Pub Date : 2019-09-01DOI: 10.23919/CinC49843.2019.9005927
J. Behar, Z. Weiner, P. Warrick
Despite the improvements made in perinatal medicine during the 20th century, stillbirths still occur for 1 in 160 pregnancies in the US which represents a total of 26,000 fetal deaths each year. In addition, approximately 1 in 1000 fetuses experience oxygen deprivation during labor which is severe enough to cause brain injury. It is estimated that half of these cases of birth-related injury are preventable. Incorrect cardiotocography (CTG) interpretation is leading the list of causes.Intrapartum CTG is used routinely to measure maternal uterine pressure and fetal heart rate (FHR). Antepartum CTG monitoring is used to identify fetuses at risk of intrauterine hypoxia and acidaemia. As early as 28 weeks of gestation, analysis of the FHR trace is used as a nonstress test to assess the fetal well-being. In the perinatal period, timely, appropriate intervention can avoid fetal neurological damage or death. The CTG is visually assessed by a clinician or interpreted by computer analysis. In the context of labor monitoring, the CTG is used for continuous fetal monitoring. An abnormal heart rate will lead the clinician to perform a cesarean.With the recent advances in machine learning and statistical signal analysis new algorithms for assessing fetal antepartum or intrapartum health status are being elaborated. These algorithms process signals recorded by CTG monitors or alternative monitoring techniques such as scalp electrocardiography or non-invasive fetal electrocardiography. This session discusses the history of fetal monitoring, its current challenges and the prospects opened by recent algorithmic development.
{"title":"Special Session on Computational Fetal Monitoring","authors":"J. Behar, Z. Weiner, P. Warrick","doi":"10.23919/CinC49843.2019.9005927","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005927","url":null,"abstract":"Despite the improvements made in perinatal medicine during the 20th century, stillbirths still occur for 1 in 160 pregnancies in the US which represents a total of 26,000 fetal deaths each year. In addition, approximately 1 in 1000 fetuses experience oxygen deprivation during labor which is severe enough to cause brain injury. It is estimated that half of these cases of birth-related injury are preventable. Incorrect cardiotocography (CTG) interpretation is leading the list of causes.Intrapartum CTG is used routinely to measure maternal uterine pressure and fetal heart rate (FHR). Antepartum CTG monitoring is used to identify fetuses at risk of intrauterine hypoxia and acidaemia. As early as 28 weeks of gestation, analysis of the FHR trace is used as a nonstress test to assess the fetal well-being. In the perinatal period, timely, appropriate intervention can avoid fetal neurological damage or death. The CTG is visually assessed by a clinician or interpreted by computer analysis. In the context of labor monitoring, the CTG is used for continuous fetal monitoring. An abnormal heart rate will lead the clinician to perform a cesarean.With the recent advances in machine learning and statistical signal analysis new algorithms for assessing fetal antepartum or intrapartum health status are being elaborated. These algorithms process signals recorded by CTG monitors or alternative monitoring techniques such as scalp electrocardiography or non-invasive fetal electrocardiography. This session discusses the history of fetal monitoring, its current challenges and the prospects opened by recent algorithmic development.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"69 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89517217","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005893
L. Zhong, S. Leng, Xiaodan Zhao, J. Tan, R. Tan
The transition of right ventricle (RV) from a compensated to decompensated state contributes to survival in pulmonary arterial hypertension (PAH). However, little is known about the significance of right atrial (RA) dysfunction on disease progression in PAH. In this context, there has been growing interest in markers of RA myocardial dysfunction. Speckle tracking echocardiography, which has been principally used to measure the myocardial strain, is technically challenging in the RA due to the thin atrial wall. Feature tracking cardiovascular magnetic resonance (FT-CMR) software designed to derive myocardial strain from CMR cine images has become available for measurements of atrial longitudinal strain. However, in subjects with relatively vigorous tricuspid annular motion, contour tracking of the RA free wall segment adjacent to the tricuspid valve is adversely affected and becomes the source of errors. In contrast to FT-CMR, we present a rapid assessable strain parameter that requires the automatic tracking of only 3 anatomical reference points – thus avoiding the segment contour tracking near the insertion of the anterior leaflet into the tricuspid annulus.
{"title":"Impaired Right Atrial Strain is Associated with Decompensated Hemodynamics in Pulmonary Arterial Hypertension","authors":"L. Zhong, S. Leng, Xiaodan Zhao, J. Tan, R. Tan","doi":"10.23919/CinC49843.2019.9005893","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005893","url":null,"abstract":"The transition of right ventricle (RV) from a compensated to decompensated state contributes to survival in pulmonary arterial hypertension (PAH). However, little is known about the significance of right atrial (RA) dysfunction on disease progression in PAH. In this context, there has been growing interest in markers of RA myocardial dysfunction. Speckle tracking echocardiography, which has been principally used to measure the myocardial strain, is technically challenging in the RA due to the thin atrial wall. Feature tracking cardiovascular magnetic resonance (FT-CMR) software designed to derive myocardial strain from CMR cine images has become available for measurements of atrial longitudinal strain. However, in subjects with relatively vigorous tricuspid annular motion, contour tracking of the RA free wall segment adjacent to the tricuspid valve is adversely affected and becomes the source of errors. In contrast to FT-CMR, we present a rapid assessable strain parameter that requires the automatic tracking of only 3 anatomical reference points – thus avoiding the segment contour tracking near the insertion of the anterior leaflet into the tricuspid annulus.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"24 1","pages":"Page 1-Page 2"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90197772","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}
Cardiovascular disease is one of the major diseases that threaten human health. Electrocardiogram (ECG) signal is an important indicator for the diagnosis of cardiovascular disease. Accurate analysis of ECG plays a key role in the diagnosis of cardiovascular disease. Underdeveloped areas have always been a high-risk area for cardiovascular disease and there are few doctors for diagnosing cardiovascular disease. One solution is using a telemedicine system for disease diagnosis. However, the quality of the ECG signal collected is not necessarily reliable and may impact diagnosis. In order to solve the problem, we have studied various methods for assessing the quality of ECG signals. In the paper, we analyzed the 12-lead ECG data provided by PhysioNet and selected two features of the time domain: the number of R peaks and the amplitude difference. These two features were extracted from the ECG data to form a matrix of 24 features. We trained the classification model with the feature matrix and achieved a classification accuracy of 95.80% on the test set. Experimental results demonstrated that the proposed Adaboost algorithm had advantages in accuracy compared with other algorithms for solving ECG quality assessment problems.
{"title":"Adaboost Based ECG Signal Quality Evaluation","authors":"Zeyang Zhu, Wenyan Liu, Yang Yao, Xuewei Chen, Yingxian Sun, Lisheng Xu","doi":"10.23919/CinC49843.2019.9005515","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005515","url":null,"abstract":"Cardiovascular disease is one of the major diseases that threaten human health. Electrocardiogram (ECG) signal is an important indicator for the diagnosis of cardiovascular disease. Accurate analysis of ECG plays a key role in the diagnosis of cardiovascular disease. Underdeveloped areas have always been a high-risk area for cardiovascular disease and there are few doctors for diagnosing cardiovascular disease. One solution is using a telemedicine system for disease diagnosis. However, the quality of the ECG signal collected is not necessarily reliable and may impact diagnosis. In order to solve the problem, we have studied various methods for assessing the quality of ECG signals. In the paper, we analyzed the 12-lead ECG data provided by PhysioNet and selected two features of the time domain: the number of R peaks and the amplitude difference. These two features were extracted from the ECG data to form a matrix of 24 features. We trained the classification model with the feature matrix and achieved a classification accuracy of 95.80% on the test set. Experimental results demonstrated that the proposed Adaboost algorithm had advantages in accuracy compared with other algorithms for solving ECG quality assessment problems.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"40 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82262993","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005539
Matteo Zauli, C. Corsi, L. Marchi
Vascular phantoms can be used as in vitro test objects to explore flow behaviour in pathological conditions and novel ways of improving ultrasound diagnosis. This kind of phantom should be anatomically realistic both in terms of geometry, acoustic and physical properties. In particular, enhancing measurements reliability of in vitro models test needs a realistic physiological flow performed by a reliable phantom set-up.This paper describes the design of a programmable flow pump system, designed to be used in an in vitro experimental studies. This system wants to overcome budget problem due mainly to expensive flowmeters. The proposed solution is to use a low cost device, not able to perform a reliable closed loop control, but suitable to obtain an ARX non-linear model of the hydraulic circuit thanks to Matlab tools. By using that model, it is possible to act an open loop control able to produce the targeted waveform with median deviation less than 9% and a similarity index of 0.98.Here, we present also the flow rate calibration steps of the designed flow phantom set-up. In the current work, the flow pump system has been developed using Carotid artery Phantom (CaP), but thanks of its programmability it’s possible to implement different flow profiles suitable for others flow phantoms.
{"title":"Design and Prototype Development of a Low-Cost Blood Flow Simulator for Vascular Phantoms","authors":"Matteo Zauli, C. Corsi, L. Marchi","doi":"10.23919/CinC49843.2019.9005539","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005539","url":null,"abstract":"Vascular phantoms can be used as in vitro test objects to explore flow behaviour in pathological conditions and novel ways of improving ultrasound diagnosis. This kind of phantom should be anatomically realistic both in terms of geometry, acoustic and physical properties. In particular, enhancing measurements reliability of in vitro models test needs a realistic physiological flow performed by a reliable phantom set-up.This paper describes the design of a programmable flow pump system, designed to be used in an in vitro experimental studies. This system wants to overcome budget problem due mainly to expensive flowmeters. The proposed solution is to use a low cost device, not able to perform a reliable closed loop control, but suitable to obtain an ARX non-linear model of the hydraulic circuit thanks to Matlab tools. By using that model, it is possible to act an open loop control able to produce the targeted waveform with median deviation less than 9% and a similarity index of 0.98.Here, we present also the flow rate calibration steps of the designed flow phantom set-up. In the current work, the flow pump system has been developed using Carotid artery Phantom (CaP), but thanks of its programmability it’s possible to implement different flow profiles suitable for others flow phantoms.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"167 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82663926","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005700
Ibrahim Hammoud, I. Ramakrishnan, M. Henry
In this work, we describe our early sepsis prediction model for the PhysioNet/Computing in Cardiology Challenge 2019. We prove that maximizing a general family of utility functions (of which the challenge utility function is a special case) is equivalent to minimizing a weighted 0-1 loss. We then utilize this fact to train an ensemble of gradient boosting decision trees using a weighted binary cross-entropy loss.Our model takes the time-series nature of the data into account by using a fixed size window of all measurements within the last 20 hours as a feature vector. Data were imputed in a way that gives the same information to the model as present to healthcare professionals in real-time. We tune the model hyper-parameters using 5-fold cross-validation. The model performance was measured on each evaluation set using the threshold that gives the maximum utility on the training set. Our best model achieves an official normalized utility score of 0.332 on the final full test set of the challenge (Team name: SBU, rank: 6th/78).
在这项工作中，我们描述了2019年PhysioNet/Computing In Cardiology Challenge的早期败血症预测模型。我们证明了最大化一般效用函数族(其中挑战效用函数是一种特殊情况)等同于最小化加权0-1损失。然后，我们利用这一事实，使用加权二元交叉熵损失来训练梯度增强决策树的集合。我们的模型通过使用过去20小时内所有测量的固定大小窗口作为特征向量来考虑数据的时间序列性质。数据输入的方式可以向模型提供与实时呈现给医疗保健专业人员相同的信息。我们使用5倍交叉验证来调整模型超参数。在每个评估集上，使用在训练集上给出最大效用的阈值来测量模型的性能。我们最好的模型在挑战的最终完整测试集(团队名称:SBU，排名:第6 /78)上实现了0.332的官方标准化效用得分。
{"title":"Early Prediction of Sepsis Using Gradient Boosting Decision Trees with Optimal Sample Weighting","authors":"Ibrahim Hammoud, I. Ramakrishnan, M. Henry","doi":"10.23919/CinC49843.2019.9005700","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005700","url":null,"abstract":"In this work, we describe our early sepsis prediction model for the PhysioNet/Computing in Cardiology Challenge 2019. We prove that maximizing a general family of utility functions (of which the challenge utility function is a special case) is equivalent to minimizing a weighted 0-1 loss. We then utilize this fact to train an ensemble of gradient boosting decision trees using a weighted binary cross-entropy loss.Our model takes the time-series nature of the data into account by using a fixed size window of all measurements within the last 20 hours as a feature vector. Data were imputed in a way that gives the same information to the model as present to healthcare professionals in real-time. We tune the model hyper-parameters using 5-fold cross-validation. The model performance was measured on each evaluation set using the threshold that gives the maximum utility on the training set. Our best model achieves an official normalized utility score of 0.332 on the final full test set of the challenge (Team name: SBU, rank: 6th/78).","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"1 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82912538","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005832
Heqing Zhan, Jingtao Zhang
In heart pathological conditions, fibroblasts proliferate and differentiate into myofibroblasts (Mfbs). This study aimed to investigate the role of Mfbs on the mechanical contraction of cardiac fiber. Mathematical modeling was done using a combination of (1) the Maleckar et al. model of the human atrial myocyte, (2) the MacCannell et al. active model of the human cardiac Mfb, (3) our formulation of INa_myofb based upon experimental findings from Chatelier et al., and (4) the Hill three-element rheological scheme of a single segment of cardiac fiber. For Mfb-myocyte coupling, different ratios of myocytes to Mfbs and gap-junctional conductances were set based on available physiological data. Both isometric contraction and isotonic contraction were considered to illustrate the effect of Mfbs on cardiac fiber’s tension and strain. The results showed that (1) Mfbs decreased APD50 and increased Vrest depolarization, (2) Mfbs regulated myocyte peak force and (3) Mfbs reduced the fiber peak force in isometric contraction and the fiber peak strain in isotonic contraction. The identified effects demonstrated that Mfbs play an important role of modulating cardiac mechanical behavior. It should be considered in future pathological cardiac mathematical modeling, such as atrial fibrillation and cardiac fibrosis.
{"title":"Myofibroblasts Alter Tension and Strain of Cardiac Fiber: A Computational Study","authors":"Heqing Zhan, Jingtao Zhang","doi":"10.23919/CinC49843.2019.9005832","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005832","url":null,"abstract":"In heart pathological conditions, fibroblasts proliferate and differentiate into myofibroblasts (Mfbs). This study aimed to investigate the role of Mfbs on the mechanical contraction of cardiac fiber. Mathematical modeling was done using a combination of (1) the Maleckar et al. model of the human atrial myocyte, (2) the MacCannell et al. active model of the human cardiac Mfb, (3) our formulation of INa_myofb based upon experimental findings from Chatelier et al., and (4) the Hill three-element rheological scheme of a single segment of cardiac fiber. For Mfb-myocyte coupling, different ratios of myocytes to Mfbs and gap-junctional conductances were set based on available physiological data. Both isometric contraction and isotonic contraction were considered to illustrate the effect of Mfbs on cardiac fiber’s tension and strain. The results showed that (1) Mfbs decreased APD50 and increased Vrest depolarization, (2) Mfbs regulated myocyte peak force and (3) Mfbs reduced the fiber peak force in isometric contraction and the fiber peak strain in isotonic contraction. The identified effects demonstrated that Mfbs play an important role of modulating cardiac mechanical behavior. It should be considered in future pathological cardiac mathematical modeling, such as atrial fibrillation and cardiac fibrosis.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"10 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84187916","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005898
Vasileios Athanasiou, Z. Konkoli
A supervised learning technique is used to carefully train memristor models to predict at an early stage whether a patient in intensive care unit (ICU) has the sepsis. A memristor behaves as a resistor, with a (mem)resistance that changes over time within a bounded interval. The resistance value depends on the full history of an applied voltage difference across the element, in the same way as the state of the brain depends on what a person has experienced in the past. The information contained in a voltage difference time series can be encoded in the resistance value. Clinical variables measured subsequently each hour since the patient’s admittance in ICU are transformed into voltage difference signals with transformation functions. The training procedure involves the optimization of the transformation functions. The decision of whether to predict sepsis or not is taken by reading the value of the resistance. The authors have participated in the Physionet 2019 challenge with the name called "the memristive agents" and their best submission resulted to a utility score 0.20 on a hidden test data-set.
{"title":"Memristor Models for Early Detection of Sepsis in ICU Patients","authors":"Vasileios Athanasiou, Z. Konkoli","doi":"10.23919/CinC49843.2019.9005898","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005898","url":null,"abstract":"A supervised learning technique is used to carefully train memristor models to predict at an early stage whether a patient in intensive care unit (ICU) has the sepsis. A memristor behaves as a resistor, with a (mem)resistance that changes over time within a bounded interval. The resistance value depends on the full history of an applied voltage difference across the element, in the same way as the state of the brain depends on what a person has experienced in the past. The information contained in a voltage difference time series can be encoded in the resistance value. Clinical variables measured subsequently each hour since the patient’s admittance in ICU are transformed into voltage difference signals with transformation functions. The training procedure involves the optimization of the transformation functions. The decision of whether to predict sepsis or not is taken by reading the value of the resistance. The authors have participated in the Physionet 2019 challenge with the name called \"the memristive agents\" and their best submission resulted to a utility score 0.20 on a hidden test data-set.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"33 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83697676","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}
Aims: Previous studies have shown that the infarction and ischemia of cardiac tissue are strongly correlated with incidence of atrial and ventricular tachyarrhythmias. However, so far the combined effect of myocardial infarction and ischemia on the genesis of cardiac arrhythmias has not been fully understood. Therefore, this study aimed to investigate how the coexistence of myocardial infarction and ischemia alters excitation wave propagation.Methods: The electrophysiology remodeling under ischemia condition was mimicked based on experimental data and incorporated into TP06 model. Using the constructed 2D and 3D models, we simulated the excitation wave conduction in ventricular tissue under five different conditions: normal, myocardial ischemia under three levels, and myocardial infarction conditions.Results: Simulation results showed that the conduction velocity and rotor tracks are different in the normal, infarcted and ischemic conditions. In addition, reentry waves are observed in myocardial infarction with the ischemic condition in 2D and 3D models.Conclusion: Simulation results demonstrate that the coaction of myocardial infarction and ischemia areas increases spatial electrical heterogeneity of ventricular tissue, which may enhance the pro-arrhythmogenic effect.
{"title":"The Combined Effect of Myocardial Infarction and Ischemia on Excitation Wave Propagation in Ventricular Tissue","authors":"Cuiping Liang, Kuanquan Wang, Qince Li, Henggui Zhang","doi":"10.23919/CinC49843.2019.9005915","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005915","url":null,"abstract":"Aims: Previous studies have shown that the infarction and ischemia of cardiac tissue are strongly correlated with incidence of atrial and ventricular tachyarrhythmias. However, so far the combined effect of myocardial infarction and ischemia on the genesis of cardiac arrhythmias has not been fully understood. Therefore, this study aimed to investigate how the coexistence of myocardial infarction and ischemia alters excitation wave propagation.Methods: The electrophysiology remodeling under ischemia condition was mimicked based on experimental data and incorporated into TP06 model. Using the constructed 2D and 3D models, we simulated the excitation wave conduction in ventricular tissue under five different conditions: normal, myocardial ischemia under three levels, and myocardial infarction conditions.Results: Simulation results showed that the conduction velocity and rotor tracks are different in the normal, infarcted and ischemic conditions. In addition, reentry waves are observed in myocardial infarction with the ischemic condition in 2D and 3D models.Conclusion: Simulation results demonstrate that the coaction of myocardial infarction and ischemia areas increases spatial electrical heterogeneity of ventricular tissue, which may enhance the pro-arrhythmogenic effect.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"32 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85113914","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005797
Saumitra Mishra, Sreehari Rammohan, K. Rajab, G. Dhillon, P. Lambiase, R. Hunter, E. Chew
We use the Filter Diagonalization Method (FDM), a harmonic inversion technique, to extract f-wave features in electrocardiographic (ECG) traces for atrial fibrillation (AF) stratification. The FDM detects f-wave frequencies and amplitudes at frame sizes of 0.15 seconds. We demonstrate our method on a dataset comprising of ECG recordings from 23 patients (61.65 ± 11.63 years, 78.26% male) before cryoablation; 2 paroxysmal AF, 16 early persistent AF (<12 months duration), and 4 longstanding persistent AF (>12 months duration). Moreover, some of these patients received adenosine to enhance their RR intervals before ablation. Our method extracts features from FDM outputs to train statistical machine learning classifiers. Tenfold cross-validation demonstrates that the Random Forest and Decision Tree models performed best for the pre-ablation without and with adenosine datasets, with accuracy 60.89 ± 0.31% and 59.58% ± 0.04%, respectively. While the results are modest, they demonstrate that f-wave features can be used for AF stratification. The accuracies are similar for the two tests, slightly better for the case without adenosine, showing that the FDM can successfully model short f-waves without the need to concatenate f-wave sequences or adenosine to elongate RR intervals.
{"title":"Atrial Fibrillation Stratification Via Super-Resolution Analysis of Fibrillatory Waves","authors":"Saumitra Mishra, Sreehari Rammohan, K. Rajab, G. Dhillon, P. Lambiase, R. Hunter, E. Chew","doi":"10.23919/CinC49843.2019.9005797","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005797","url":null,"abstract":"We use the Filter Diagonalization Method (FDM), a harmonic inversion technique, to extract f-wave features in electrocardiographic (ECG) traces for atrial fibrillation (AF) stratification. The FDM detects f-wave frequencies and amplitudes at frame sizes of 0.15 seconds. We demonstrate our method on a dataset comprising of ECG recordings from 23 patients (61.65 ± 11.63 years, 78.26% male) before cryoablation; 2 paroxysmal AF, 16 early persistent AF (<12 months duration), and 4 longstanding persistent AF (>12 months duration). Moreover, some of these patients received adenosine to enhance their RR intervals before ablation. Our method extracts features from FDM outputs to train statistical machine learning classifiers. Tenfold cross-validation demonstrates that the Random Forest and Decision Tree models performed best for the pre-ablation without and with adenosine datasets, with accuracy 60.89 ± 0.31% and 59.58% ± 0.04%, respectively. While the results are modest, they demonstrate that f-wave features can be used for AF stratification. The accuracies are similar for the two tests, slightly better for the case without adenosine, showing that the FDM can successfully model short f-waves without the need to concatenate f-wave sequences or adenosine to elongate RR intervals.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"5 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89214555","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005625
V. Sholohov, V. Zverev, A. Kursanov
We developed three-dimensional model of isolated myocardial muscular preparation that takes into account the coupling of excitation with contraction in the myocardium at the cellular and tissue levels. This model describes myocardium sample using approaches and methods developed in continuum mechanics. In the model, electromechanical interactions and mechano-electric feedbacks are realized both at the micro level and at the macro level. We used non-linear partial differential equations describing the deformation of the cardiac tissue, and a detailed "Ekaterinburg-Oxford" (EO) cellular model of the electrical and mechanical activity of cardiomyocytes. Electrical and mechanical interactions between the cells in tissue, as well as intracellular mechano-electric feedback beat-to-beat affect the functional characteristics of coupled cardiomyocytes further, adjusting their electrical and mechanical heterogeneity to the activation timing. Model analysis suggests that cooperative mechanisms of myofilament calcium activation contribute essentially to the generation of cellular functional heterogeneity in contracting cardiac tissue.
{"title":"Investigation of Mechanisms of Regulation of Electromechanical Function of Cardiomyocytes in the Biomechanical Model of Myocardium","authors":"V. Sholohov, V. Zverev, A. Kursanov","doi":"10.23919/CinC49843.2019.9005625","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005625","url":null,"abstract":"We developed three-dimensional model of isolated myocardial muscular preparation that takes into account the coupling of excitation with contraction in the myocardium at the cellular and tissue levels. This model describes myocardium sample using approaches and methods developed in continuum mechanics. In the model, electromechanical interactions and mechano-electric feedbacks are realized both at the micro level and at the macro level. We used non-linear partial differential equations describing the deformation of the cardiac tissue, and a detailed \"Ekaterinburg-Oxford\" (EO) cellular model of the electrical and mechanical activity of cardiomyocytes. Electrical and mechanical interactions between the cells in tissue, as well as intracellular mechano-electric feedback beat-to-beat affect the functional characteristics of coupled cardiomyocytes further, adjusting their electrical and mechanical heterogeneity to the activation timing. Model analysis suggests that cooperative mechanisms of myofilament calcium activation contribute essentially to the generation of cellular functional heterogeneity in contracting cardiac tissue.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"6 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80365611","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：