2019 Computing in Cardiology (CinC)最新文献_第6页

An Automated Device for Recording Peripheral Arterial Waveform 一种记录外周动脉波形的自动装置

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005815

T. Panula, J. Blomster, Mikko Pänkäälä, T. Koivisto, M. Kaisti

引用次数: 1

Artificially Generated Training Datasets for Supervised Machine Learning Techniques in Magnetic Resonance Imaging: An Example in Myocardial Segmentation 磁共振成像中用于监督机器学习技术的人工生成训练数据集:以心肌分割为例

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005762

C. Xanthis, K. Haris, D. Filos, A. Aletras

引用次数: 0

The Signature-Based Model for Early Detection of Sepsis From Electronic Health Records in the Intensive Care Unit 基于签名的重症监护病房电子健康记录败血症早期检测模型

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005805

James Morrill, A. Kormilitzin, A. Nevado-Holgado, S. Swaminathan, Sam, Howison, Terry Lyons

引用次数: 46

A Review of Bandwidth for Pediatric ECGs 儿童心电图带宽研究综述

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005925

S. Luo, Hong Wei, P. Macfarlane

引用次数: 0

A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series 基于多变量临床时间序列的脓毒症早期预测的多任务归算和分类神经结构

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005751

Yale Chang, Jonathan Rubin, G. Boverman, S. Vij, Asif Rahman, A. Natarajan, S. Parvaneh

{"title":"A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series","authors":"Yale Chang, Jonathan Rubin, G. Boverman, S. Vij, Asif Rahman, A. Natarajan, S. Parvaneh","doi":"10.23919/CinC49843.2019.9005751","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005751","url":null,"abstract":"Early prediction of sepsis onset can notify clinicians to provide timely interventions to patients to improve their clinical outcomes. The key question motivating this work is: given a retrospective patient cohort consisting of multivariate clinical time series (e.g., vital signs and lab measurement) and patients' demographics, how to build a model to predict the onset of sepsis six hours earlier? To tackle this challenge, we first used a recurrent imputation for time series (RITS) approach to impute missing values in multivariate clinical time series. Second, we applied temporal convolutional networks (TCN) to the RITS-imputed data. Compared to other sequence prediction models, TCN can effectively control the size of sequence history. Third, when defining the loss function, we assigned custom time- dependent weights to different types of errors. We achieved 9th place (team name = prna, utility score = 0.328) at the 2019 PhysioNet Computing in Cardiology Challenge, which evaluated our proposed model on a real-world sepsis patient cohort. At a follow-up ‘hackathon’ event, held by the challenge organizers, an improved version of our algorithm achieved 2nd place (utility score = 0.342).","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"81 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":"76713456","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: 11

Automated 3D MRI Aortic Morphometry Demonstrates the Added Value of Volumes as Compared to Diameters 自动3D MRI主动脉形态测量显示体积相对于直径的附加价值

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005743

T. Dietenbeck, Sophia Houriez--Gombaud-Saintonge, U. Gencer, A. Giron, G. Soulat, É. Mousseaux, P. Cluzel, A. Redheuil, N. Kachenoura

{"title":"Automated 3D MRI Aortic Morphometry Demonstrates the Added Value of Volumes as Compared to Diameters","authors":"T. Dietenbeck, Sophia Houriez--Gombaud-Saintonge, U. Gencer, A. Giron, G. Soulat, É. Mousseaux, P. Cluzel, A. Redheuil, N. Kachenoura","doi":"10.23919/CinC49843.2019.9005743","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005743","url":null,"abstract":"Aim: The diagnosis of thoracic aortic aneurysm is based on local aortic deformation associated to excessive aortic diameter (D). Maximal local aortic diameter was shown to be below the recommended surgical threshold in 30% of patients who ultimately developed aortic dissection. Aortic volumes integrate both dilation and elongation and may be more sensitive to changes in aortic geometry and less dependent on slice orientation and obliquity than diameter measurements. Methods: We studied 278 asymptomatic individuals with 3D aortic MRI: 119 healthy volunteers (hC), 53 hypertensive patients (HT) and 106 patients with dilated ascending aorta of which 62 with tricuspid (APt) and 44 with bicuspid (APb) aortic valve. Automated 3D aortic segmentation was performed and aortic lengths, maximal diameters and volumes were measured from sino-tubular junction to the brachiocephalic trunk for the ascending aorta (AAo) and from the left subclavian artery to the diaphragm for the descending aorta (DAo). Results: While AAo D increased by 40% between APt and HC, AAo volume increased by 170%. Moreover, when comparing HT patients with controls, AAo volume difference was significant (p < 0.05) even after adjustment to BSA while AAo D was not. Conclusion: Aortic volumes measured automatically from 3D MRI were able to characterize subclinical and pathological dilation more accurately than maximal diameters.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"331 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":"80506238","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

Non-Invasive Localization of Atrial Flutter Circuit Using Recurrence Quantification Analysis and Machine Learning 应用递归量化分析和机器学习的心房扑动回路无创定位

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005844

Muhammad Haziq Kamarul Azman, Olivier Meste, D. Latcu, K. Kadir

引用次数: 1

Causal Relationship Analysis of Heart Rate Variability and Band Power Time Series of Electroencephalographic Signals 心率变异性与脑电图信号带功率时间序列的因果关系分析

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005719

MariNieves Pardo-Rodrı́guez, E. Bojorges-Valdez, O. Yáñez-Suárez

{"title":"Causal Relationship Analysis of Heart Rate Variability and Band Power Time Series of Electroencephalographic Signals","authors":"MariNieves Pardo-Rodrı́guez, E. Bojorges-Valdez, O. Yáñez-Suárez","doi":"10.23919/CinC49843.2019.9005719","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005719","url":null,"abstract":"This study aimed to find whether there is a causal relationship between band power time series (BPts) extracted from EEG and heart rate variability (HRV). Such relationships were explored during spontaneous and a controlled breathing tasks. Data analyzed were recordings obtained from 14 healthy subjects using one ECG lead and 21 EEG channels. The RR intervals from the ECG were used to obtain the HRV signal, which was decomposed with Empirical Mode Decomposition into components of different spectral content known as intrinsic mode functions (IMFs). Granger causality tests were run for the BPts of alpha, beta and gamma frequency ranges of the EEG signal and the HRV signals IMFs. G-causality increased for three different conditions: slower IMFs (IMF4), BPts of higher frequency (gamma) band and during task realization. Meaning, gamma’s BPts G-caused HRV for a larger number of subjects and channels. Also there was a larger incidence on the number of channels that G-caused HRV during the controlled breathing task. The causal influence from the BPts of EEG signals to the HRV IMFs suggests there is an indirect or unobserved interaction between instantaneous changes on EEG band power and components of HRV which may explain changes in its dynamics.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"16 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":"80466413","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

Multivariate Classification of Brugada Syndrome Patients Based on the Autonomic Response During Sleep, Exercise and Head-up Tilt Testing 基于自主神经反应的Brugada综合征患者睡眠、运动和平视倾斜测试的多变量分类

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005882

M. Calvo, V. Rolle, D. Romero, N. Béhar, P. Gomis, P. Mabo, Alfredo I. Hernández

引用次数: 0

Comparison of CARTO LAT Maps and Non-Invasive Activation Maps for Patients with Intraventricular Conduction Disturbance During Sinus Rhythm 窦性心律时脑室传导障碍患者的CARTO LAT图与无创激活图的比较

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005738

M. Budanova, M. Chmelevsky, S. Zubarev, D. Potyagaylo, B. Rudic, E. Tueluemen, M. Borggrefe

引用次数: 2