2019 Computing in Cardiology Conference (CinC)最新文献_第2页

Automatic Emotions Assessment Using Heart Rate Variability Analysis and 2D Regression Model of Emotions 基于心率变异性分析和情绪二维回归模型的自动情绪评估

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.356

S. Moharreri, Shahab Rezaei, N. Jafarnia Dabanloo, S. Parvaneh

引用次数: 0

A Framework of Left Atrium Segmentation on CT Images with Combined Detection Network and Level Set Model 结合检测网络和水平集模型的CT图像左心房分割框架

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.240

Yashu Liu, Kuanquan Wang, Gongning Luo, Henggui Zhang

{"title":"A Framework of Left Atrium Segmentation on CT Images with Combined Detection Network and Level Set Model","authors":"Yashu Liu, Kuanquan Wang, Gongning Luo, Henggui Zhang","doi":"10.22489/cinc.2019.240","DOIUrl":"https://doi.org/10.22489/cinc.2019.240","url":null,"abstract":"In this paper, we proposed a framework for left atrium (LA) segmentation on CT with combined detection network and level set model. The proposed framework consists of two steps. Firstly, we trained a Faster RCNN to generate location boxes for LA. The obtained location box can remove unrelated regions to reduce the interference of background and similarity tissues. Secondly, we utilized a self-adapted threshold on the location box to get the initialization for the level set model, which is nearer the LA and more robust than the random and fixed initialization. Then we proposed a 3D level set model with a new edge indicator based on DRLSE for the final LA segmentation. This edge indicator incorporated both numerical and direction information of the data gradient. Hence, the proposed level set model can guide the contour to the correct boundary when there are many boundaries surrounded the object. The framework was trained and evaluated on MICCAI 2013 LA segmentation challenge. The proposed segmentation method achieved the Dice score of 86.46%. Comparing to the original DRLSE, it achieved a 2.72% improvement on the Dice score.","PeriodicalId":6716,"journal":{"name":"2019 Computing in Cardiology Conference (CinC)","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78123947","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

Myofibroblasts Alter Tension and Strain of Cardiac Fiber: A Computational Study 肌成纤维细胞改变心脏纤维的张力和应变:一个计算研究

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.007

Zhan Heqing, Zhang Jingtao

{"title":"Myofibroblasts Alter Tension and Strain of Cardiac Fiber: A Computational Study","authors":"Zhan Heqing, Zhang Jingtao","doi":"10.22489/cinc.2019.007","DOIUrl":"https://doi.org/10.22489/cinc.2019.007","url":null,"abstract":"In heart pathological conditions, fibroblasts proliferate and differentiate into myofibroblasts (Mfbs). This study aimed to investigate the role of Mfbs on the mechanical contraction of cardiac fiber. Mathematical modeling was done using a combination of (1) the Maleckar et al. model of the human atrial myocyte, (2) the MacCannell et al. active model of the human cardiac Mfb, (3) our formulation of INa_myofb based upon experimental findings from Chatelier et al., and (4) the Hill three-element rheological scheme of a single segment of cardiac fiber. For Mfb-myocyte coupling, different ratios of myocytes to Mfbs and gap-junctional conductances were set based on available physiological data. Both isometric contraction and isotonic contraction were considered to illustrate the effect of Mfbs on cardiac fiber’s tension and strain. The results showed that (1) Mfbs decreased APD50 and increased Vrest depolarization, (2) Mfbs regulated myocyte peak force and (3) Mfbs reduced the fiber peak force in isometric contraction and the fiber peak strain in isotonic contraction. The identified effects demonstrated that Mfbs play an important role of modulating cardiac mechanical behavior. It should be considered in future pathological cardiac mathematical modeling, such as atrial fibrillation and cardiac fibrosis.","PeriodicalId":6716,"journal":{"name":"2019 Computing in Cardiology Conference (CinC)","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90510055","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

Prediction Algorithm of Malignant Ventricular Arrhythmia Validated across Multiple Online Public Databases 跨多个在线公共数据库验证的恶性室性心律失常预测算法

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.295

Wei Wei Heng, Eileen Su Lee Ming, Ahmad Nizar Jamaluddin, Fauzan Khairi Che Harun, Nurul Ashikin Abdul-Kadir, Che Fai Yeong

{"title":"Prediction Algorithm of Malignant Ventricular Arrhythmia Validated across Multiple Online Public Databases","authors":"Wei Wei Heng, Eileen Su Lee Ming, Ahmad Nizar Jamaluddin, Fauzan Khairi Che Harun, Nurul Ashikin Abdul-Kadir, Che Fai Yeong","doi":"10.22489/cinc.2019.295","DOIUrl":"https://doi.org/10.22489/cinc.2019.295","url":null,"abstract":"Prediction of malignant ventricular arrhythmia (mVA) is essential to prevent sudden cardiac death. There were mainly three research clusters on mVA prediction using electrocardiogram (ECG): prediction using CUDB, SDDB and private databases. Comparability and generalization issue arose due to the different usage of arrhythmic datasets for analysis. Very few studies attempted short-term prediction of mVA using multiple databases, and those studies achieved low prediction performance. Our study aims to improve the prediction performance involving multiple databases and to promote the algorithm comparability by performing more comprehensive comparability study while including a more complete set of data available from the public databases. In our study, eight statistical box count features derived from phase space reconstruction on ECG signal were classified using maximum thresholding method. This was followed by performance benchmarking against the first two clusters of existing research and a performance evaluation using the combined set of databases. Our algorithm using box count coefficient of mean absolute deviation achieved over 90% of accuracy and over 4-minutes prediction time for all the three set of performance evaluations. This algorithm outperforms the existing work by introducing lower computational efforts.","PeriodicalId":6716,"journal":{"name":"2019 Computing in Cardiology Conference (CinC)","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90845629","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

HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability HRVTool -一个开源的Matlab工具箱，用于分析心率变异性

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.032

M. Vollmer

{"title":"HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability","authors":"M. Vollmer","doi":"10.22489/cinc.2019.032","DOIUrl":"https://doi.org/10.22489/cinc.2019.032","url":null,"abstract":"Motivation: Many software tools for ECG processing are commercial. New innovative and alternative features for heart rate variability analysis (HRV) and improved methods in ECG preprocessing cannot be incorporated. Moreover, software manuals are lacking of clarity and often conceal the exact calculation methods that makes clinical interpretation difficult, and reproducibility is reduced. Software description: HRVTool provides an opensource and intuitive user-friendly environment for the HRV analysis in Matlab. The software is available at http://marcusvollmer.github.io/HRV and supports the processing of ECG, pulsatile waveforms and RR intervals from various sources (mat and text files containing raw data, Polar, PhysioNet, Hexoskin, BIOPAC, European Data Format, ISHNE Holter Standard Format, and Machine-Independent Beat files). An integrated heart beat detector locates R peaks or pulse waves. Visual inspection, and manual adjustments of beat locations are possible and the corresponding annotation file can be saved in a standard Matlab format or as a delimited text file. HRV statistics are computed in a sliding window to evaluate the alteration over time. HRV metrics can be exported. An animation of intervals supports pattern identification. Moreover the Matlab class (HRV.m) includes functions for windowed HRV computation that can be used for batch processing.","PeriodicalId":6716,"journal":{"name":"2019 Computing in Cardiology Conference (CinC)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84975268","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: 38

A Three-Dimensional Model of the Human Atria With Heterogeneous Thickness and Fibre Transmurality - A Realistic Platform for the Study of Atrial Fibrillation 具有非均匀厚度和纤维跨壁性的人类心房三维模型——房颤研究的一个现实平台

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.380

Sara Rocher, L. Martinez, Alejandro López, A. Ferrer, D. Sánchez-Quintana, J. Saiz

引用次数: 1

An Ensemble Machine Learning Model for the Early Detection of Sepsis from Clinical Data 从临床数据中早期检测败血症的集成机器学习模型

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.317

Mengsha Fu, Jiabin Yuan, Menglin Lu, Pengfei Hong, M. Zeng

{"title":"An Ensemble Machine Learning Model for the Early Detection of Sepsis from Clinical Data","authors":"Mengsha Fu, Jiabin Yuan, Menglin Lu, Pengfei Hong, M. Zeng","doi":"10.22489/cinc.2019.317","DOIUrl":"https://doi.org/10.22489/cinc.2019.317","url":null,"abstract":"Sepsis is a life-threatening disease with high mortality and expensive cost of treatment. In order to improve the outcomes of patients, it is important to detect atrisk patients with sepsis at an early stage. The PhysioNet/Computing in Cardiology Challenge 2019 focused on improving predicting sepsis six hours before the clinical diagnosis by using the latest definition of Sepsis-3. A total of 40,336 ICU patients were provided as public training data, A hidden test dataset was used to evaluate. An ensemble model, which combined boosting and bagging tree models (lightgbm, xgboost and random forest ) were designed to predict sepsis based on the records of the patient’s hourly data. We compared the ensemble model and each single model of evaluation metrics results on selected inner test data Offline, the best performance was achieved AUC of 0.792, ACC of 0.727. Finally, the proposed model was evaluated on the full test sets received an official utility score, defined by the organizers, was 0.087, ranked 75/105 (our team name: cinc sepsis pass). While the single model of lightgbm only received a utility score of -0.036. The ensemble model utilized the preprocessing data and achieved better performance than a single tree-based model.","PeriodicalId":6716,"journal":{"name":"2019 Computing in Cardiology Conference (CinC)","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84154101","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: 8

Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization 基于XGBoost学习和贝叶斯优化的多特征融合脓毒症早期预测

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.020

Meicheng Yang, Xingyao Wang, Hongxiang Gao, Yuwen Li, Xing Liu, Jianqing Li, Chengyu Liu

{"title":"Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization","authors":"Meicheng Yang, Xingyao Wang, Hongxiang Gao, Yuwen Li, Xing Liu, Jianqing Li, Chengyu Liu","doi":"10.22489/cinc.2019.020","DOIUrl":"https://doi.org/10.22489/cinc.2019.020","url":null,"abstract":"Early prediction of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an algorithm for accurately predicting the onset of sepsis in the proceeding of six hours. Firstly, we selected 37 available variates features after data pre-processing, and then extracted three kinds of features as well in this paper, including 62 missing value features, 8 scoring quantified features and 61 time series features. After that, a multi-feature fusion based XGBoost classification model was developed and was further improved by a Bayesian optimizer and an ensemble learning framework. Analysis was performed on the PhysioNet/Computing in Cardiology Challenge 2019, which provided a publicly available sepsis data sourced from 40,336 ICU patients. Finally, after searching an optimized predicted risk threshold of 0.522 through the official submissions, our team “SailOcean” applied the developed model on the full hidden test set of 24,819 ICU patients from three hospital systems and obtained a final Unormalized score (U-Score) defined by the organizers of 0.364, which was the highest unofficial score.","PeriodicalId":6716,"journal":{"name":"2019 Computing in Cardiology Conference (CinC)","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78330083","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: 12

A New Graphical Method for Reporting Performance Results of a Diagnostic Test 报告诊断测试性能结果的一种新的图形方法

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.409

Wang C John

引用次数: 0

A Novel 2-Lead to 12-Lead ECG Reconstruction Methodology for Remote Health Monitoring Applications 一种新的2导联至12导联心电图重建方法用于远程健康监测应用

2019 Computing in Cardiology Conference (CinC) Pub Date : 2019-12-30 DOI: 10.22489/cinc.2019.231

N. Vemishetty, Vishnuvardhan Gundlapalle, A. Acharyya, Bhudeb Chakravarti

引用次数: 0