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/CinC49843.2019.9005927
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/CinC49843.2019.9005832
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/CinC49843.2019.9005875
S. Palacios, E. Caiani, E. Pueyo, J. P. Martínez
Head-Down Bed Rest (HDBR) microgravity simulation induces cardiovascular deconditioning, including effects on ventricular repolarization. The index of Periodic Repolarization Dynamics (PRD) was developed to quantify low-frequency oscillations of cardiac repolarization. In this study, PRD was quantified by Phase Rectified Signal Averaging (PRDPRSA) and Continuous Wavelet Transform (PRDCWT) methods. PRD was analyzed in ECGs from 22 volunteers at rest and during orthostatic Tilt-Table Test (TTT) performed before and after -6° 60-days HDBR. Significant correlation was found between PRD measured by PRSA and CWT (Pearson’s ρ = 0.93, p < 10-54 and Kendall’s τ = 0.79 p < 10-38). A highly significant increase was found when PRDPRSA values were measured at POST-HDBR with respect to PRE-HDBR in the tilt phase: 1.40 [1.10] deg and 0.97 [0.90] deg (median [IQR]), p = 0.008, respectively. PRDPRSA also increased significantly in the tilt phase with respect to baseline, both at POST-HDBR (0.90 [0.57] deg, p = 0.003) and at PRE-HDBR (0.75 [0.45] deg, p = 0.011). PRD, either measured with PRSA or with CWT, is able to measure changes in ventricular repolarization induced by microgravity simulation as well as following sympathetic provocation.
头下床休息(HDBR)微重力模拟诱导心血管降功能，包括对心室复极的影响。建立了周期复极化动力学指标(PRD)来量化心脏复极化的低频振荡。在本研究中，PRD通过相位校正信号平均(PRDPRSA)和连续小波变换(PRDCWT)方法进行量化。研究人员分析了22名志愿者在-6°60天HDBR前后休息时和直立倾斜台试验(TTT)期间的心电图中的PRD。PRSA测量的PRD与CWT之间存在显著相关(Pearson’s ρ = 0.93, p < 10-54, Kendall’s τ = 0.79 p < 10-38)。相对于倾斜阶段的PRE-HDBR，在hdbr后测量PRDPRSA值时发现了非常显著的增加:分别为1.40[1.10]度和0.97[0.90]度(中位数[IQR])， p = 0.008。相对于基线，在hdbr后(0.90[0.57]度，p = 0.003)和hdbr前(0.75[0.45]度，p = 0.011)， PRDPRSA在倾斜期也显著增加。PRD，无论是用PRSA还是CWT测量，都能够测量微重力模拟和交感刺激引起的心室复极的变化。
{"title":"Response of Ventricular Repolarization to Simulated Microgravity Measured by Periodic Repolarization Dynamics Using Phase-Rectified Signal Averaging","authors":"S. Palacios, E. Caiani, E. Pueyo, J. P. Martínez","doi":"10.23919/CinC49843.2019.9005875","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005875","url":null,"abstract":"Head-Down Bed Rest (HDBR) microgravity simulation induces cardiovascular deconditioning, including effects on ventricular repolarization. The index of Periodic Repolarization Dynamics (PRD) was developed to quantify low-frequency oscillations of cardiac repolarization. In this study, PRD was quantified by Phase Rectified Signal Averaging (PRDPRSA) and Continuous Wavelet Transform (PRDCWT) methods. PRD was analyzed in ECGs from 22 volunteers at rest and during orthostatic Tilt-Table Test (TTT) performed before and after -6° 60-days HDBR. Significant correlation was found between PRD measured by PRSA and CWT (Pearson’s ρ = 0.93, p < 10-54 and Kendall’s τ = 0.79 p < 10-38). A highly significant increase was found when PRDPRSA values were measured at POST-HDBR with respect to PRE-HDBR in the tilt phase: 1.40 [1.10] deg and 0.97 [0.90] deg (median [IQR]), p = 0.008, respectively. PRDPRSA also increased significantly in the tilt phase with respect to baseline, both at POST-HDBR (0.90 [0.57] deg, p = 0.003) and at PRE-HDBR (0.75 [0.45] deg, p = 0.011). PRD, either measured with PRSA or with CWT, is able to measure changes in ventricular repolarization induced by microgravity simulation as well as following sympathetic provocation.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"45 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":"87323504","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/CinC49843.2019.9005719
MariNieves Pardo-Rodrı́guez, E. Bojorges-Valdez, O. Yáñez-Suárez
This study aimed to find whether there is a causal relationship between band power time series (BPts) extracted from EEG and heart rate variability (HRV). Such relationships were explored during spontaneous and a controlled breathing tasks. Data analyzed were recordings obtained from 14 healthy subjects using one ECG lead and 21 EEG channels. The RR intervals from the ECG were used to obtain the HRV signal, which was decomposed with Empirical Mode Decomposition into components of different spectral content known as intrinsic mode functions (IMFs). Granger causality tests were run for the BPts of alpha, beta and gamma frequency ranges of the EEG signal and the HRV signals IMFs. G-causality increased for three different conditions: slower IMFs (IMF4), BPts of higher frequency (gamma) band and during task realization. Meaning, gamma’s BPts G-caused HRV for a larger number of subjects and channels. Also there was a larger incidence on the number of channels that G-caused HRV during the controlled breathing task. The causal influence from the BPts of EEG signals to the HRV IMFs suggests there is an indirect or unobserved interaction between instantaneous changes on EEG band power and components of HRV which may explain changes in its dynamics.
{"title":"Causal Relationship Analysis of Heart Rate Variability and Band Power Time Series of Electroencephalographic Signals","authors":"MariNieves Pardo-Rodrı́guez, E. Bojorges-Valdez, O. Yáñez-Suárez","doi":"10.23919/CinC49843.2019.9005719","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005719","url":null,"abstract":"This study aimed to find whether there is a causal relationship between band power time series (BPts) extracted from EEG and heart rate variability (HRV). Such relationships were explored during spontaneous and a controlled breathing tasks. Data analyzed were recordings obtained from 14 healthy subjects using one ECG lead and 21 EEG channels. The RR intervals from the ECG were used to obtain the HRV signal, which was decomposed with Empirical Mode Decomposition into components of different spectral content known as intrinsic mode functions (IMFs). Granger causality tests were run for the BPts of alpha, beta and gamma frequency ranges of the EEG signal and the HRV signals IMFs. G-causality increased for three different conditions: slower IMFs (IMF4), BPts of higher frequency (gamma) band and during task realization. Meaning, gamma’s BPts G-caused HRV for a larger number of subjects and channels. Also there was a larger incidence on the number of channels that G-caused HRV during the controlled breathing task. The causal influence from the BPts of EEG signals to the HRV IMFs suggests there is an indirect or unobserved interaction between instantaneous changes on EEG band power and components of HRV which may explain changes in its dynamics.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"16 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":"80466413","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/CinC49843.2019.9005743
T. Dietenbeck, Sophia Houriez--Gombaud-Saintonge, U. Gencer, A. Giron, G. Soulat, É. Mousseaux, P. Cluzel, A. Redheuil, N. Kachenoura
Aim: The diagnosis of thoracic aortic aneurysm is based on local aortic deformation associated to excessive aortic diameter (D). Maximal local aortic diameter was shown to be below the recommended surgical threshold in 30% of patients who ultimately developed aortic dissection. Aortic volumes integrate both dilation and elongation and may be more sensitive to changes in aortic geometry and less dependent on slice orientation and obliquity than diameter measurements. Methods: We studied 278 asymptomatic individuals with 3D aortic MRI: 119 healthy volunteers (hC), 53 hypertensive patients (HT) and 106 patients with dilated ascending aorta of which 62 with tricuspid (APt) and 44 with bicuspid (APb) aortic valve. Automated 3D aortic segmentation was performed and aortic lengths, maximal diameters and volumes were measured from sino-tubular junction to the brachiocephalic trunk for the ascending aorta (AAo) and from the left subclavian artery to the diaphragm for the descending aorta (DAo). Results: While AAo D increased by 40% between APt and HC, AAo volume increased by 170%. Moreover, when comparing HT patients with controls, AAo volume difference was significant (p < 0.05) even after adjustment to BSA while AAo D was not. Conclusion: Aortic volumes measured automatically from 3D MRI were able to characterize subclinical and pathological dilation more accurately than maximal diameters.
{"title":"Automated 3D MRI Aortic Morphometry Demonstrates the Added Value of Volumes as Compared to Diameters","authors":"T. Dietenbeck, Sophia Houriez--Gombaud-Saintonge, U. Gencer, A. Giron, G. Soulat, É. Mousseaux, P. Cluzel, A. Redheuil, N. Kachenoura","doi":"10.23919/CinC49843.2019.9005743","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005743","url":null,"abstract":"Aim: The diagnosis of thoracic aortic aneurysm is based on local aortic deformation associated to excessive aortic diameter (D). Maximal local aortic diameter was shown to be below the recommended surgical threshold in 30% of patients who ultimately developed aortic dissection. Aortic volumes integrate both dilation and elongation and may be more sensitive to changes in aortic geometry and less dependent on slice orientation and obliquity than diameter measurements. Methods: We studied 278 asymptomatic individuals with 3D aortic MRI: 119 healthy volunteers (hC), 53 hypertensive patients (HT) and 106 patients with dilated ascending aorta of which 62 with tricuspid (APt) and 44 with bicuspid (APb) aortic valve. Automated 3D aortic segmentation was performed and aortic lengths, maximal diameters and volumes were measured from sino-tubular junction to the brachiocephalic trunk for the ascending aorta (AAo) and from the left subclavian artery to the diaphragm for the descending aorta (DAo). Results: While AAo D increased by 40% between APt and HC, AAo volume increased by 170%. Moreover, when comparing HT patients with controls, AAo volume difference was significant (p < 0.05) even after adjustment to BSA while AAo D was not. Conclusion: Aortic volumes measured automatically from 3D MRI were able to characterize subclinical and pathological dilation more accurately than maximal diameters.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"331 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":"80506238","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/CinC49843.2019.9005878
Shailesh Nirgudkar, Tianyu Ding
This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensem- bler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. On a hidden test data set maintained by the organizers, the model obtained a utility score of 0.192. The utility score as defined by the organizers takes into account true positives, negatives and false alarms. Our team was Team Tesseract and our overall ranking was 49 out of 79 officially ranked entries.
{"title":"Early Detection of Sepsis Using Ensemblers","authors":"Shailesh Nirgudkar, Tianyu Ding","doi":"10.23919/CinC49843.2019.9005878","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005878","url":null,"abstract":"This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensem- bler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. On a hidden test data set maintained by the organizers, the model obtained a utility score of 0.192. The utility score as defined by the organizers takes into account true positives, negatives and false alarms. Our team was Team Tesseract and our overall ranking was 49 out of 79 officially ranked entries.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"38 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":"80744531","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.
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