2019 Computing in Cardiology (CinC)最新文献_第3页

Automatic Quality Electrogram Assessment Improves Reentrant Activity Identification in Atrial Fibrillation 自动质量电图评估提高心房颤动再入活动识别

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005881

Alejandro Costoya-Sánchez, A. Climent, I. Hernández-Romero, A. Liberos, F. Fernández‐Avilés, S. Narayan, F. Atienza, M. Guillem, M. Rodrigo

{"title":"Automatic Quality Electrogram Assessment Improves Reentrant Activity Identification in Atrial Fibrillation","authors":"Alejandro Costoya-Sánchez, A. Climent, I. Hernández-Romero, A. Liberos, F. Fernández‐Avilés, S. Narayan, F. Atienza, M. Guillem, M. Rodrigo","doi":"10.23919/CinC49843.2019.9005881","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005881","url":null,"abstract":"Location of reentrant electrical activity responsible for driving atrial fibrillation (AF) is key to ablative therapies. The aim of this work is to study the effect of the quality of the electrograms (EGMs) used for 3D phase analysis on reentrant activity identification, as well as to develop an algorithm capable of automatically identifying low- quality signals.EGMs signals from 259 episodes obtained from 29 AF patients were recorded using 64-electrode basket catheters. Low-quality EGMs were manually identified. Reentrant activity was identified in 3D phase maps and provided an area under the ROC curve (AUC) of 0.69 when compared to a 2D activation-based method. Reentries located in regions with poor-quality EGMs were then removed, increasing the AUC to 0.80. The EGM classification algorithm showed a similar performance both for low-quality EGM identification (sensitivity 0.91 and specificity 0.80) and for reentrant activity location with 3D phase analysis (AUC 0.80).Discard of reentrant activity identified in regions where EGMs showed low quality significantly improved the specificity of the 3D phase analysis. Besides, EGMs classification according to their quality proved to be possible using time and spectral domain parameters.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"34 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88028830","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

Interpretation of Artificial Intelligence Algorithms in the Prediction of Sepsis 人工智能算法在脓毒症预测中的解释

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005667

Induparkavi Murugesan, K. Murugesan, Lingeshwaran Balasubramanian, Malathi Arumugam

引用次数: 3

Feature Tracking for Ventricular Strain Assessment in Heart Failure with Preserved Ejection Fraction 保留射血分数的心力衰竭患者心室应变评估的特征跟踪

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005634

L. Zhong, S. Leng, Xiaodan Zhao, R. Tan

引用次数: 0

Head Pulsation Signal Analysis for 3-Axis Head-Worn Accelerometers 三轴头戴式加速度计头部脉动信号分析

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005624

O. Lahdenoja, Tero Hurnanen, Juho Koskinen, M. Kaisti, Kim Munck, S. Schmidt, T. Koivisto, Mikko Pänkäälä

引用次数: 1

Using Features Extracted From Vital Time Series for Early Prediction of Sepsis 基于生命时间序列特征的脓毒症早期预测

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005646

Qiang Yu, Xiaolin Huang, Weifeng Li, Cheng Wang, Ying Chen, Yun Ge

引用次数: 1

Fetal Electrocardiography and Deep Learning for Prenatal Detection of Congenital Heart Disease 胎儿心电图和深度学习在先天性心脏病产前检测中的应用

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005870

R. Vullings

引用次数: 8

Application of the Entropy of Approximation for the nonlinear characterization in patients with Chagas Disease 近似熵在查加斯病非线性表征中的应用

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005876

M. Vizcardo, A. Ravelo, Miriam Manrique, P. Gomis

{"title":"Application of the Entropy of Approximation for the nonlinear characterization in patients with Chagas Disease","authors":"M. Vizcardo, A. Ravelo, Miriam Manrique, P. Gomis","doi":"10.23919/CinC49843.2019.9005876","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005876","url":null,"abstract":"Chagas disease American trypanosomiasis is caused by a flagellated parasite: Trypanosoma cruzi, transmitted by an insect of the genus Triatoma and also by blood transfusions. In Latin America, the number of infected people is approximately 6 million, with a population exposed to the risk of infection of 550000. It is our interest to develop a non-invasive and low-cost methodology, capable of detecting any early cardiac alteration that also allows us to see dysautononia or dysfunction within 24 hours and with this it could be used to detect any cardiac alteration caused by T early Cruzi. For this, we analyzed the 24- hour Holter ECG records in 107 patients with ECG abnormalities (CH2), 102 patients without ECG alterations (CH1) who had positive serological results for Chagas disease and 83 volunteers without positive serological results for Chagas disease (CONTROL). Approximate entropy was used to quantify the regularity of electrocardiograms (ECG) in the three groups. We analyzed 288 ECG segments per patient. Significant differences were found between the CONTROL-CH1, CONTROL-CH2 and CH1- CH2 groups.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"17 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89570317","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

Atherosclerotic Plaques Recognition in Intracoronary Optical Images Using Neural Networks 利用神经网络识别冠状动脉内光学图像中的动脉粥样硬化斑块

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005679

M. Macedo, Dario Augusto Borges Oliveira, M. A. Gutierrez

{"title":"Atherosclerotic Plaques Recognition in Intracoronary Optical Images Using Neural Networks","authors":"M. Macedo, Dario Augusto Borges Oliveira, M. A. Gutierrez","doi":"10.23919/CinC49843.2019.9005679","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005679","url":null,"abstract":"Coronary artery disease (CAD) is intrinsically related to presence of atherosclerotic plaques. The rupture of this plaques is responsible for most acute coronary events. Intracoronary optical coherence tomography (IOCT) enables a detailed high-resolution visualization of micro-structural changes of the arterial wall in vivo. In this paper, we introduce a new way of identifying atherosclerotic plaques using 1D Convolutional Neural Networks (CNN) analyzing only the lumen contour. Training and test were performed with 1600 IOCT frames from in vivo patients. In our tests, we achieved f1-score of 95% for atherosclerotic plaque recognition. The results allow us to report an interesting correlation between the lumen contour geometry and the presence of plaques in the vascular wall observed through IOCT exams. The use of lumen contour for plaque detection opens two new perspectives: assisting specialists in the task of detecting plaques visually by paying special attention to the lumen and allowing methods to work in real time to detect plaques using efficient methods that use less information and deliver accurate results.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"3 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89265843","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

An Algorithm Based on Combining hs-cTnT and H-FABP for Ruling Out Acute Myocardial Infarction 基于hs-cTnT和H-FABP的急性心肌梗死排除算法

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005791

César Navarro, M. Kurth, M. Ruddock, S. Fishlock, J. Mclaughlin

引用次数: 1

Diagnosis of Sepsis Using Ratio Based Features 脓毒症的比例特征诊断

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005516

Shivnarayan Patidar

{"title":"Diagnosis of Sepsis Using Ratio Based Features","authors":"Shivnarayan Patidar","doi":"10.23919/CinC49843.2019.9005516","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005516","url":null,"abstract":"Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to use machine learning for early prediction of sepsis using ratio and power-based feature transformation. The feature transformation and feature selection process is optimized by applying a genetic algorithm (GA) based approach to extract the information specific to the sepsis from the given raw patient covariates that maximizes the underlying classification performance in terms of utility score. The proposed method begins with filling the missing values in the training dataset. Then, GA is applied strategically to identify influential ratio and power-based features from the raw patient covariates. The utility score is maximized as an objective of the optimization. RusBoost is used with default settings for underlying classification during optimization. Subsequently, an optimal RusBoost model is developed with a set of 55 identified features. Independent performance evaluation of the proposed method with the 2019 PhysioNet/CinC Challenge dataset has officially achieved 19th rank with a utility score of 30.9% on the full hidden test data. This work appears as Shivpatidar on the leader-board. The proposed early warning system has potential clinical value in critical care clinics.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"21 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85068622","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