Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662749
Achal Dixit, Soumili Chattopadhyay
Treating Heart Failure (HF) patients with mid-range Ejection Fraction (HFmrEF) is a challenging task due to prognostic uncertainty and transitional behaviour of HFmrEF, often referred to as “grey-area”. In this study, we address the uncertainty of HFmrEF through Machine Learning (ML) by classifying it into two well studied phenotypes: HF with preserved Ejection Fraction and HF with reduced Ejection Fraction, using the data from clinical attributes. We propose a semi-supervised Active Learning based model that uses significantly lesser data to tackle the need of supervised label validation and performs on-par with supervised ML models developed for comparison. We believe the use of proposed ML models can enable experts in making informed data-driven decisions leading to the accurate prognosis of HF patients.
{"title":"Demystifying Heart Failure with Mid-Range Ejection Fraction Using Machine Learning","authors":"Achal Dixit, Soumili Chattopadhyay","doi":"10.23919/cinc53138.2021.9662749","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662749","url":null,"abstract":"Treating Heart Failure (HF) patients with mid-range Ejection Fraction (HFmrEF) is a challenging task due to prognostic uncertainty and transitional behaviour of HFmrEF, often referred to as “grey-area”. In this study, we address the uncertainty of HFmrEF through Machine Learning (ML) by classifying it into two well studied phenotypes: HF with preserved Ejection Fraction and HF with reduced Ejection Fraction, using the data from clinical attributes. We propose a semi-supervised Active Learning based model that uses significantly lesser data to tackle the need of supervised label validation and performs on-par with supervised ML models developed for comparison. We believe the use of proposed ML models can enable experts in making informed data-driven decisions leading to the accurate prognosis of HF patients.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"1119 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":"116070328","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}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662723
P. Nejedly, Adam Ivora, R. Smíšek, I. Viscor, Zuzana Koscova, P. Jurák, F. Plesinger
This paper introduces a winning solution (team ISIBrno-AIMT) to the PhysioNet Challenge 2021. The method is based on the ResNet deep neural network architecture with a multi-head attention mechanism for ECG classification into 26 independent groups. The model is optimized using a mixture of loss functions, i.e., binary cross-entropy, custom challenge score loss function, and sparsity loss function. Probability thresholds for each classification class are estimated using the evolutionary optimization method. The final model consists of three submodels forming a majority voting classification ensemble. The proposed method classifies ECGs with a variable number of leads, e.g., 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead. The algorithm was validated and tested on the external hidden datasets (CPSC, G12EC, undisclosed set, UMich), achieving a challenge score 0.58 for all tested lead configurations. The total training time was approximately 27 hours, i.e., 9 hours per model. The presented solution was ranked first across all 39 teams in all categories.
{"title":"Classification of ECG Using Ensemble of Residual CNNs with Attention Mechanism","authors":"P. Nejedly, Adam Ivora, R. Smíšek, I. Viscor, Zuzana Koscova, P. Jurák, F. Plesinger","doi":"10.23919/cinc53138.2021.9662723","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662723","url":null,"abstract":"This paper introduces a winning solution (team ISIBrno-AIMT) to the PhysioNet Challenge 2021. The method is based on the ResNet deep neural network architecture with a multi-head attention mechanism for ECG classification into 26 independent groups. The model is optimized using a mixture of loss functions, i.e., binary cross-entropy, custom challenge score loss function, and sparsity loss function. Probability thresholds for each classification class are estimated using the evolutionary optimization method. The final model consists of three submodels forming a majority voting classification ensemble. The proposed method classifies ECGs with a variable number of leads, e.g., 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead. The algorithm was validated and tested on the external hidden datasets (CPSC, G12EC, undisclosed set, UMich), achieving a challenge score 0.58 for all tested lead configurations. The total training time was approximately 27 hours, i.e., 9 hours per model. The presented solution was ranked first across all 39 teams in all categories.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"59 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":"124879801","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}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662820
J. Lyle, M. Nandi, P. Aston
The electrocardiogram (ECG) appears highly individual in nature. By applying the Symmetric Projection Attractor Reconstruction (SPAR) method, we obtain a unique visualisation of an individual's ECG and show how the subtle inter- and intra-individual differences observed may be quantified. This preliminary study supports further development of the novel SPAR approach for patient stratification and monitoring.
{"title":"Symmetric Projection Attractor Reconstruction: Inter-Individual Differences in the ECG","authors":"J. Lyle, M. Nandi, P. Aston","doi":"10.23919/cinc53138.2021.9662820","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662820","url":null,"abstract":"The electrocardiogram (ECG) appears highly individual in nature. By applying the Symmetric Projection Attractor Reconstruction (SPAR) method, we obtain a unique visualisation of an individual's ECG and show how the subtle inter- and intra-individual differences observed may be quantified. This preliminary study supports further development of the novel SPAR approach for patient stratification and monitoring.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"30 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":"131575528","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}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662804
M. Pérez-Zabalza, L. García-Mendívil, Kostantinos A Mountris, N. Smisdom, José M. Vallejo-Gil, Pedro C. Fresneda-Roldán, Javier Fañanás-Mastral, Marta Matamala-Adell, Fernando Sorribas-Berjón, Manuel Vázquez-Sancho, Javier André Bellido-Morales, Francisco Javier Mancebón-Sierra, Alexánder Sebastián Vaca-Núñez, C. Ballester-Cuenca, A. Oliván-Viguera, L. Ordovás, Emilio L. Pueyo
Aging is known to involve alterations in the composition and organization of the extracellular matrix, which have an impact on heart function. However, there is not a comprehensive description of how collagen characteristics vary with age in the human left ventricle (LV) and its impact on electrophysiological properties. Here, we quantified the amount and spatial organization of collagen from human LV second harmonic generation (SHG) microscopy images of middle-age and elderly individuals. The results were input to in silico models of human LV tissues and numerical simulations were conducted to characterize the effects on electrical conduction and repolarization. Results from SHG image processing showed an increase in the amount of collagen and in its clustering in LV tissues with age. The increase in the amount of fibrosis induced a clear decrease in conduction velocity (CV), whereas increased clustering did not impact CV in our simulated tissues. In terms of ventricular repolarization, we observed a remarkable reduction in action potential duration (APD) as the percentage of fibrosis increased and a slighter reduction with increasing clustering. Importantly, more clustered fibrosis had a major effect on the enhancement of spatial APD dispersion, which was, however, diminished with increased fibrosis percentage. As a conclusion, both the amount and spatial organization offibrosis in human LV tissues have a relevant role in electrophysiological properties.
{"title":"Age-associated changes in fibrosis amount and spatial organization and its effects on human ventricular electrophysiology","authors":"M. Pérez-Zabalza, L. García-Mendívil, Kostantinos A Mountris, N. Smisdom, José M. Vallejo-Gil, Pedro C. Fresneda-Roldán, Javier Fañanás-Mastral, Marta Matamala-Adell, Fernando Sorribas-Berjón, Manuel Vázquez-Sancho, Javier André Bellido-Morales, Francisco Javier Mancebón-Sierra, Alexánder Sebastián Vaca-Núñez, C. Ballester-Cuenca, A. Oliván-Viguera, L. Ordovás, Emilio L. Pueyo","doi":"10.23919/cinc53138.2021.9662804","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662804","url":null,"abstract":"Aging is known to involve alterations in the composition and organization of the extracellular matrix, which have an impact on heart function. However, there is not a comprehensive description of how collagen characteristics vary with age in the human left ventricle (LV) and its impact on electrophysiological properties. Here, we quantified the amount and spatial organization of collagen from human LV second harmonic generation (SHG) microscopy images of middle-age and elderly individuals. The results were input to in silico models of human LV tissues and numerical simulations were conducted to characterize the effects on electrical conduction and repolarization. Results from SHG image processing showed an increase in the amount of collagen and in its clustering in LV tissues with age. The increase in the amount of fibrosis induced a clear decrease in conduction velocity (CV), whereas increased clustering did not impact CV in our simulated tissues. In terms of ventricular repolarization, we observed a remarkable reduction in action potential duration (APD) as the percentage of fibrosis increased and a slighter reduction with increasing clustering. Importantly, more clustered fibrosis had a major effect on the enhancement of spatial APD dispersion, which was, however, diminished with increased fibrosis percentage. As a conclusion, both the amount and spatial organization offibrosis in human LV tissues have a relevant role in electrophysiological properties.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"46 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":"127676994","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}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662800
Mohamadamin Forouzandehmehr, C. Bartolucci, J. Hyttinen, Jussi T. Koivumäki, M. Paci
Myocardial acute ischemia is due to a reduced or suppressed blood supply to the heart. It heavily impacts the electrical and mechanical functionality of cardiomyocytes (CMs), up to cell necrosis. We evaluate the effects of the three main consequences of acute ischemia (hypoxia, acidosis, and hyperkalemia) on the recent Bartolucci-Passini-Severi (BPS2020) model of human adult ventricular CM. We run a sensitivity analysis considering different ischemia severity, mechanisms, and formulations of the ATP-sensitive K+ current (IKATP), initially not included in BPS2020. We further compare our results with other in silico and in vitro data and evaluate the BPS2020 capability to simulate alternans in ischemia. Hyperkalemia remarkably depolarized the resting membrane potential and reduced the maximum upstroke velocity. Acidosis slightly shortened the action potential (AP) duration. Hypoxia mainly reduced the AP duration and its peak. Our results agree with simulations performed with other in silico models. Finally, the full ischemia model produced alternans at fast pacing. Our sensitivity analysis demonstrates that the BPS2020 model correctly recapitulates the acute ischemia effects, and it is suitable for more advanced simulations.
{"title":"Sensitivity of the Human Ventricular BPS2020 Action Potential Model to the In Silico Mechanisms of Ischemia","authors":"Mohamadamin Forouzandehmehr, C. Bartolucci, J. Hyttinen, Jussi T. Koivumäki, M. Paci","doi":"10.23919/cinc53138.2021.9662800","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662800","url":null,"abstract":"Myocardial acute ischemia is due to a reduced or suppressed blood supply to the heart. It heavily impacts the electrical and mechanical functionality of cardiomyocytes (CMs), up to cell necrosis. We evaluate the effects of the three main consequences of acute ischemia (hypoxia, acidosis, and hyperkalemia) on the recent Bartolucci-Passini-Severi (BPS2020) model of human adult ventricular CM. We run a sensitivity analysis considering different ischemia severity, mechanisms, and formulations of the ATP-sensitive K+ current (IKATP), initially not included in BPS2020. We further compare our results with other in silico and in vitro data and evaluate the BPS2020 capability to simulate alternans in ischemia. Hyperkalemia remarkably depolarized the resting membrane potential and reduced the maximum upstroke velocity. Acidosis slightly shortened the action potential (AP) duration. Hypoxia mainly reduced the AP duration and its peak. Our results agree with simulations performed with other in silico models. Finally, the full ischemia model produced alternans at fast pacing. Our sensitivity analysis demonstrates that the BPS2020 model correctly recapitulates the acute ischemia effects, and it is suitable for more advanced simulations.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"194 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":"133465721","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}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662785
Matteo Falanga, A. Masci, A. Chiaravalloti, F. Ansaloni, C. Tomasi, C. Corsi
Atrial Fibrillation (AF) is associated with a five-fold increase in the risk of cerebrovascular events. Recent studies suggest that a computational fluid-dynamics (CFD) approach could provide insights on AF mechanisms thus potentially allowing a quantitative assessment of cardioembolic risk. The goal of this study was to use a previously developed patient specific CFD model of the left atrium (LA) to enhance differences in blood flow in AF patients and normal subjects. In this study we computed LA blood flow and derived parameters in normal subjects (NL), patients affected by paroxysmal AF (PAR-AF) and patients affected by persistent AF (PER-AF). Results showed mean peak velocities continuously decreasing from NL to PER-AF groups. In agreement, a lower number of vortex structures was observed in PER-AF with respect to PAR-AF and NL, thus limiting an effective washout of the LA and the left atrial appendage (LAA). Velocities at the LAA ostium and inside the LAA were also strongly reduced showing a limited washout effect as confirmed by blood stasis in terms of number of particles still present after five cardiac cycles (NL: 5±2, PAR-AF: 18±3, PER-AF: 41±10). The developed approach quantifies differences in LA hemodynamic between AF patients and NL.
{"title":"Left Atrium Hemodynamic in Atrial Fibrillation and Normal Subjects","authors":"Matteo Falanga, A. Masci, A. Chiaravalloti, F. Ansaloni, C. Tomasi, C. Corsi","doi":"10.23919/cinc53138.2021.9662785","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662785","url":null,"abstract":"Atrial Fibrillation (AF) is associated with a five-fold increase in the risk of cerebrovascular events. Recent studies suggest that a computational fluid-dynamics (CFD) approach could provide insights on AF mechanisms thus potentially allowing a quantitative assessment of cardioembolic risk. The goal of this study was to use a previously developed patient specific CFD model of the left atrium (LA) to enhance differences in blood flow in AF patients and normal subjects. In this study we computed LA blood flow and derived parameters in normal subjects (NL), patients affected by paroxysmal AF (PAR-AF) and patients affected by persistent AF (PER-AF). Results showed mean peak velocities continuously decreasing from NL to PER-AF groups. In agreement, a lower number of vortex structures was observed in PER-AF with respect to PAR-AF and NL, thus limiting an effective washout of the LA and the left atrial appendage (LAA). Velocities at the LAA ostium and inside the LAA were also strongly reduced showing a limited washout effect as confirmed by blood stasis in terms of number of particles still present after five cardiac cycles (NL: 5±2, PAR-AF: 18±3, PER-AF: 41±10). The developed approach quantifies differences in LA hemodynamic between AF patients and NL.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"54 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":"133614442","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}
Pub Date : 2021-09-13DOI: 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
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.
PhysioNet/Computing in Cardiology Challenge 2021的目标是从12导联和减少导联的心电图记录中识别临床诊断，包括6导联、4导联、3导联和2导联记录。我们的团队snu_adsl使用了EfficientNet-B3作为基础深度学习模型，并研究了包括数据增强、自监督学习作为预训练、处理多个数据源的标签屏蔽、阈值优化和特征提取在内的方法。当标记数据集的大小有限时，自监督学习显示出有希望的结果，但竞争对手的数据集足够大，实际收益是边际的。因此，我们在最终的条目中没有包括自我监督的预训练。我们的分类器获得了0.48,0.48,0.47,0.47和0.45的分数(在39个团队中排名第12，第10，第11，第11和第13)，用于12-lead, 6-lead, 4-lead, 3-lead和2-lead版本的隐藏测试集与挑战评估指标。
{"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}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662649
M. Jennings, A. Rababah, Daniel Güldenring, J. Mclaughlin, D. Finlay
Background: There are limited datasets available to facilitate the evaluation of patch-based lead systems, so the leads must be derived from existing data, mainly the 12-lead ECG. We have previously introduced a short spaced lead (SSL) system consisting of two leads with the largest ST segment changes during ischaemic-type episodes. In this study, we aim to evaluate the derivation of this patch-based lead system from the 12-lead ECG. Method: Thoracic body surface potential maps (BSPM) were recorded from $n=734$ patients. Using Laplacian interpolation, each recording was expanded to the 352-node Dalhousie torso. The eight independent channels of the 12-lead ECG were extracted (I, II, V1-V6) with the two leads of the SSL patch Coefficients were derived using linear regression from the 12-lead ECG to the SSL patch. Results: The median Pearson correlation coefficients (CC) and root mean square error (RMSE) for each lead were calculated as follows (CC/RMSE): $0.986/74.3 mu V$ (ST monitoring lead); $0.976/65.3 mu V$ (spatially orthogonal lead). Conclusion: We have developed coefficients that allow the derivation of a patch-based lead system from the 12-lead ECG. Given the high correlation, it is possible to generate short spaced lead systems from existing diagnostic lead systems, however, amplitude errors are introduced in the process.
背景:可用于评估贴片导联系统的数据集有限，因此导联必须来自现有数据，主要是12导联心电图。我们之前介绍过一种短间隔导联(SSL)系统，该系统由两条导联组成，在缺血型发作期间ST段变化最大。在这项研究中，我们的目的是评估这种基于贴片的导联系统从12导联心电图的推导。方法:记录734例患者的胸椎体表电位图(BSPM)。使用拉普拉斯插值，每个记录被扩展到352节点的达尔豪西躯干。提取了12导联心电图的8个独立通道(I、II、V1-V6)，并利用SSL贴片的两导联提取了12导联心电图与SSL贴片的线性回归系数。结果:各导联的中位Pearson相关系数(CC)和均方根误差(RMSE)计算如下(CC/RMSE): $0.986/74.3 mu V$ (ST监测导联);$0.976/65.3 mu V$(空间正交引线)。结论:我们已经开发了系数，允许从12导联心电图中推导出基于贴片的导联系统。鉴于高相关性，可以从现有的诊断导联系统中生成短间隔导联系统，然而，在此过程中引入幅度误差。
{"title":"Coefficients for the Derivation of an ST Sensitive Patch Based Lead System from the 12 Lead Electrocardiogram","authors":"M. Jennings, A. Rababah, Daniel Güldenring, J. Mclaughlin, D. Finlay","doi":"10.23919/cinc53138.2021.9662649","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662649","url":null,"abstract":"Background: There are limited datasets available to facilitate the evaluation of patch-based lead systems, so the leads must be derived from existing data, mainly the 12-lead ECG. We have previously introduced a short spaced lead (SSL) system consisting of two leads with the largest ST segment changes during ischaemic-type episodes. In this study, we aim to evaluate the derivation of this patch-based lead system from the 12-lead ECG. Method: Thoracic body surface potential maps (BSPM) were recorded from $n=734$ patients. Using Laplacian interpolation, each recording was expanded to the 352-node Dalhousie torso. The eight independent channels of the 12-lead ECG were extracted (I, II, V1-V6) with the two leads of the SSL patch Coefficients were derived using linear regression from the 12-lead ECG to the SSL patch. Results: The median Pearson correlation coefficients (CC) and root mean square error (RMSE) for each lead were calculated as follows (CC/RMSE): $0.986/74.3 mu V$ (ST monitoring lead); $0.976/65.3 mu V$ (spatially orthogonal lead). Conclusion: We have developed coefficients that allow the derivation of a patch-based lead system from the 12-lead ECG. Given the high correlation, it is possible to generate short spaced lead systems from existing diagnostic lead systems, however, amplitude errors are introduced in the process.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"50 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":"132070980","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}
Pub Date : 2021-09-13DOI: 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
Thoracic bio-impedance (BioZ) measurements have been proposed as an alternative for respiratory monitoring. Given the ambulatory nature of this modality, it is more prone to noise sources. In this study, two pre-trained machine learning models were used to classify BioZ signals into clean and noisy classes. The models were trained on data from patients suffering from chronic obstructive pulmonary disease, and their performance was evaluated on data from patients undergoing bariatric surgery. Additionally, transfer learning (TL) was used to optimize the models for the new patient cohort. Lastly, the effect of different breathing patterns on the performance of the machine learning models was studied. Results showed that the models performed accurately when applying them to another patient population and their performance was improved by TL. However, different imposed respiratory frequencies were found to affect the performance of the models.
{"title":"Controlled Breathing Effect on Respiration Quality Assessment Using Machine Learning Approaches","authors":"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","doi":"10.23919/cinc53138.2021.9662854","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662854","url":null,"abstract":"Thoracic bio-impedance (BioZ) measurements have been proposed as an alternative for respiratory monitoring. Given the ambulatory nature of this modality, it is more prone to noise sources. In this study, two pre-trained machine learning models were used to classify BioZ signals into clean and noisy classes. The models were trained on data from patients suffering from chronic obstructive pulmonary disease, and their performance was evaluated on data from patients undergoing bariatric surgery. Additionally, transfer learning (TL) was used to optimize the models for the new patient cohort. Lastly, the effect of different breathing patterns on the performance of the machine learning models was studied. Results showed that the models performed accurately when applying them to another patient population and their performance was improved by TL. However, different imposed respiratory frequencies were found to affect the performance of the models.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"29 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":"131893804","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}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662847
Monika Butkuvienė, A. Petrėnas, Andrius Sološenko, A. Martín-Yebra, V. Marozas, L. Sörnmo
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.
{"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}
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