Ruipérez-Campillo, R. Feng, B. Deb, Prasanth Ganesan, R. Knops, I. Išgum, S. Narayan, E. Bekkers, B. D. Vos, F. Tjong","doi":"10.22489/cinc.2022.191","DOIUrl":"https://doi.org/10.22489/cinc.2022.191","url":null,"abstract":"Many patients at high risk of life-threatening ventricular arrhythmias (VA) and sudden cardiac death (SCD) who received an implantable cardioverter defibrillator (ICD), never receive appropriate device therapy. The presence of fibrosis on LGE CMR imaging is shown to be associated with increased risk of VA. Therefore, there is a strong need for both automatic segmentation and quantification of cardiac fibrosis as well as better risk stratification for SCD. This study first presents a novel two-stage deep learning network for the segmentation of left ventricle myocardium and fibrosis on LGE CMR images. Secondly it aims to effectively predict device therapy in ICD patients by using a graph neural network approach which incorporates both myocardium and fibrosis features as well as the left ventricle geometry. Our segmentation network outperforms previous state-of-the-art methods on 2D CMR data, reaching a Dice score of 0.82 and 0.77 on myocardium and fibrosis segmentation, respectively. The ICD therapy prediction network reaches an AUC of 0.60 while using only CMR data and outperforms baseline methods based on current guideline markers for ICD implantation. This work lays a strong basis for future research on improved risk stratification for VA and SCD","PeriodicalId":176425,"journal":{"name":"International Conference on Computing in Cardiology","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593606","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

A Wavelet-Based Approach for Automatic Diagnosis of Strict Left Bundle Branch Block 基于小波的严格左束分支块自动诊断方法

International Conference on Computing in Cardiology Pub Date : 2018-12-30 DOI: 10.22489/CinC.2018.154

Alba Martín, J. P. Martínez

{"title":"A Wavelet-Based Approach for Automatic Diagnosis of Strict Left Bundle Branch Block","authors":"Alba Martín, J. P. Martínez","doi":"10.22489/CinC.2018.154","DOIUrl":"https://doi.org/10.22489/CinC.2018.154","url":null,"abstract":"Cardiac resynchronization therapy (CRT) is widely used in heart failure patients with left bundle branch block (LBBB). However, the high false-positive rates obtained with the conventional LBBB criteria limit the effectiveness of this therapy. This has yielded to the definition of a new stricter criteria for diagnosis. The aim of this work was to develop and assess a fully-automatic algorithm for strict LBBB diagnosis. Twelve-lead, high-resolution, 10-second ECGs from 602 patients enrolled in the MADIT-CRT trial were available. Data were labelled for strict LBBB by 2 experts and divided into training (n=300) and validation (n=302, blind annotations to the investigators) sets for assessing algorithm performance. After QRS detection, a wavelet-based delineator was used to detect individual Q-R-S waves, QRS onsets and ends, and identify the type of QRS pattern on each standard lead. Then, multilead QRS boundaries were determined in order to compute the QRS width. Finally, an automatic algorithm for notch/slur detection within the QRS complex was applied based on the same wavelet approach used for delineation. In the validation set, LBBB was diagnosed with a sensitivity and specificity of Se=92.9% and Sp=65% (Acc=79%, PPV=73.9% and NPV=89.6%). Results confirmed an accurate diagnosis of strict LBBB based on a fully-automatic extraction of temporal and morphological QRS features.","PeriodicalId":176425,"journal":{"name":"International Conference on Computing in Cardiology","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124419381","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

Detection of Fibrosis in LGE-Cardiac MRI using Kernel DL-based Clustering 基于Kernel dl聚类的心肌大磁共振成像纤维化检测

International Conference on Computing in Cardiology Pub Date : 1900-01-01 DOI: 10.1109/CIC.2015.7408660

J. Mantilla, J. Paredes, J. Bellanger, J. Betancur, F. Schnell, C. Leclercq, M. Garreau

{"title":"Detection of Fibrosis in LGE-Cardiac MRI using Kernel DL-based Clustering","authors":"J. Mantilla, J. Paredes, J. Bellanger, J. Betancur, F. Schnell, C. Leclercq, M. Garreau","doi":"10.1109/CIC.2015.7408660","DOIUrl":"https://doi.org/10.1109/CIC.2015.7408660","url":null,"abstract":"In this paper we address the problem of fibrosis detection in patients with Hypertrophic cardiomyopathy (HCM) by using a sparse-based clustering approach and Dictionary learning. HCM, as a common cardiovascular disease, is characterized by the abnormal thickening, architectural disorganization and the presence of fibrosis in the left ventricular myocardium. Myocardial fibrosis in HCM leads to both systolic and diastolic dysfunction. It can be detected in Late Gadolinium Enhanced (LGE) cardiac magnetic resonance imaging. We present the use of a Dictionary Learning (DL)-based clustering technique for the detection of fibrosis in LGE-Short axis (SAX) images. The DL-based detection approach consists in two stages: the construction of one dictionary with samples from 2 clusters (LGE and Non-LGE regions) and the use of sparse coefficients of the input data obtained with a kernel-based DL approach to train a K-Nearest Neighbor (K-NN) classifier. The label of a test patch is obtained with its respective sparse coefficients obtained over the learned dictionary and using the trained K-NN classifier. The method has been applied on 11 patients with HCM providing good results.","PeriodicalId":176425,"journal":{"name":"International Conference on Computing in Cardiology","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122951268","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

Aging Changes in the Regularity of Hemodynamic Parameters during Six-Minute Walk Test 6分钟步行试验中血流动力学参数规律的衰老变化

International Conference on Computing in Cardiology Pub Date : 1900-01-01 DOI: 10.1109/CIC.2015.7411101

Marcos Hortelano, R. Reilly, R. Abad

引用次数: 0