2021 Computing in Cardiology (CinC)最新文献_第6页

Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data 基于高斯预测数据的深度学习过早收缩定位

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662903

Petra Novotna, Tomáš Vičar, Jakub Hejc, M. Ronzhina

{"title":"Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data","authors":"Petra Novotna, Tomáš Vičar, Jakub Hejc, M. Ronzhina","doi":"10.23919/cinc53138.2021.9662903","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662903","url":null,"abstract":"Detection of cardiac arrhythmias is still an ongoing challenge. Here we focus on premature ventricular contraction (PVC) and premature atrial contraction (PAC) and introduce a deep-learning-based method for PVC/PAC localization in ECG. Our method is based on involving the time series with non-zero values corresponding to the ground truth PVC/PAC positions into the training process. To improve the efficiency of deep model training, the transition between the non-zero and zero areas in the train output time series was smoothed by introducing a Gaussian function. When applied to the new ECGs, the output signal (time series including Gaussians) is processed by a robust peak detector with Bayesian optimization of threshold, minimal distance and peak prominence. Positions of the detected peaks correspond to the desired PVC/PAC positions. The proposed method was evaluated on China Physiological Signal Challenge 2018 (CPSC2018) using own-created ground truth positions of PVC/PAC. The proposed method reached F1 score 0.923 and 0.688 for PAC and PVC, respectively, which is better than our previous results obtained via multiple instance learning-based method.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126868599","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

Automated Diagnosis of Reduced-Lead Electrocardiograms Using a Shared Classifier 使用共享分类器的低铅心电图自动诊断

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662872

H. Jessen, R. V. D. Leur, P. Doevendans, R. V. Es

引用次数: 1

Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance 房颤发作模式及其对检测性能的影响

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662847

Monika Butkuvienė, A. Petrėnas, Andrius Sološenko, A. Martín-Yebra, V. Marozas, L. Sörnmo

{"title":"Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance","authors":"Monika Butkuvienė, A. Petrėnas, Andrius Sološenko, A. Martín-Yebra, V. Marozas, L. Sörnmo","doi":"10.23919/cinc53138.2021.9662847","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662847","url":null,"abstract":"Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132651393","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

Gender Differences in Short-Term Multiscale Complexity of the Heart Rate Variability 心率变异性短期多尺度复杂性的性别差异

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662906

B. D. Maria, F. Perego, G. Cassetti, V. Bari, B. Cairo, F. Gelpi, Monica Parati, L. Vecchia, A. Porta

{"title":"Gender Differences in Short-Term Multiscale Complexity of the Heart Rate Variability","authors":"B. D. Maria, F. Perego, G. Cassetti, V. Bari, B. Cairo, F. Gelpi, Monica Parati, L. Vecchia, A. Porta","doi":"10.23919/cinc53138.2021.9662906","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662906","url":null,"abstract":"Among the analytical methods estimating the complexity of the heart period (HP), the linear model-based multiscale complexity (MSC) approach allows the estimation of the complexity over time scales linked to the cardiac autonomic control, i.e. in the low frequency (LF, 0.04-0.15 Hz) and high frequency $(HF, 0.15-0.4 Hz)$ bands. In this study we exploited MSC to evaluate the differences in the HP variability complexity during daytime (DAY) and nighttime (NIGHT) in 23 healthy females (WOMEN, age $36pm 6yrs)$ ) and 21 males (MEN, age $35pm 5yrs)$ performing a 24-hour Holter electrocardiogram. Parametric power spectral analysis was applied as well for comparison. Complexity indexes were computed regardless of the temporal scale (CI) and in the LF and HF bands ( $CI_{LF}$ and $CI_{HF}$, respectively). We found that the power spectral indexes did not differentiate WOMEN and MEN, while CI and $CI_{LF}$ were higher in WOMEN during DAY. The higher HP complexity in females could be explained by a lower sympathetic drive and more complex hormonal regulation than males. We conclude that MSC was more powerful than power spectral analysis in detecting gender differences in HP variability. In addition, as cardiac control differs between females and males, preventive and therapeutic interventions should take gender differences into account.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130430927","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

Mavacamten Efficacy in Mutation-specific Hypertrophic Cardiomyopathy: an In Silico Approach to Inform Precision Medicine 马伐卡坦对突变特异性肥厚性心肌病的疗效:一种为精准医学提供信息的计算机方法

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662736

F. Margara, B. Rodríguez, Christopher N Toepfer, A. Bueno-Orovio

{"title":"Mavacamten Efficacy in Mutation-specific Hypertrophic Cardiomyopathy: an In Silico Approach to Inform Precision Medicine","authors":"F. Margara, B. Rodríguez, Christopher N Toepfer, A. Bueno-Orovio","doi":"10.23919/cinc53138.2021.9662736","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662736","url":null,"abstract":"Hypertrophic cardiomyopathy (HCM) is a common genetic heart disease characterised by hyperdynamic contraction and slowed relaxation. It has been proposed that cellular hypercontractility can derive from mutations that destabilise the energy-conserving myosin super relaxed state, SRX. A new drug, Mavacamten, has been shown to re-stabilise myosin SRX. Here we develop a human-based in-silico model to investigate how disease and drug-induced SRX changes alter cardiac contractility. We do this to mechanistically investigate how Mavacamten restores function in a HCM causing mutation. Our simulations show that hypercontractility is accounted for by an increased availability of crossbridges due to a reduced abundance of myosin SRX, but cellular diastolic dysfunction is only recapitulated if there is a direct crossbridge contribution to thin filament activation. Our model replicates reduced cellular contractility with Mavacamten treatment, which also rescues the hypercontractile phenotype in HCM Our model demonstrates that Mavacamten is effective in correcting HCM abnormalities caused by mutations that destabilise SRX. However, genotypes that cause HCM via other molecular pathways may be incompletely salvaged by Mavacamten.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130471152","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

A Novel Computational Model of Pacemaker Activity in the Mouse Atrioventricular Node Cell 一种新的小鼠房室结细胞起搏器活动计算模型

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662700

C. Bartolucci, P. Mesirca, Claire Belles, Eugenio Ricci, E. Torre, J. Louradour, M. Mangoni, S. Severi

{"title":"A Novel Computational Model of Pacemaker Activity in the Mouse Atrioventricular Node Cell","authors":"C. Bartolucci, P. Mesirca, Claire Belles, Eugenio Ricci, E. Torre, J. Louradour, M. Mangoni, S. Severi","doi":"10.23919/cinc53138.2021.9662700","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662700","url":null,"abstract":"Nowadays, mathematical modeling has been one of the improvements in technologically advanced science in supporting decision-making in different healthcare scenarios. In the field of numerical modelling of heart electrophysiology, several models of action potential (AP) have been developed for cardiac chambers of different species. The atrioventricular node (AVN) acts as a subsidiary pacemaker and controls impulse conduction between the atria and ventricles. Despite its physiological importance, limited data are available for computing AVN cellular electrophysiology. Further, the ionic mechanisms underlying the automaticity of AVN myocytes are incompletely understood. Only two computational models of AVN have been developed in the last decades (one for rabbit, the other for mouse but without calcium handling). We aimed to develop a new mouse AVN model. We thus build on the preliminary AP mouse AVN model published by Marger et al., which has been updated and improved, by implementing more realistic cellular compartments and calculation of dynamics and handling of intracellular $Ca^{2+}$. The new model reproduces almost all the AVN AP hallmarks and has been used to simulate the effects of blockade of ionic currents involved in AVN pacemaking.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905751","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

Ensemble Learning of Modified Residual Networks for Classifying ECG with Different Set of Leads 基于改进残差网络的不同导联心电分类集成学习

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662779

Federico M. Muscato, V. Corino, L. Mainardi

{"title":"Ensemble Learning of Modified Residual Networks for Classifying ECG with Different Set of Leads","authors":"Federico M. Muscato, V. Corino, L. Mainardi","doi":"10.23919/cinc53138.2021.9662779","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662779","url":null,"abstract":"The automatic detection and classification of cardiac abnormalities can assist physicians in making diagnoses, saving costs in modern healthcare systems. In this study we present an automatic algorithm for classification of cardiac abnormalities included in the CinC's challenge 2021 dataset consisting of twelve-lead, six-lead, three-lead, and two-lead ECGs (team: Polimi_1). For each set of leads an ensemble of three deep learning models, trained on three different subsets, was developed. These subsets, obtained by splitting the recordings with the most frequent classes, had more balanced distributions for training and were used to train the 3 classifiers. The trained models were modified Residual Networks with a Squeeze-and-Excitation module. This module is based on the intuition of channel attention: the basic idea of this approach is to apply a weight to the Convolutional channels based on their relevance in learning before propagating to the next layer. For evaluation, we submitted our model to the official phase of the PhysioNet/Computing in Cardiology Challenge 2021. The model received scores of 0.47, 0.46, 0.45, 0.48 and 0.45 (ranked 14th, 13th, 15th, 10th, and 13th out of 39 teams) on 12-lead, 6-lead, 4-lead, 3-lead, 2-lead hidden test set, respectively; placing us in the 11th position for the mean of the 12-lead, 3-lead, and 2-lead scores.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412730","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

Controlled Breathing Effect on Respiration Quality Assessment Using Machine Learning Approaches 机器学习方法对呼吸质量评估的控制呼吸效果

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662854

Andrea Rozo, J. Buil, Jonathan Moeyersons, John F. Morales, Roberto Garcia van der Westen, L. Lijnen, C. Smeets, S. Jantzen, V. Monpellier, D. Ruttens, C. Hoof, S. Huffel, W. Groenendaal, C. Varon

引用次数: 2

Learning ECG Representations for Multi-Label Classification of Cardiac Abnormalities 学习ECG表征用于心脏异常的多标签分类

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662753

J. Suh, Jimyeong Kim, Eunjung Lee, Jaeill Kim, Duhun Hwang, J. Park, Junghoon Lee, Jaeseung Park, Seo-Yoon Moon, Yeonsu Kim, Min-Ho Kang, Soo-Jung Kwon, E. Choi, Wonjong Rhee

{"title":"Learning ECG Representations for Multi-Label Classification of Cardiac Abnormalities","authors":"J. Suh, Jimyeong Kim, Eunjung Lee, Jaeill Kim, Duhun Hwang, J. Park, Junghoon Lee, Jaeseung Park, Seo-Yoon Moon, Yeonsu Kim, Min-Ho Kang, Soo-Jung Kwon, E. Choi, Wonjong Rhee","doi":"10.23919/cinc53138.2021.9662753","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662753","url":null,"abstract":"The goal of PhysioNet/Computing in Cardiology Challenge 2021 was to identify clinical diagnoses from 12 -lead and reduced-lead ECG recordings, including 6-lead, 4-lead, 3-lead, and 2-lead recordings. Our team, snu_adsl, have used EfficientNet-B3 as the base deep learning model and have investigated methods including data augmentation, self-supervised learning as pre-training, label masking that deals with multiple data sources, threshold optimization, and feature extraction. Self-supervised learning showed promising results when the size of labeled dataset was limited, but the competition's dataset turned out to be large enough that the actual gain was marginal. In consequence, we did not include self-supervised pre-training in our final entry. Our classifiers received scores of 0.48, 0.48, 0.47, 0.47, and 0.45 (ranked 12th, 10th, 11th, 11th, and 13th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2 -lead versions of the hidden test set with the Challenge evaluation metric.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134598659","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: 5

A Prediction Model of In-Patient Deteriorations Based on Passive Vital Signs Monitoring Technology 基于被动生命体征监测技术的住院患者病情恶化预测模型

2021 Computing in Cardiology (CinC) Pub Date : 2021-09-13 DOI: 10.23919/cinc53138.2021.9662864

Veronica Maidel, Maayan Lia Yizraeli Davidovich, Z. Shinar, Tal Klap

{"title":"A Prediction Model of In-Patient Deteriorations Based on Passive Vital Signs Monitoring Technology","authors":"Veronica Maidel, Maayan Lia Yizraeli Davidovich, Z. Shinar, Tal Klap","doi":"10.23919/cinc53138.2021.9662864","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662864","url":null,"abstract":"Lately, many health systems accelerated their initiatives of advanced remote monitoring systems. Moving to an unattended environment requires overcoming patients' compliance issues and demonstrating the effectiveness of remote monitoring technology. Current Early Warning Scores detection of deterioration, commonly based on spot check EMR data, demonstrates low translational impact from one facility to another. In this study we used vitals collected passively by a sensor, to build a Machine Learning model for timely prediction of deteriorating patients, within 24-hours of their transfer to ICU or death. Time series features, such as trends and vitals' variability were used in conjunction with age & comorbidity data. Evaluating the model yielded an AUROC of 0.81 on data from an inpatient setting, and an AUROC of 0.88 on an independent test set from a COVID-19 unit. The suggested model, based on passive measurement technology, performs equally well as models based on EMR that include nurse inputs. Applying the model on other acute settings (such as a COVID-19 unit) showed similar performance, increasing confidence of its robustness and transferability. The model performance combined with the fact that it does not require human compliance, makes it a good candidate for future testing on home settings.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132945262","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