2020 Computing in Cardiology最新文献_第4页

Solving the ECGI Problem with Known Locations of Scar Regions 用已知疤痕区域的位置解决ECGI问题

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.237

M. M. Diallo, M. Potse, R. Dubois, Y. Coudière

引用次数: 2

Elements Read GUI: a Versatile Tool to Display and Analyse Electrophysiological Experimental Data 元素读取GUI:显示和分析电生理实验数据的通用工具

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.422

Eugenio Ricci, F. Cona, S. Severi

引用次数: 0

Unreadable Segment Recognition of Single-lead Dynamic Electrocardiogram Signals Based on Morphological Algorithm and Random Forest Classifier 基于形态学算法和随机森林分类器的单导联动态心电图信号不可读段识别

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.029

Hanshuang Xie, Huaiyu Zhu, Ji Zhao, Yisheng Zhao, Yun Pan

{"title":"Unreadable Segment Recognition of Single-lead Dynamic Electrocardiogram Signals Based on Morphological Algorithm and Random Forest Classifier","authors":"Hanshuang Xie, Huaiyu Zhu, Ji Zhao, Yisheng Zhao, Yun Pan","doi":"10.22489/CinC.2020.029","DOIUrl":"https://doi.org/10.22489/CinC.2020.029","url":null,"abstract":"Recognizing unreadable electrocardiogram (ECG) signals could reduce the error rate of automatic software analysis and improve the interpretation efficiency of doctors, especially for single-lead dynamic ECGs. In this paper, we propose an unreadable ECG segment recognition method based on morphological algorithm and random forest classifier (RFC). The single-lead ECG signals are first filtered and normalized for morphological opening and closing operation, to generate detection sequences with more obvious QRS waves, since the large amplitudes introduced by motion interference could be suppressed during this procedure. Then features such as Shannon entropy and kurtosis are extracted and the RFC is used for unreadable segment classification. A total of 3354 readable segments and 2199 unreadable segments with a length of 4 seconds are obtained from 37 patients for method evaluation. The accuracy of our method (92.94 ± 0.93%) is significantly higher than that of the method without morphological algorithm (85.68 ± 1.30%). Moreover, we also used the “N” and “~” categories of the database from PhysioNet/CinC Challenge 2017 for further verification, and the accuracy of the proposed method (93.75 ± 0.69%) is significantly higher than that of the model without morphological processing (82.25 ± 1.06%) as well.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114610378","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

Baroreflex Sensitivity Evolution Before Weaning From Mechanical Ventilation 脱离机械通气前的压力反射敏感性演变

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.235

Pablo Armañac, D. Hernando, J. Lázaro, C. D. Haro, R. Magrans, L. Sarlabous, J. López-Aguilar, P. Laguna, E. Gil, L. Blanch, R. Bailón

{"title":"Baroreflex Sensitivity Evolution Before Weaning From Mechanical Ventilation","authors":"Pablo Armañac, D. Hernando, J. Lázaro, C. D. Haro, R. Magrans, L. Sarlabous, J. López-Aguilar, P. Laguna, E. Gil, L. Blanch, R. Bailón","doi":"10.22489/CinC.2020.235","DOIUrl":"https://doi.org/10.22489/CinC.2020.235","url":null,"abstract":"Weaning is the process of withdrawing mechanical ventilation at the Intensive Care Units. The problem is that around 20% of weaned patients were not actually ready for discontinuation. Studies suggest that vagal dysfunction is lower in patients successfully weaned. Therefore, the Baroreflex Sensitivity (BRS) and Heart Rate Variability (HRV) are estimated to see if they can provide additional information to improve the prediction of weaning outcomes. 9 successfully weaned patients (S-group) and 6 unsuccessfully weaned (F-group) were monitored in the last hour prior to the Spontaneous Breathing Trial. The BRS is estimated through spectral analysis, to obtain the a parameter in the low and high frequency bands, and through the capacity, C, estimated by the Bivariate Phase Rectified Signal Average (BPRSA) method. The current clinic parameters of weaning readiness do not show statistical differences. However, the capacity to changes of the BRS, C, estimated via BPRSA, exhibits significant differences between the two groups. Negative values of C, and with higher absolute values, were obtained for the S-group. Temporal indices of HRV also show differences, but not significant. These results suggest that BRS should be further explored for predicting weaning outcomes.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116293945","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

SE-ECGNet: Multi-scale SE-Net for Multi-lead ECG Data SE-ECGNet:多导联心电图数据的多尺度SE-Net

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.085

Jiabo Chen, Tianlong Chen, Bin Xiao, Xiuli Bi, Yongchao Wang, Han Duan, Weisheng Li, Junhui Zhang, Xu Ma

{"title":"SE-ECGNet: Multi-scale SE-Net for Multi-lead ECG Data","authors":"Jiabo Chen, Tianlong Chen, Bin Xiao, Xiuli Bi, Yongchao Wang, Han Duan, Weisheng Li, Junhui Zhang, Xu Ma","doi":"10.22489/CinC.2020.085","DOIUrl":"https://doi.org/10.22489/CinC.2020.085","url":null,"abstract":"Cardiovascular disease is a life-threatening condition, and more than 20 million people die from heart disease. Therefore, developing an objective and efficient computer-aided tool for diagnosis of heart disease has become a promising research topic. In this paper, we design a multi-scale shared convolution kernel model. In this model, two paths are designed to extract the features of electrocardiogram(ECG). The two paths have different convolution kernel sizes, which are 3×1 and 5×1, respectively. Such multi-scale design enables the network to obtain different receptive fields and capture information at different scales, which significantly improves the classification effect. And squeeze-and-excitation networks (SE-Net) are added to every path of the model. The attention mechanism of SE-Net learns feature weights according to loss, which makes the effective feature maps have large weights and the ineffective or low-effect feature maps have small weights. Our team name is CQUPT_ECG. Our approach achieved a challenge validation score of 0.640, and full test score of 0.411, placing us 8 out of 41 in the official ranking.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116432365","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: 8

Diffuse and Stringy Fibrosis in a Bilayer Interconnected Cable Model of the Left Atrium 左心房双层互联索模型的弥漫性和纤维性纤维化

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.450

Ariane Saliani, V. Jacquemet

引用次数: 2

Fusion of Multiple Univariate Data Analysis-based Detectors to Build a Specific Fingerprint of Atrial Fibrillation 基于单变量数据分析的多个检测器融合构建心房颤动特异性指纹图谱

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.449

Z. Haddi, B. Ananou, Youssef Trardi, S. Delliaux, J. Deharo, M. Ouladsine

{"title":"Fusion of Multiple Univariate Data Analysis-based Detectors to Build a Specific Fingerprint of Atrial Fibrillation","authors":"Z. Haddi, B. Ananou, Youssef Trardi, S. Delliaux, J. Deharo, M. Ouladsine","doi":"10.22489/CinC.2020.449","DOIUrl":"https://doi.org/10.22489/CinC.2020.449","url":null,"abstract":"Automatic and fast atrial fibrillation (AF) diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. Although the published results do show satisfactory detection accuracy, computational complexity of such methods is still questionable. This study proposes an alternative way to diagnosis AF arrhythmia which is based on the combination of seven univariate data analysis-based detectors followed by a majority voting in order to build a digital fingerprint of AF. Four publicly-accessible sets of clinical data were used for AF assessment. The time series were segmented in 10 s RR interval window. The features of the four databases were merged in order to give rise huge variability and therefore to better characterize AF arrhythmia. Afterwards, a receiver operating characteristic curve analysis has been conducted to fix optimal thresholds for AF detection. Finally, the seven obtained detectors have been concatenated and then a majority rule was applied to yield a final decision on AF diagnosis. The results showed that this strategy performed better than some existing algorithms do, with 98.50% for sensitivity and 95.1 % specificity.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124461054","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

False Alarm Reduction in Atrial Fibrillation Screening 减少房颤筛查中的误报

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.255

Hesam Halvaei, E. Svennberg, L. Sörnmo, M. Stridh

引用次数: 3

A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification 用于12导联心电分类的宽深变压器神经网络

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.107

A. Natarajan, Yale Chang, S. Mariani, Asif Rahman, G. Boverman, S. Vij, J. Rubin

引用次数: 75

Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification 卷积递归神经网络与LightGBM集成模型在12导联心电图分类中的应用

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.417

Charilaos A. Zisou, Andreas Sochopoulos, Konstantinos Kitsios

引用次数: 6