2022 Computing in Cardiology (CinC)最新文献_第8页

Tracking of Atrial Fibrillation Drivers Based on Propagation Patterns: An In-Silico Study 基于传播模式的房颤驱动跟踪:一项计算机研究

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.174

"Victor Gonçalves Marques, A. Gharaviri, Simone Pezzuto, A. Auricchio, P. Bonizzi, S. Zeemering, U. Schotten

{"title":"Tracking of Atrial Fibrillation Drivers Based on Propagation Patterns: An In-Silico Study","authors":"\"Victor Gonçalves Marques, A. Gharaviri, Simone Pezzuto, A. Auricchio, P. Bonizzi, S. Zeemering, U. Schotten","doi":"10.22489/CinC.2022.174","DOIUrl":"https://doi.org/10.22489/CinC.2022.174","url":null,"abstract":"In some persistent atrial fibrillation (AF) patients, localized drivers may sustain AF and thus could represent possible ablation targets. In this work, we test in silico the feasibility of locating AF drivers from high-density electrode grid catheter mapping. A volumetric 3D atrial model was used to simulate 8 AF episodes driven by a stable reentry around a region of scar tissue (5 left atrium [LA], 3 right atrium [RA]). Sequential mapping in 1s segments was performed with a high-density electrode grid, starting from 20 uniformly distributed regions (12 LA, 8 RA). Conduction velocities estimated for each AF cycle were used to obtain temporal and directional parameters of the propagation. Trajectories of connected activation times were used to detect reentries or radial spread of activations. If no pattern was detected, the electrode array was moved in 5mm steps upstream of the propagation direction. The algorithm obtained accuracy, sensitivity, and precision of 87.2%, 23.4%, and 56.3% for reentries and 87.0%, 8.5%, and 26.8% for radial spread of activations, respectively. Reentries were found in average within 1.52 steps15 mm from the initial position of the grid. The results indicate that propagation patterns may be sufficient to track localized AF drivers sequentially during high-density mapping.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125754748","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

Unexpected Errors in the Electrocardiographic Forward Problem 心电图正演问题中的意外错误

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.217

Anna Busatto, J. Bergquist, Lindsay C. Rupp, B. Zenger, Rob S. MacLeod

{"title":"Unexpected Errors in the Electrocardiographic Forward Problem","authors":"Anna Busatto, J. Bergquist, Lindsay C. Rupp, B. Zenger, Rob S. MacLeod","doi":"10.22489/CinC.2022.217","DOIUrl":"https://doi.org/10.22489/CinC.2022.217","url":null,"abstract":"Previous studies have compared recorded torso potentials with electrocardiographic forward solutions from a pericardial cage. In this study, we introduce new comparisons of the forward solutions from the sock and cage with each other and with respect to the measured potentials on the torso. The forward problem of electrocardiographic imaging is expected to achieve high levels of accuracy since it is mathematically well posed. However, unexpectedly high residual errors remain between the computed and measured torso signals in experiments. A possible source of these errors is the limited spatial coverage of the cardiac sources in most experiments; most capture potentials only from the ventricles. To resolve the relationship between spatial coverage and the accuracy of the forward simulations, we combined two methods of capturing cardiac potentials using a 240-electrode sock and a 256-electrode cage, both surrounding a heart suspended in a 192-electrode torso tank. We analyzed beats from three pacing sites and calculated the RMSE, spatial correlation, and temporal correlation. We found that the forward solutions using the sock as the cardiac source were poorer compared to those obtained from the cage. In this study, we explore the differences in forward solution accuracy using the sock and the cage and suggest some possible explanations for these differences.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"498 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129570347","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

Weakly-Supervised Deep Learning for Left Ventricle Fibrosis Segmentation in Cardiac MRI Using Image-Level Labels 基于图像级标签的弱监督深度学习心脏MRI左心室纤维化分割

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.197

"Roel Klein, Florence E. van Lieshout, Maarten Z. Kolk, Kylian van Geijtenbeek, Romy Vos, S. Ruipérez-Campillo, Ruibin Feng, B. Deb, Prasanth Ganesan, R. Knops, I. Išgum, S. Narayan, E. Bekkers, B. D. de Vos, Fleur V. Tjong"

{"title":"Weakly-Supervised Deep Learning for Left Ventricle Fibrosis Segmentation in Cardiac MRI Using Image-Level Labels","authors":"\"Roel Klein, Florence E. van Lieshout, Maarten Z. Kolk, Kylian van Geijtenbeek, Romy Vos, S. Ruipérez-Campillo, Ruibin Feng, B. Deb, Prasanth Ganesan, R. Knops, I. Išgum, S. Narayan, E. Bekkers, B. D. de Vos, Fleur V. Tjong\"","doi":"10.22489/CinC.2022.197","DOIUrl":"https://doi.org/10.22489/CinC.2022.197","url":null,"abstract":"Automated segmentation of myocardial fibrosis in late gadolinium enhancement (LGE) cardiac MRI (CMR) has the potential to improve efficiency and precision of diagnosis and treatment of cardiomyopathies. However, state-of-the-art Deep Learning approaches require manual pixel-level annotations. Using weaker labels can greatly reduce manual annotation time and expedite dataset curation, which is why we propose fibrosis segmentation methods using either slice-level or stack-level fibrosis labels. 5759 short-axis LGE CMR image slices were retrospectively obtained from 482 patients. U-Nets with slice-level and stack-level supervision are trained with 446 weakly-labeled patients by making use of a myocardium segmentation U-Net and fibrosis classification Dilated Residual Networks (DRN). For comparison, a U-Net is trained with pixel-level supervision using a training set of 81 patients. On the proprietary test set of 24 patients, pixel-level, slice-level and stack-level supervision reach Dice scores of 0.74, 0.70 and 0.70, while on the external Emidec dataset of 100 patients Dice scores of 0.55, 0.61 and 0.52 were obtained. Results indicate that using larger weakly-annotated datasets can approach the performance of methods using pixel-level annotated datasets and potentially improve generalization to external datasets.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124597300","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

Improving Aorta Segmentation from Phase Contrast MRI Using Adaptive Velocity-Dependent Weighting on the Deep Learning Output for Magnitude and Phase Images 在深度学习输出的幅度和相位图像上使用自适应速度相关加权来改进相位对比MRI主动脉分割

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.244

Mohamed A Elbayumi, S. Saraya, T. Basha

{"title":"Improving Aorta Segmentation from Phase Contrast MRI Using Adaptive Velocity-Dependent Weighting on the Deep Learning Output for Magnitude and Phase Images","authors":"Mohamed A Elbayumi, S. Saraya, T. Basha","doi":"10.22489/CinC.2022.244","DOIUrl":"https://doi.org/10.22489/CinC.2022.244","url":null,"abstract":"Phase contrast MRI can provide a comprehensive analysis for the hemodynamic changes in the aorta which is useful for the diagnosis of several aortic diseases. However, an initial step of accurate segmentation of the aorta is necessary, which is usually a time-consuming and subjective step. Several methods have been proposed to automate this step using classical segmentation methods and recently deep learning models. Most of the current models combine the magnitude and phase images equally across all time phases which hinder the potential advantage that the frames of higher velocity might have more useful information compared to the low velocity frames. In this work, we propose a novel adaptive combination model that combines the output probability maps of both the magnitude and phase models based on an initial velocity estimation as a surrogate for the confidence level in the velocity images. We applied our model on the 2D-PC images of 215 patients and our results shows an accuracy of 87% for the magnitude images, 68% for the velocity images, 87.1% for the combined images, and 89.1 % for our proposed combination model.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131243949","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

Simulation of Acquired LQT Syndrome Using Human Virtual Ventricular Cardiomyocyte Model 利用人虚拟心室心肌细胞模型模拟获得性LQT综合征

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.420

Shumo Zhao, Cunjin Luo, Ying He, Linghua Li

引用次数: 0

Towards Uncertainty-Aware Murmur Detection in Heart Sounds via Tandem Learning 基于串联学习的心音杂音检测研究

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.234

E. Bondareva, Tong Xia, Jing Han, Cecilia Mascolo

{"title":"Towards Uncertainty-Aware Murmur Detection in Heart Sounds via Tandem Learning","authors":"E. Bondareva, Tong Xia, Jing Han, Cecilia Mascolo","doi":"10.22489/CinC.2022.234","DOIUrl":"https://doi.org/10.22489/CinC.2022.234","url":null,"abstract":"The field of automated auscultation has been growing in popularity in the past decade due to manual auscultation being a challenging task requiring years of training. Many efforts in the field focus on achieving high accuracy, with confident, albeit sometimes wrong, classifiers. Such model over-confidence is especially dangerous in health-care setting. Leveraging the release of the new heart sound dataset as a part of PhysioNet 2022 challenge, we explored a novel murmur detection methodology using uncertainty-aware tandem learning. To separate unknown samples and detect heart sounds with murmur present, we developed two binary classifiers, under the assumption that training two models to solve simpler tasks could improve the overall sensitivity. First, we used a support vector machine for identification of unknown samples, followed by a Deep Neural Network (DNN) for prediction of murmur. In addition, we implemented uncertainty estimation in DNN using Monte Carlo dropouts for further eliminating any samples that should be labelled as unknown. Our team mobihealth achieved 63% and 69% sensitivity and specificity of murmur, scoring 0.467 (ranked 34th out of 40) and 11032 (ranked 25th out of 39) on the hidden validation set and 0.374 (ranked 40th out of 40) and 18754 (ranked 39th out of 39) on the hidden testing set during the challenge for murmur and outcome prediction tasks, respectively.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122529925","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

Improving Clinical ECG-based Atrial Fibrosis Quantification With Neural Networks Through In Silico P waves From an Extensive Virtual Patient Cohort 通过广泛的虚拟患者队列的计算机P波，用神经网络改进临床心电图为基础的心房纤维化量化

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.124

C. Nagel, Johannes Osypka, L. Unger, D. Nairn, A. Luik, R. Wakili, O. Doessel, A. Loewe

{"title":"Improving Clinical ECG-based Atrial Fibrosis Quantification With Neural Networks Through In Silico P waves From an Extensive Virtual Patient Cohort","authors":"C. Nagel, Johannes Osypka, L. Unger, D. Nairn, A. Luik, R. Wakili, O. Doessel, A. Loewe","doi":"10.22489/CinC.2022.124","DOIUrl":"https://doi.org/10.22489/CinC.2022.124","url":null,"abstract":"Fibrotic atrial cardiomyopathy is characterized by a replacement of healthy atrial tissue with diffuse patches exhibiting slow electrical conduction properties and altered myocardial tissue structure, which provides a substrate for the maintenance of reentrant activity during atrial fibrillation (AF). Therefore, an early detection of atrial fibrosis could be a valuable risk marker for new-onset AF episodes to select asymptomatic subjects for screening, allowing for timely intervention and optimizing therapy planning. We examined the potential of estimating the fibrotic tissue volume fraction in the atria based on P waves of the 12-lead ECG recorded in sinus rhythm in a quantitative and non-invasive way. Our dataset comprised 68,282 P waves from healthy subjects and 42,227 P waves from AF patients with low voltage areas in the atria, as well as 642,400 simulated P waves of a virtual cohort derived from statistical shape models with different extents of the left atrial myocardium replaced by fibrosis. The root mean squared error for estimating the left atrial fibrotic volume fraction on a clinical test set with a neural network trained on features extracted from simulated and clinical P waves was 16.57 %. Our study shows that the 12-lead ECG contains valuable information on atrial tissue structure. As such it could potentially be employed as an inexpensive and widely available tool to support AF risk stratification in clinical practice.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126595661","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

Multichannel Bed Based Ballistocardiography Heart Rate Estimation Using Continuous Wavelet Transforms and Autocorrelation 基于连续小波变换和自相关的多通道床弹道心电图心率估计

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.364

Ismail Elnaggar, Tero Hurnanen, Jonas Sandelin, O. Lahdenoja, A. Airola, M. Kaisti, T. Koivisto

{"title":"Multichannel Bed Based Ballistocardiography Heart Rate Estimation Using Continuous Wavelet Transforms and Autocorrelation","authors":"Ismail Elnaggar, Tero Hurnanen, Jonas Sandelin, O. Lahdenoja, A. Airola, M. Kaisti, T. Koivisto","doi":"10.22489/CinC.2022.364","DOIUrl":"https://doi.org/10.22489/CinC.2022.364","url":null,"abstract":"Bed based ballistocardiography (BCG) is a prime candidate for at home and nighttime monitoring especially in the growing elderly population because co-operation from the user is not required to be able to record signals. One issue with BCG is that the signal quality has intra-and inter-person variability based on factors such as age, gender, body position, and motion artifacts, making it challenging to accurately measure heart rate. A rule-based algorithm which considers all eight available BCG channels simultaneously from a given time epoch was developed using continuous wavelet transform (CWT) to extract the localized time-frequency representation of each epoch and then an averaging method was applied across the different scales of the CWT to produce a 1-dimensional array. Autocorrelation was then applied to this array to produce a heart rate estimate based on the lag between the autocorrelation maximum and the first side peak. This method does not require identification of individual heart beats to estimate heart rate and does not require annotated training data. This model produces an average mean absolute error (MAE) of 1.09 bpm across 40 subjects when compared to heart rate derived from ECG. This method produces competitive results without the need for annotated training data, which can be challenging to collect.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127260947","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

Optimal Fluid And Vasopressor Interventions In Septic ICU Patients Through Reinforcement Learning Model 通过强化学习模型对脓毒症ICU患者进行最佳液体和血管加压干预

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.189

Maximiliano Mollura, Cristian Drudi, Li-wei H. Lehman, Riccardo Barbieri

{"title":"Optimal Fluid And Vasopressor Interventions In Septic ICU Patients Through Reinforcement Learning Model","authors":"Maximiliano Mollura, Cristian Drudi, Li-wei H. Lehman, Riccardo Barbieri","doi":"10.22489/CinC.2022.189","DOIUrl":"https://doi.org/10.22489/CinC.2022.189","url":null,"abstract":"Introduction: Fluids and vasopressors represent the cornerstone for hemodynamic instability management in the intensive care unit (ICU). However, optimal personalized treatments strategies are still missing. Goal: To evaluate the ability of a reduced set of cardiovascular features in determining optimal actions with a reinforcement learning approach. Methods: Data were extracted from the MIMIC-III database Patients' trajectories were modeled as a Markov decision process with a target reward based on 90-day mortality. Performances with a reduced set of cardiovascular features (CARDIO), including heart rate, systolic and diastolic blood pressure, shock index, and oxygen saturation were compared with a random policy model (RANDOM) and a model with a full set of 48 clinical variables including physiologic, laboratory measurement, and ventilation parameters (FULL). Results: The CARDIa model achieved the highest results with a 95% lower bound (LB) of estimated policy value equal to 96.17 compared with the 86.00 obtained from the FULL model and 82.62 from the RANDOM policy model. Conclusions: Results show that cardiovascular features and ongoing treatments have the potential to determine the optimal dosage of fluids and vasopressors for septic patients when using reinforcement learning tools for the development of medical decision support systems.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126399124","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

Outcome Prediction and Murmur Detection in Sets of Phonocardiograms by a Deep Learning-Based Ensemble Approach 基于深度学习集成方法的心音图预后预测和杂音检测

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.137

Sven Festag, Gideon Stein, Tim Büchner, M. Shadaydeh, J. Denzler, C. Spreckelsen

{"title":"Outcome Prediction and Murmur Detection in Sets of Phonocardiograms by a Deep Learning-Based Ensemble Approach","authors":"Sven Festag, Gideon Stein, Tim Büchner, M. Shadaydeh, J. Denzler, C. Spreckelsen","doi":"10.22489/CinC.2022.137","DOIUrl":"https://doi.org/10.22489/CinC.2022.137","url":null,"abstract":"We, the team UKJ_FSU, propose a deep learning system for the prediction of congenital heart diseases. Our method is able to predict the clinical outcomes (normal, abnormal) of patients as well as to identify heart murmur (present, absent, unclear) based on phonocardiograms recorded at different auscultation locations. The system we propose is an ensemble of four temporal convolutional networks with identical topologies, each specialized in identifying murmurs and predicting patient outcome from a phonocardiogram taken at one specific auscultation location. Their intermediate outputs are augmented by the manually ascertained patient features such as age group, sex, height, and weight. The outputs of the four networks are combined to form a single final decision as demanded by the rules of the George B. Moody PhysioNet Challenge 2022. On the first task of this challenge, the murmur detection, our model reached a weighted accuracy of 0.567 with respect to the validation set. On the outcome prediction task (second task) the ensemble led to a mean outcome cost of 10679 on the same set. By focusing on the clinical outcome prediction and tuning some of the hyper-parameters only for this task, our model reached a cost score of 12373 on the official test set (rank 13 of 39). The same model scored a weighted accuracy of 0.458 regarding the murmur detection on the test set (rank 37 of 40).","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127878702","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