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

Comparison of Signal Combinations for Cardiorespiratory Sleep Staging 心肺睡眠分期信号组合的比较

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.077

Miriam Goldammer, S. Zaunseder, Franz Ehrlich, Hagen Malberg

{"title":"Comparison of Signal Combinations for Cardiorespiratory Sleep Staging","authors":"Miriam Goldammer, S. Zaunseder, Franz Ehrlich, Hagen Malberg","doi":"10.22489/CinC.2022.077","DOIUrl":"https://doi.org/10.22489/CinC.2022.077","url":null,"abstract":"This work investigates the benefit of using multiple signals and preprocessing strategies for sleep staging from cardiorespiratory signals. We modified our previous Neural Network model to take different signal combinations as input. To that end, we added oxygen saturation and different respiratory signals to the electrocardiogram. We further invoked different preprocessing strategies that have been described previously for such signals, namely using downsampled signals vs. using time series of breath-to-breath intervals. We trained and tested our model variations with 4784 polysomnograms from the Sleep Heart Health Study. We found the best combination of signals to be heart rate together with a downsampled respiratory signal. The classification resulted in a k of 0.68 on hold-out test data, which outperforms our previous results and state of the art for cardiorespiratory sleep staging. We observe that combinations of cardiorespiratory signals can improve classification performance for automatic cardiorespiratory sleep staging. As there are generally more cardiorespiratory signals available and many more options for preprocessing them, we expect that further research in this area will show even more improvements.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"260 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":"124256043","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

ECG, EEG, Breathing Signals, and Machine Learning: Computer-Aided Detection of Obstructive Sleep Apnea Syndrome and Depression 心电图、脑电图、呼吸信号和机器学习:阻塞性睡眠呼吸暂停综合征和抑郁症的计算机辅助检测

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.082

Mostafa M. Moussa, Yahya Alzaabi, Ahsan H. Khandoker

{"title":"ECG, EEG, Breathing Signals, and Machine Learning: Computer-Aided Detection of Obstructive Sleep Apnea Syndrome and Depression","authors":"Mostafa M. Moussa, Yahya Alzaabi, Ahsan H. Khandoker","doi":"10.22489/CinC.2022.082","DOIUrl":"https://doi.org/10.22489/CinC.2022.082","url":null,"abstract":"Obstructive Sleep Apnea Syndrome (OSAS) and Major Depressive Disorder (MDD) are both common conditions associated with poor quality of life. We seek to classify OSAS and depression in OSAS patients, as well as sleep stages using multiple machine learning techniques. We have extracted features from 5-minute intervals of electrocardiograms (ECG), breathing signals, and electroen-cephalograms (EEG) recorded from a total of 118 subjects, of which 89 are used for training and 10-fold cross-validation and 29 are used for testing or a 75/25% split. The best classification performance of OSAS was obtained with light sleep and deep sleep with ReliefF using random forest and boosted trees, respectively. It has yielded an accuracy of 100.00%, F1-Score of 100.00%, Cohen's k Coefficient of 1.00, and a Matthews correlation coefficient (MCC) of 1.00. All sleep stages with 10 principal components using random forest yielded an accuracy of 77.50%, F1-Score of 78.05%, Cohen's k of 0.571, and an MCC of 0.632 for classification of depression in OSAS patients. Sleep staging was best done using bagged trees with features selected via sequential backward feature selection, yielding an accuracy of 76.90%, F1-Score of 75.90%, Cohen's k of 0.480, and an MCC of 0.634. These results show promise in detecting OSAS and depression in OSAS patients, particularly using light and deep sleep data.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"65 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":"128772042","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

Modeling Structural Abnormalities in Equivalent Dipole Layer Based ECG Simulations 基于等效偶极子层的ECG模拟中的结构异常建模

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.160

M. Kloosterman, M. Boonstra, F. Asselbergs, P. Loh, T. Oostendorp, P. V. Dam

引用次数: 0

Detection of Heart Sound Murmurs and Clinical Outcome with Bidirectional Long Short-Term Memory Networks 双向长短期记忆网络检测心音杂音及临床预后

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.153

S. Monteiro, A. Fred, H. Silva

引用次数: 6

Exercise-based Predictors of Late Recurrence of Atrial Fibrillation After Catheter Ablation 导管消融后房颤晚期复发的运动预测因素

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.106

Jakub Hejc, Richard Redina, Tomas Kulik, M. Pešl, Z. Stárek

{"title":"Exercise-based Predictors of Late Recurrence of Atrial Fibrillation After Catheter Ablation","authors":"Jakub Hejc, Richard Redina, Tomas Kulik, M. Pešl, Z. Stárek","doi":"10.22489/CinC.2022.106","DOIUrl":"https://doi.org/10.22489/CinC.2022.106","url":null,"abstract":"Freedom from atrial fibrillation at 1 year is estimated to be between 55–80 % of patients undergoing catheter ablation. A significant number of them would require repeat procedures due to recurrent <tex>$AF$</tex>. Patients at higher risk for developing recurrent <tex>$AF$</tex> could benefit from different ablation strategies and post-ablation rhythm control therapy. We aim to identify the exercise-based risk factors associated with the first recurrence of <tex>$AF$</tex> between 3 and 36 months following the ablation. Patients <tex>$(n=98$</tex>, 69.4 % men) referred for catheter ablation of paroxysmal <tex>$AF$</tex> underwent simultaneous arm ergometry, exercise echocardiography and invasive left atrial pressure measurements. After the index ablation procedure, follow-up visits were scheduled. The observed freedom from <tex>$AF$</tex> ecurrence during the follow-up was 81 %. Multivariable-adjusted <tex>$Cox$</tex> regression revealed the peak <tex>$VO_{2}$</tex> as the most significant predictor of late <tex>$AF$</tex> reccurence (hazard ratio 0.53, <tex>$p < 0.005)$</tex>. Among analyzed parameters, the lowest prediction error was achieved by including left atrial vol{###}- <tex>$ume$</tex> index, left atrial pressure and peak <tex>$VO_{2}$</tex> into age and sex adjusted <tex>$Cox$</tex> model (<tex>$AIC=132.02$</tex>, C-statistics <tex>$=0.83$</tex> ). Presence of either decreased exercise capacity or elevated left atrial pressure is able to identify patients with potentially impaired left atrial function and different clinical outcome after conventional pulmonary vein isolation.","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":"130815563","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

Cardiac Time Intervals Derived from Electrocardiography and Seismocardiography in Different Patient Groups 由不同患者组的心电图和地震心动图得出的心脏时间间隔

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.370

Ismail Elnaggar, Jouni Pykäri, Tero Hurnanen, O. Lahdenoja, A. Airola, M. Kaisti, T. Vasankari, M. Savontaus, T. Koivisto

{"title":"Cardiac Time Intervals Derived from Electrocardiography and Seismocardiography in Different Patient Groups","authors":"Ismail Elnaggar, Jouni Pykäri, Tero Hurnanen, O. Lahdenoja, A. Airola, M. Kaisti, T. Vasankari, M. Savontaus, T. Koivisto","doi":"10.22489/CinC.2022.370","DOIUrl":"https://doi.org/10.22489/CinC.2022.370","url":null,"abstract":"Differences in cardiac time intervals (CTIs) have previously been shown in different patient groups with varying levels of cardiac function. These studies relied on methods such as conventional echocardiography or tissue doppler imaging performed by a specialist to extract CTIs. The goal of this study was to evaluate the ability of using a combination of single lead ECG and 3-axis seismocardiography (SCG) from a sensor placed on a subject's sternum to automatically extract CTIs. For each subject, pre-ejection period (PEP), left ventricular ejection time ($L$ VET), total systolic time $(TST)$, and total diastolic time $(TDT)$, which were normalized by the mean heart rate representing the entire recording were extracted using a custom developed algorithm. LVET was on average 20.5 % shorter in the NKHCD group $vs$ PRE-TAVI $(p< 0.05)$) and 5.9% shorter in the $HCD$ group $vs$ PRE-TAVI $(p> 0.05)$). Comparing CTIs between the subjects who had data recorded before and after receiving a TAVI procedure, $a$ 12.6% postoperative reduction in LVET $(p < 0.05)$ was found on average as well as a 30.2% increase in $PEP/L$ VET $(p < 0.05)$. These results are in line with literature where LVET increases with age and severe aortic stenosis and decreases after TAVI procedures when echocardiography was the main methodology used to extract CTIs.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"22 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":"130679575","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 Workflow for Probabilistic Calibration of Models of Left Atrial Electrophysiology 左心房电生理模型的概率校准工作流程

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.283

S. Coveney, C. Corrado, C. Roney, Richard D. Wilkinson, J. Oakley, S. Niederer, R. Clayton

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

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