2022 Computing in Cardiology (CinC)最新文献_第5页

Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets 提供QRS起跳和偏移的超高频ECG深度学习心跳检测器

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.230

Zuzana Koscova, R. Smíšek, P. Nejedly, J. Halámek, P. Jurák, P. Leinveber, K. Čurila, F. Plesinger

{"title":"Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets","authors":"Zuzana Koscova, R. Smíšek, P. Nejedly, J. Halámek, P. Jurák, P. Leinveber, K. Čurila, F. Plesinger","doi":"10.22489/CinC.2022.230","DOIUrl":"https://doi.org/10.22489/CinC.2022.230","url":null,"abstract":"Background: QRS duration is a common measure linked to conduction abnormalities in heart ventricles. Aim: We propose a QRS detector, further able to locate QRS onset and offset in one inference step. Method: A 3-second window from 12 leads of UHF ECG signal (5 kHz) is standardized and processed with the UNet network. The output is an array of QRS probabilities, further processed with probability and distance criterion, allowing us to determine duration and final location of QRSs. Results: The model was trained on 2,250 ECG recordings from the FNUSA-ICRC hospital (Brno, Czechia). The model was tested on 5 different datasets: FNUSA, a dataset from FNKV hospital (Prague, Czechia), and three public datasets (Cipa, Strict LBBB, LUDB). Regarding QRS duration, results showed a mean absolute error of 13.99 ± 4.29 ms between annotated durations and the output of the proposed model. A QRS detection F-score was 0.98 ± 0.01. Conclusion: Our results indicate high QRS detection performance on both spontaneous and paced UHF ECG data. We also showed that QRS detection and duration could be combined in one deep learning algorithm.","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":"128936763","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

PhysioTag: An Open-Source Platform for Collaborative Annotation of Physiological Waveforms 生理波形协同标注的开源平台PhysioTag

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.335

L. McCullum, Hasan Saeed, Benjamin Moody, D. Perry, Eric Gottlieb, T. Pollard, Xavier Borrat Frigola, Qiao Li, Gari D. Clifford, R. Mark, Li-wei H. Lehman

引用次数: 0

Heart Murmur Detection Using Ensemble of Deep Learning Classifiers for Phonocardiograms Recorded from Multiple Auscultation Locations 使用深度学习分类器对多个听诊位置记录的心音图进行心脏杂音检测

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.241

S. Parvaneh, Zaniar Ardalan, Joomyung Song, Kathan Vyas, C. Potes

{"title":"Heart Murmur Detection Using Ensemble of Deep Learning Classifiers for Phonocardiograms Recorded from Multiple Auscultation Locations","authors":"S. Parvaneh, Zaniar Ardalan, Joomyung Song, Kathan Vyas, C. Potes","doi":"10.22489/CinC.2022.241","DOIUrl":"https://doi.org/10.22489/CinC.2022.241","url":null,"abstract":"A digital phonocardiogram (PCG) provides an opportunity for automated screening in resource-constrained environments. As part of the George B. Moody PhysioNet Challenge 2022, our team, Life_Is _Now, developed a computational approach using an ensemble of deep learning classifiers for identifying abnormal cardiac function from PCG. A stratified 5-fold cross-validation was used for model development and evaluation for murmur and clinical outcome identification. The backbone of our trained classifiers is a modified pre-trained deep convolutional neural network on AudioSet-Youtube corpus (YAMNet) and transfer learning. The YAMNet model is modified and finetuned on the publicly available PhysioNet dataset. Our murmur and clinical outcome classifiers received a weighted accuracy score of 0.831 and a Challenge cost score of 14,850 from cross-validation on the public training set. Our murmur scores were 0.678 and outcome score were 10,518 on the hidden validation set. However, we did not receive the official score for the hidden test set as our entry crashed in evaluation on the test set.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"17 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":"130490383","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 Novel Human Atrial Electromechanical Cardiomyocyte Model with Mechano-Calcium Feedback Effect 一种具有机械钙反馈效应的新型人心房机电心肌细胞模型

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.195

"Fazeelat Mazhar, Francesco Regazzoni, C. Bartolucci, C. Corsi, L. Dede’, A. Quarteroni, S. Severi

引用次数: 0

Does Ectopic Beats Bring More Discriminatory Information to Diagnose Ischemic Heart Disease? 异位心跳是否为缺血性心脏病的诊断提供了更多的歧视性信息?

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.199

Katerina Iscra, A. Miladinović, M. Ajčević, Luca Restivo, Simone Kresevic, M. Merlo, G. Sinagra, A. Accardo

{"title":"Does Ectopic Beats Bring More Discriminatory Information to Diagnose Ischemic Heart Disease?","authors":"Katerina Iscra, A. Miladinović, M. Ajčević, Luca Restivo, Simone Kresevic, M. Merlo, G. Sinagra, A. Accardo","doi":"10.22489/CinC.2022.199","DOIUrl":"https://doi.org/10.22489/CinC.2022.199","url":null,"abstract":"Early non-invasive diagnosis of Ischemic Heart Disease (IHD) can often be challenging. HRV features have a potentially important role in risk stratification for subjects with suspected heart disease. However, there is no consensus on the HRV preprocessing steps, particularly on how to properly treat ectopic beats. We aimed to investigate the performance of the models for classification of early IHD versus healthy subjects (HC) based on HRV features extracted from signals excluding ectopic beats and based on the same features extracted from the signals that contain both ectopic and normal heartbeats. This study encompassed 385 subjects (170 IHD and 215 HC). The models were produced by logistic regression method considering two sets of HRV features obtained by two preprocessing approaches. The results showed that the model with the input features from HRV signals including normal and ectopic beats presented a higher classification accuracy (72.7%) than the model based on features extracted only from normal heart beats (67.8%). In addition, the evaluation of the feature importance by analysis of produced nomograms and observed significant differences between features extracted with two preprocessing approaches, showed also that the exclusion of the ectopic beats modifies the features' discriminatory power between HC and IHD.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"29 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":"126453914","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

Comparison of Newtonian and Non-Newtonian Blood Flow in an Ascending Aortic Aneurysm 升主动脉瘤牛顿与非牛顿血流的比较

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.085

A. Petuchova, A. Maknickas

引用次数: 1

Cellular Heterogeneity in the Atria: An In Silico Study in the Impact on Reentries 心房细胞异质性:对再入影响的计算机研究

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.296

J. Elliott, Daniele Cinque, L. Mainardi, J. F. R. Matas

{"title":"Cellular Heterogeneity in the Atria: An In Silico Study in the Impact on Reentries","authors":"J. Elliott, Daniele Cinque, L. Mainardi, J. F. R. Matas","doi":"10.22489/CinC.2022.296","DOIUrl":"https://doi.org/10.22489/CinC.2022.296","url":null,"abstract":"In-silico modelling is increasingly relied upon to gain new insights into the underlying mechanisms of atrial fibrillation. Due to the complex nature of the atria, insilico models typically exclude cellular heterogeneity. One question that remains unanswered is the impact of cellular heterogeneity on reentrant mechanisms and in the vulnerable window (VW). This study aims to present the impact of cellular heterogeneity on the AF mechanisms and susceptibility to re-entry behaviour. Cellular heterogeneity was introduced into the whole atrial model using the population of models approach and regionally specific node assignment. Each atrial model was stimulated from the SA node, followed by a series of rapid-paced ectopic beats at one of three locations in the left atria. Results showed a small, insignificant increase in reentrant frequency as a result of cellular heterogeneity, with only minor changes to the re-entrant circuit. However, the vulnerable window was significantly impacted through the introduction of cellular heterogeneity. The results suggest that cellular heterogeneity in the atrial model resulted in an increased VW for reentry depending on EB location. This suggests that local cellular heterogeneity plays a significant role in the susceptibility to re-entries, but does not significantly impact the path or frequency of re-entries.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"34 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":"122224322","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

Association Between Photoplethysmography Pulse Upslope and Cardiovascular Events in over 170,000 UK Biobank Participants 在超过170,000名英国生物银行参与者中，光容积脉搏图脉冲上坡与心血管事件之间的关系

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.226

"Michele Orini, S. van Duijvenboden, A. Tinker, P. Munroe, P. Lambiase

{"title":"Association Between Photoplethysmography Pulse Upslope and Cardiovascular Events in over 170,000 UK Biobank Participants","authors":"\"Michele Orini, S. van Duijvenboden, A. Tinker, P. Munroe, P. Lambiase","doi":"10.22489/CinC.2022.226","DOIUrl":"https://doi.org/10.22489/CinC.2022.226","url":null,"abstract":"Photoplethysmography (PPG) is used in many wearable devices and it is becoming the most commonly measured cardiovascular signal, but its association with cardiovascular events is undetermined. This study uses data from the UK Biobank to assess the association between PPG morphological features and risk of cardiovascular (CV) events. N=175,284 individuals without CV disease were included (44.6% male, $56.4pm 8.1$ years old). A single finger PPG waveform of 101 data points, evenly sampled over the cycle length was available. The PPG waveforms were normalized between 0 and 1 and the maximum of the first derivative during the pulse's upslope was measured $(x_{MAX}^{prime})$. Cox regressions were used to assess the association between $x_{MAX}^{prime}$ and mortality and cardiovascular events. After a median follow-up period of 11.2 years, incidence of all-cause mortality (ACM), myocardial infarction (MI), heart failure (HF), atrial fibrillation (AF) and stroke (STR), ranged between 2.1% and 5.2%. A reduction of 1 standard deviation in $x_{MAX}^{prime}$ was associated with increased risk of all outcomes with hazard ratio between 1.20 and 1.30. After adjusting for sex, age, and body mass index, associations remained significant for all outcomes except AF.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"80 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":"122290021","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

AI-Enabled ECG Combined with Dry Electrode Sensors for Population-Based Screening of Atrial Fibrillation 人工智能心电图结合干电极传感器用于人群心房颤动筛查

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.312

Alan Kennedy, D. Finlay, R. Bond, D. Guldenring, J. Mclaughlin, Chris Crockford"

{"title":"AI-Enabled ECG Combined with Dry Electrode Sensors for Population-Based Screening of Atrial Fibrillation","authors":"Alan Kennedy, D. Finlay, R. Bond, D. Guldenring, J. Mclaughlin, Chris Crockford\"","doi":"10.22489/CinC.2022.312","DOIUrl":"https://doi.org/10.22489/CinC.2022.312","url":null,"abstract":"This study assessed the performance of a deep neural network (PulseAI, Belfast, United Kingdom) used in conjunction with a dry-electrode ECG sensor device (RhythmPad, D&FT, United Kingdom) to detect AF automatically. Simultaneous pairs of 12-lead ECGs and single-lead dry-electrode ECGs were collected from 622 patients. The 12-lead ECGs were manually overread and used as reference diagnoses. Twenty-two patients were confirmed with AF and had an interpretable 12-lead and single-lead dry-electrode ECG recording. The deep neural network analysed the dry-electrode ECGs, and performance was compared to the 12-lead interpretation. Overall, the deep neural network algorithm yielded a sensitivity of 96% (95% CI, 87%-100%), specificity of 99% (95% CI, 98%-100%) and positive predictive value of 81% (95% CI, 66%-96%) for detection of AF episodes. When coupled with dry-electrode ECG sensors, the PulseAI neural network allows for large-scale and low-cost screening for AF. Widespread implementation of this technology may allow for earlier detection, treatment, and management of patients with AF.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"69 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":"121240415","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

Pulse Wave Analysis of Photoplethysmography Signals to Enhance Classification of Cardiac Arrhythmias 光容积脉搏波信号的脉搏波分析增强心律失常的分类

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.023

Loïc Jeanningros, F. Braun, J. V. Zaen, M. L. Bloa, A. Porretta, C. Teres, C. Herrera, G. Domenichini, Patrice Carroz, D. Graf, P. Pascale, J. Vesin, J. Thiran, E. Pruvot, M. Lemay

{"title":"Pulse Wave Analysis of Photoplethysmography Signals to Enhance Classification of Cardiac Arrhythmias","authors":"Loïc Jeanningros, F. Braun, J. V. Zaen, M. L. Bloa, A. Porretta, C. Teres, C. Herrera, G. Domenichini, Patrice Carroz, D. Graf, P. Pascale, J. Vesin, J. Thiran, E. Pruvot, M. Lemay","doi":"10.22489/CinC.2022.023","DOIUrl":"https://doi.org/10.22489/CinC.2022.023","url":null,"abstract":"Photoplethysmography (PPG) has recently gained increasing interest for less obtrusive long-term cardiovascular monitoring. As for cardiac arrhythmia (CA), most research and available PPG devices have focused on the detection of atrial fibrillation (AF), the most common CA. However, other less studied CAs can induce errors in standard AF detectors. To address the PPG-based detection of both AF and non-AF CAs, we investigate novel features, extracted by pulse wave analysis (PWA), that provide insight into the morphology of individual pulses. Their discriminative power was evaluated based on the RELIEFF algorithm for feature selection, and we compared performance metrics for CA classification with and without PWA features. The classification accuracy using ridge regression was increased by 0.4%, from 75.6% to 76.0%, when using PWA features on top of temporal and spectral features. Likewise, the classification of non-AF CAs was globally improved. These results show the potential of extracting measures about individual pulse morphologies to improve detection of various CAs.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"110 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":"121363541","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