2019 Computing in Cardiology (CinC)最新文献_第7页

Early Prediction of Sepsis Using Gradient Boosting Decision Trees with Optimal Sample Weighting 基于最优样本加权梯度增强决策树的脓毒症早期预测

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005700

Ibrahim Hammoud, I. Ramakrishnan, M. Henry

引用次数: 3

Memristor Models for Early Detection of Sepsis in ICU Patients 忆阻器模型用于ICU患者脓毒症的早期检测

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005898

Vasileios Athanasiou, Z. Konkoli

引用次数: 0

Impaired Right Atrial Strain is Associated with Decompensated Hemodynamics in Pulmonary Arterial Hypertension 肺动脉高压患者右心房应变受损与失代偿性血流动力学相关

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005893

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

{"title":"Impaired Right Atrial Strain is Associated with Decompensated Hemodynamics in Pulmonary Arterial Hypertension","authors":"L. Zhong, S. Leng, Xiaodan Zhao, J. Tan, R. Tan","doi":"10.23919/CinC49843.2019.9005893","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005893","url":null,"abstract":"The transition of right ventricle (RV) from a compensated to decompensated state contributes to survival in pulmonary arterial hypertension (PAH). However, little is known about the significance of right atrial (RA) dysfunction on disease progression in PAH. In this context, there has been growing interest in markers of RA myocardial dysfunction. Speckle tracking echocardiography, which has been principally used to measure the myocardial strain, is technically challenging in the RA due to the thin atrial wall. Feature tracking cardiovascular magnetic resonance (FT-CMR) software designed to derive myocardial strain from CMR cine images has become available for measurements of atrial longitudinal strain. However, in subjects with relatively vigorous tricuspid annular motion, contour tracking of the RA free wall segment adjacent to the tricuspid valve is adversely affected and becomes the source of errors. In contrast to FT-CMR, we present a rapid assessable strain parameter that requires the automatic tracking of only 3 anatomical reference points – thus avoiding the segment contour tracking near the insertion of the anterior leaflet into the tricuspid annulus.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"24 1","pages":"Page 1-Page 2"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90197772","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 Ensemble LSTM Architecture for Clinical Sepsis Detection 用于临床脓毒症检测的集成LSTM体系结构

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005457

S. Schellenberger, Kilin Shi, J. P. Wiedemann, F. Lurz, R. Weigel, A. Koelpin

引用次数: 1

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

Investigation of Mechanisms of Regulation of Electromechanical Function of Cardiomyocytes in the Biomechanical Model of Myocardium 心肌生物力学模型中心肌细胞机电功能调控机制的研究

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005625

V. Sholohov, V. Zverev, A. Kursanov

{"title":"Investigation of Mechanisms of Regulation of Electromechanical Function of Cardiomyocytes in the Biomechanical Model of Myocardium","authors":"V. Sholohov, V. Zverev, A. Kursanov","doi":"10.23919/CinC49843.2019.9005625","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005625","url":null,"abstract":"We developed three-dimensional model of isolated myocardial muscular preparation that takes into account the coupling of excitation with contraction in the myocardium at the cellular and tissue levels. This model describes myocardium sample using approaches and methods developed in continuum mechanics. In the model, electromechanical interactions and mechano-electric feedbacks are realized both at the micro level and at the macro level. We used non-linear partial differential equations describing the deformation of the cardiac tissue, and a detailed \"Ekaterinburg-Oxford\" (EO) cellular model of the electrical and mechanical activity of cardiomyocytes. Electrical and mechanical interactions between the cells in tissue, as well as intracellular mechano-electric feedback beat-to-beat affect the functional characteristics of coupled cardiomyocytes further, adjusting their electrical and mechanical heterogeneity to the activation timing. Model analysis suggests that cooperative mechanisms of myofilament calcium activation contribute essentially to the generation of cellular functional heterogeneity in contracting cardiac tissue.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"6 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":"80365611","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 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

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

Multi-Feature Probabilistic Detector Applied to Apnea/Hypopnea Monitoring 多特征概率检测器在呼吸暂停/低呼吸监测中的应用

2019 Computing in Cardiology (CinC) Pub Date : 2019-09-01 DOI: 10.23919/CinC49843.2019.9005766

D. Ge, Alfredo I. Hernández

{"title":"Multi-Feature Probabilistic Detector Applied to Apnea/Hypopnea Monitoring","authors":"D. Ge, Alfredo I. Hernández","doi":"10.23919/CinC49843.2019.9005766","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005766","url":null,"abstract":"Robust, real-time apnea and hypopnea detection for monitoring patients suffering from sleep apnea syndrome (SAS) still represents an open problem due to the effect of noise artifacts, the complexity of respiratory patterns and inter-subject variability. We propose in this study the application of an original multi-feature probabilistic detector (MFPD) for SAS event detection during long-term monitoring recordings on three SAS patients. The nasal pressure signal is used as input to derive a set of respiratory features (variance, peak-to-peak amplitude and total respiration cycle) which are statistically characterized during time and used to provide a mono-feature detection probability in realtime. A centralized fusion approach based on the Kullback-Leibler divergence (KLD), optimally combines these mono-feature distributions in order to produce a final detection. While the optimal feature set selection lies beyond the scope of our study, we illustrate the ability to adapt each feature’s weight dynamically to make centralized fusion decisions. The method can be directly applied to data acquired from multiple sensors as long as features are synchronized. Our proposed fusion method achieves a very high sensitivity (94%) as compared with reference thresholding based methods in the literature.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"67 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75435560","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

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