Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662818
M. Lange, Eugene Kwan, R. MacLeod, R. Ranjan
Personalized computational models used to guide ablation heavily depend on late gadolinium enhanced images for scar and gray area estimation. The estimation has a high degree of uncertainty, but it is unclear how sensitive the simulation outcome is to the specific scar. In this work, we study the sensitivity of the simulation outcome on the scar. Two personalized left atrial models were generated for a de-novo and a redo atrial. In control setting scar and gray area were obtained by thresholding LGE-MRI images at 70%, and 60% of the maximum myocardial intensity, respectively. This was compared against segmentations, generated by dilating, or eroding the control segmentation by one pixel, and increasing or decreasing the threshold by 5%. The outcomes were normal capture without further activity, extra beats with additional activity but not sustained, sustained arrhythmia with activity until the end of the simulation, and no capture. We found normally captured beats were not affected in redo cases but did change in de-novo ablation. However, extra beats were likely to change to arrhythmia when adding or subtracting scar. Sustained arrhythmia was sensitive to a reduction in scar size. This reiterates that attention is need when determining appropriate thresholds for scar and gray area.
{"title":"Computer Simulations Outcomes of Left Atrial Arrhythmia Induction are Highly Sensitive to Scar and Fibrosis Determination","authors":"M. Lange, Eugene Kwan, R. MacLeod, R. Ranjan","doi":"10.23919/cinc53138.2021.9662818","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662818","url":null,"abstract":"Personalized computational models used to guide ablation heavily depend on late gadolinium enhanced images for scar and gray area estimation. The estimation has a high degree of uncertainty, but it is unclear how sensitive the simulation outcome is to the specific scar. In this work, we study the sensitivity of the simulation outcome on the scar. Two personalized left atrial models were generated for a de-novo and a redo atrial. In control setting scar and gray area were obtained by thresholding LGE-MRI images at 70%, and 60% of the maximum myocardial intensity, respectively. This was compared against segmentations, generated by dilating, or eroding the control segmentation by one pixel, and increasing or decreasing the threshold by 5%. The outcomes were normal capture without further activity, extra beats with additional activity but not sustained, sustained arrhythmia with activity until the end of the simulation, and no capture. We found normally captured beats were not affected in redo cases but did change in de-novo ablation. However, extra beats were likely to change to arrhythmia when adding or subtracting scar. Sustained arrhythmia was sensitive to a reduction in scar size. This reiterates that attention is need when determining appropriate thresholds for scar and gray area.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"18 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":"122147706","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/cinc53138.2021.9662693
Rui Rodrigues, Paula Couto
We present an approach for automatic cardiac abnormality detection using two leads ECG. This approach was developed in the context of the Physionet/Computing in Cardiology Challenge 2021. Our model is decomposed into an Encoder and a Decoder. It is a huge neural network model with more than 36 million parameters. Although the Challenge training dataset consists of more than 88 thousand annotated ECGs, our model is extremely prone to overfitting to the training data. The encoder is a convolution neural network followed by three transformer encoder blocks. The decoder is a transformer encoder block followed by a feed forward neural network. To reduce the overfitting, we pretrain the Encoder in a semi-supervised way on three tasks. Given an ECG segment, L1, the first task is to detect the QRS on L1; the second task is to predict the ECG shape on an ECG segment, L2 following L1, given the QRS location on $L_{2}$; the third task is to predict the number of samples, after $L_{1}$ , before the next QRS. The Decoder weights were firstly estimated with the frozen Endoder pre-trained parameters and then the whole model parameters were fine-tunned. Our team, named matFCT, received a challenge score of 0.43 on the official test dataset. However, we were unable to qualify for ranking because we weren't able to submit the preprint to the Computing in Cardiology Conference before the deadline.
{"title":"Semi-Supervised Learning for ECG Classification","authors":"Rui Rodrigues, Paula Couto","doi":"10.23919/cinc53138.2021.9662693","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662693","url":null,"abstract":"We present an approach for automatic cardiac abnormality detection using two leads ECG. This approach was developed in the context of the Physionet/Computing in Cardiology Challenge 2021. Our model is decomposed into an Encoder and a Decoder. It is a huge neural network model with more than 36 million parameters. Although the Challenge training dataset consists of more than 88 thousand annotated ECGs, our model is extremely prone to overfitting to the training data. The encoder is a convolution neural network followed by three transformer encoder blocks. The decoder is a transformer encoder block followed by a feed forward neural network. To reduce the overfitting, we pretrain the Encoder in a semi-supervised way on three tasks. Given an ECG segment, L1, the first task is to detect the QRS on L1; the second task is to predict the ECG shape on an ECG segment, L2 following L1, given the QRS location on $L_{2}$; the third task is to predict the number of samples, after $L_{1}$ , before the next QRS. The Decoder weights were firstly estimated with the frozen Endoder pre-trained parameters and then the whole model parameters were fine-tunned. Our team, named matFCT, received a challenge score of 0.43 on the official test dataset. However, we were unable to qualify for ranking because we weren't able to submit the preprint to the Computing in Cardiology Conference before the deadline.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"93 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":"122694890","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/cinc53138.2021.9662729
Jakub Hejc, D. Pospisil, Petra Novotna, M. Pešl, O. Janousek, M. Ronzhina, Z. Stárek
Timing pattern of intracardiac atrial activity recorded by multipolar catheter in the coronary sinus (CS) provides insightful information about the type and approximate origin of common non-complex arrhythmias. Depending on the anatomy of the CS, the atrial activity can be substantially disturbed by ventricular far field complex preventing accurate segmentation by convential methods. In this paper, we present small clinically validated database of 326 surface 12-lead and intracardiac electrograms (ECG and IEGs) and a simple deep learning framework for semantic beat-to-beat segmentation of atrial activity in CS recordings. The model is based on a residual convolutional neural network (CNN) combined with pyramidal upsampling decoder. It is capable to recognize well between atrial and ventricular signals recorded by decapolar CS catheter in multiple arrhythmic scenarios reaching dice score of 0.875 on evaluation dataset. To address a dataset size and imbalance issues, we have adopted several preprocessing and learning techniques with adequate evaluation of its impact on the model performance.
{"title":"Segmentation of Atrial Electrical Activity in Intracardiac Electrograms (IECGs) Using Convolutional Neural Network (CNN) Trained on Small Imbalanced Dataset","authors":"Jakub Hejc, D. Pospisil, Petra Novotna, M. Pešl, O. Janousek, M. Ronzhina, Z. Stárek","doi":"10.23919/cinc53138.2021.9662729","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662729","url":null,"abstract":"Timing pattern of intracardiac atrial activity recorded by multipolar catheter in the coronary sinus (CS) provides insightful information about the type and approximate origin of common non-complex arrhythmias. Depending on the anatomy of the CS, the atrial activity can be substantially disturbed by ventricular far field complex preventing accurate segmentation by convential methods. In this paper, we present small clinically validated database of 326 surface 12-lead and intracardiac electrograms (ECG and IEGs) and a simple deep learning framework for semantic beat-to-beat segmentation of atrial activity in CS recordings. The model is based on a residual convolutional neural network (CNN) combined with pyramidal upsampling decoder. It is capable to recognize well between atrial and ventricular signals recorded by decapolar CS catheter in multiple arrhythmic scenarios reaching dice score of 0.875 on evaluation dataset. To address a dataset size and imbalance issues, we have adopted several preprocessing and learning techniques with adequate evaluation of its impact on the model performance.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"16 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":"123989816","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/cinc53138.2021.9662947
Olivier Meste, S. Zeemering, Joël M. H. Karel, T. Lankveld, U. Schotten, H. Crijns, R. Peeters, P. Bonizzi
In contrast to electrograms, Body-Surface Potential Mapping (BSPM) records the global atrial activity, at the expenses of a lower spatial accuracy. The aim of this study is to investigate whether BSPM recordings can discriminate persistent patients undergoing electrical cardiover-sion, based on the body-surface normalized AF spatial frequency distribution. High-density BSPMs (120 anterior, 64 posterior electrodes) were recorded in 63 patients with persistent AF. For each patient and electrode recording, the frequency content of AF was analyzed on the raw signal, and also by means of the normalized correlation function, and by Singular Spectrum Analysis (SSA). In order to compare the body-surface spatial distributions of AF frequency in all patients, these distributions were first normalized, before performing statistical analysis. We found that the distribution of AF frequency on the body-surface, and its interpretation, are strongly dependent on the specific method employed. Moreover, the estimated body-surface AF frequency was greater over the central posterior and the right anterior BSPM locations. Finally, SSA-based decomposition followed by frequency analysis could discriminate AF patients recurring 4 to 6 weeks after electrical cardioversion from those who did not, based on the frequency content in the proximity of V1.
{"title":"Body-Surface Atrial Signals Analysis Based on Spatial Frequency Distribution: Comparison Between Different Signal Transformations","authors":"Olivier Meste, S. Zeemering, Joël M. H. Karel, T. Lankveld, U. Schotten, H. Crijns, R. Peeters, P. Bonizzi","doi":"10.23919/cinc53138.2021.9662947","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662947","url":null,"abstract":"In contrast to electrograms, Body-Surface Potential Mapping (BSPM) records the global atrial activity, at the expenses of a lower spatial accuracy. The aim of this study is to investigate whether BSPM recordings can discriminate persistent patients undergoing electrical cardiover-sion, based on the body-surface normalized AF spatial frequency distribution. High-density BSPMs (120 anterior, 64 posterior electrodes) were recorded in 63 patients with persistent AF. For each patient and electrode recording, the frequency content of AF was analyzed on the raw signal, and also by means of the normalized correlation function, and by Singular Spectrum Analysis (SSA). In order to compare the body-surface spatial distributions of AF frequency in all patients, these distributions were first normalized, before performing statistical analysis. We found that the distribution of AF frequency on the body-surface, and its interpretation, are strongly dependent on the specific method employed. Moreover, the estimated body-surface AF frequency was greater over the central posterior and the right anterior BSPM locations. Finally, SSA-based decomposition followed by frequency analysis could discriminate AF patients recurring 4 to 6 weeks after electrical cardioversion from those who did not, based on the frequency content in the proximity of V1.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"1 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":"125950028","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/cinc53138.2021.9662747
M. Dik, Resi M. Schoonderwoerd, S. Man, A. Maan, C. A. Swenne
Introduction. Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. We sought to validate this tenet by intra-individual comparison of the VG of sinus and ectopic beats, thus assessing both the effects of altered ventricular conduction and of restitution (caused by varying ectopic prematurity). Methods. We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles, who had either normally conducted supraventricular ectopic beats ($SN, N=6$), aberrantly conducted supraventricular ectopic beats ($SA, N=20$), or ventricular ectopic beats ($V, N=92$). We computed the ventricular gradient vectors of the predominant beat, VGp, of the ectopic beat, VGe, the VG difference vector, VGpe, and compared their sizes. Results. The VGe vectors of the SA and $V$ ectopic beats were significantly larger than the VGp vectors. The VGpe vectors were three times larger than the difference in size of the VGe and VGp vectors, demonstrating differences in the VGp and VGe spatial directions. Ectopic prematurity had no influence on these results. Discussion. Electrotonic interactions during repolarization form the likely explanation of our findings. Because of this electrophysiological mechanism, the concept of a conduction-independent ventricular gradient is untenable and cannot be used in ECG diagnostics.
{"title":"Validation of the Ventricular Gradient Comparing Sinus Beats and Ectopic Beats","authors":"M. Dik, Resi M. Schoonderwoerd, S. Man, A. Maan, C. A. Swenne","doi":"10.23919/cinc53138.2021.9662747","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662747","url":null,"abstract":"Introduction. Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. We sought to validate this tenet by intra-individual comparison of the VG of sinus and ectopic beats, thus assessing both the effects of altered ventricular conduction and of restitution (caused by varying ectopic prematurity). Methods. We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles, who had either normally conducted supraventricular ectopic beats ($SN, N=6$), aberrantly conducted supraventricular ectopic beats ($SA, N=20$), or ventricular ectopic beats ($V, N=92$). We computed the ventricular gradient vectors of the predominant beat, VGp, of the ectopic beat, VGe, the VG difference vector, VGpe, and compared their sizes. Results. The VGe vectors of the SA and $V$ ectopic beats were significantly larger than the VGp vectors. The VGpe vectors were three times larger than the difference in size of the VGe and VGp vectors, demonstrating differences in the VGp and VGe spatial directions. Ectopic prematurity had no influence on these results. Discussion. Electrotonic interactions during repolarization form the likely explanation of our findings. Because of this electrophysiological mechanism, the concept of a conduction-independent ventricular gradient is untenable and cannot be used in ECG diagnostics.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"28 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":"129454472","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/cinc53138.2021.9662757
Long Chen, Zheheng Jiang, T. Almeida, F. Schlindwein, Jakevir S. Shoker, G. Ng, Huiyu Zhou, Xin Li
Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our entry was not officially ranked and scored on the test data of the Challenge, because our code was not successfully processed during the official phase and failed to run with errors.
心功能障碍的自动检测与分类在临床心电图分析中起着至关重要的作用。深度学习方法是一种有效的自动特征提取方法，在心电分类中显示出良好的效果。在这项工作中，我们提出了一个深度时空ECG网络(ST-ECGNet)来提取鲁棒的时空特征，用于从多导联ECG数据中检测多种心脏疾病。所提出的ST-ECGNet结合了卷积神经网络(CNN)模块用于提取局部空间特征，注意力模块用于捕获全局空间特征，双向门控循环单元(Bi-GRU)模块用于从心电数据中提取时间特征。具体来说，注意力机制使我们的深度学习架构能够专注于输入中最重要和最有用的部分，从而做出更准确的预测。在PhysioNet/Computing In Cardiology Challenge 2021中，我们的参赛作品没有在挑战赛的测试数据上得到正式的排名和评分，因为我们的代码在官方阶段没有被成功处理，并且错误地运行失败。
{"title":"Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs","authors":"Long Chen, Zheheng Jiang, T. Almeida, F. Schlindwein, Jakevir S. Shoker, G. Ng, Huiyu Zhou, Xin Li","doi":"10.23919/cinc53138.2021.9662757","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662757","url":null,"abstract":"Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our entry was not officially ranked and scored on the test data of the Challenge, because our code was not successfully processed during the official phase and failed to run with errors.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"48 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":"131329422","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/cinc53138.2021.9662817
M. Rivolta, R. Sassi, L. Mainardi, V. Corino
Aim of this study is to assess, using numerical simulations, the effect of different degrees of ischemia on spatial heterogeneity of ventricular repolarization (SHVR), as evaluated by the V-index. Twelve-lead electrocardiograms were simulated using EGCSIM. Different degrees of ischemia were simulated in three regions, i.e., left anterior descending artery (LAD), right coronary artery (RCA) and left circumflex artery (LCX), by varying the size of the ischemic region (35 mm vs 50 mm), the amplitude of action potentials (APs; maximum reduction of 50%), and by shortening the AP durations (maximum reduction of 35%). The time progression of ischemia was simulated on a time window of 8 minutes in which 30 Monte Carlo simulations of 70 beats were generated each minute. V-index significantly increased at $LCA$ and $RCA$ of 11.2 $pm$ 1.8 ms (+ 35.4%) and $12.6 pm 1.6ms (>+ 39.7%)$ with respect to baseline $(p < 0.05)$, for the ischemic region of 35 mm. The increment was larger for the 50 mm region, in which Vindex approximately doubled. On the other hand, ischemia at LCX resulted in small changes of V-index of about 2 ms for both region sizes $(p < 0.05)$. The study showed that the V-index depended on the ischemic location, its size and electrophysiological changes of APs.
{"title":"Effect of Ischemia on the Spatial Heterogeneity of Ventricular Repolarization: a Simulation Study","authors":"M. Rivolta, R. Sassi, L. Mainardi, V. Corino","doi":"10.23919/cinc53138.2021.9662817","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662817","url":null,"abstract":"Aim of this study is to assess, using numerical simulations, the effect of different degrees of ischemia on spatial heterogeneity of ventricular repolarization (SHVR), as evaluated by the V-index. Twelve-lead electrocardiograms were simulated using EGCSIM. Different degrees of ischemia were simulated in three regions, i.e., left anterior descending artery (LAD), right coronary artery (RCA) and left circumflex artery (LCX), by varying the size of the ischemic region (35 mm vs 50 mm), the amplitude of action potentials (APs; maximum reduction of 50%), and by shortening the AP durations (maximum reduction of 35%). The time progression of ischemia was simulated on a time window of 8 minutes in which 30 Monte Carlo simulations of 70 beats were generated each minute. V-index significantly increased at $LCA$ and $RCA$ of 11.2 $pm$ 1.8 ms (+ 35.4%) and $12.6 pm 1.6ms (>+ 39.7%)$ with respect to baseline $(p < 0.05)$, for the ischemic region of 35 mm. The increment was larger for the 50 mm region, in which Vindex approximately doubled. On the other hand, ischemia at LCX resulted in small changes of V-index of about 2 ms for both region sizes $(p < 0.05)$. The study showed that the V-index depended on the ischemic location, its size and electrophysiological changes of APs.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"83 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":"122287026","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/cinc53138.2021.9662910
Ataollah Tajabadi, Aditi Roy, M. Varela, O. Aslanidi
Atrial fibrillation (AF) is the most common arrhythmia, but its mechanisms are still unclear. Commonly observed phenomena during AF are epicardial re-entrant drivers (rotors) and breakthrough waves. This study aims to elucidate AF mechanisms, including links between rotors and breakthroughs. We used 3D canine atrial models based on micro-CT reconstruction of biatrial geometry combined with region-specific electrophysiology models. Hence, the 3D model included ionic and structural heterogeneities in the entire atria, with special focus on the right atrium (RA) and pectinate muscles (PM). Results were visualized through 3D atrial membrane voltage maps (VM), 2D isochronal maps (IM), and wave maps (WM). AF episodes were initiated in the atria and were maintained by several epicardial rotors in the PV and RA. Transmural rotors were also seen to propagate through the PM and reemerge at the RA epicardium during these episodes. IM and WM revealed multiple breakthroughs at the region where the PM connect to the RA. The VM simulations, as well as electrogram-based IM and WM, showed that the complex AF patterns seen experimentally can be explained by the interactions of epicardial and transmural rotors.
{"title":"Evolution of Epicardial Rotors into Breakthrough Waves During Atrial Fibrillation in 3D Canine Biatrial Model with Detailed Fibre Orientation","authors":"Ataollah Tajabadi, Aditi Roy, M. Varela, O. Aslanidi","doi":"10.23919/cinc53138.2021.9662910","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662910","url":null,"abstract":"Atrial fibrillation (AF) is the most common arrhythmia, but its mechanisms are still unclear. Commonly observed phenomena during AF are epicardial re-entrant drivers (rotors) and breakthrough waves. This study aims to elucidate AF mechanisms, including links between rotors and breakthroughs. We used 3D canine atrial models based on micro-CT reconstruction of biatrial geometry combined with region-specific electrophysiology models. Hence, the 3D model included ionic and structural heterogeneities in the entire atria, with special focus on the right atrium (RA) and pectinate muscles (PM). Results were visualized through 3D atrial membrane voltage maps (VM), 2D isochronal maps (IM), and wave maps (WM). AF episodes were initiated in the atria and were maintained by several epicardial rotors in the PV and RA. Transmural rotors were also seen to propagate through the PM and reemerge at the RA epicardium during these episodes. IM and WM revealed multiple breakthroughs at the region where the PM connect to the RA. The VM simulations, as well as electrogram-based IM and WM, showed that the complex AF patterns seen experimentally can be explained by the interactions of epicardial and transmural rotors.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"38 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":"116322754","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/cinc53138.2021.9662935
B. U. Demirel, Adnan Harun Dogan, M. A. Al Faruque
This paper proposes an efficient convolutional neural network to detect 26 different classes of cardiac activities from different numbers of leads in the Phys-ionetlComputing data in the Cardiology Challenge 2021. The proposed CNN architecture is designed to utilize heart rate variation features from ECG recordings and wave-form morphologies of heartbeats simultaneously. Also, the designed architecture is flexible for the implementation of a different number of leads with a varied length of ECG recordings. The proposed algorithm achieved a score of 0.38 using only 2 channels ofECG on all recordings for the hidden test set of the challenge, placing us 21, 20, 19, 20, 20th (Team name: METU-19) out of 39 teams for 12, 6, 4, 3, and 2-leads respectively. These results show the potential of an efficient, flexible novel neural network for beat-by-beat classification of raw ECG recordings to a complex multi-class multi-label classification problem.
{"title":"Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities Using Deep Learning with Domain-Specific Features","authors":"B. U. Demirel, Adnan Harun Dogan, M. A. Al Faruque","doi":"10.23919/cinc53138.2021.9662935","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662935","url":null,"abstract":"This paper proposes an efficient convolutional neural network to detect 26 different classes of cardiac activities from different numbers of leads in the Phys-ionetlComputing data in the Cardiology Challenge 2021. The proposed CNN architecture is designed to utilize heart rate variation features from ECG recordings and wave-form morphologies of heartbeats simultaneously. Also, the designed architecture is flexible for the implementation of a different number of leads with a varied length of ECG recordings. The proposed algorithm achieved a score of 0.38 using only 2 channels ofECG on all recordings for the hidden test set of the challenge, placing us 21, 20, 19, 20, 20th (Team name: METU-19) out of 39 teams for 12, 6, 4, 3, and 2-leads respectively. These results show the potential of an efficient, flexible novel neural network for beat-by-beat classification of raw ECG recordings to a complex multi-class multi-label classification problem.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"61 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":"127092741","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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