Pub Date : 2019-09-01DOI: 10.23919/CinC49843.2019.9005782
R. Firoozabadi, R. Gregg, S. Babaeizadeh
A number of cardiac conditions such as acute pericarditis (PC) and early repolarization (ER) cause ST elevation which mimics ST-segment Elevation Myocardial Infarction (STEMI). Current guidelines recommend analyzing ST segment morphology to distinguish STEMI from these confounders. ST elevation in PC and ER (and possibly in STEMI) is concave (upward) in the JTpeak interval, while a convex or straight ECG ST segment is associated with the diagnosis of STEMI. We developed an algorithm to classify concavity characteristic of the ST segment. A quadratic polynomial regression algorithm was introduced to model the shape of JTpeak interval. Our diagnostic algorithm generated representative beats and measured the fiducial points and standard measurements such as ST level in 12-lead 10-sec segments of ECG recordings. JTpeak interval was modeled by a parabola using a least-squares polynomial regression algorithm. Classifier features such as curvature, parabola direction and vertex, model fit error, and the noise measure were determined. A bootstrap-aggregated tree ensemble classifier determined the ST segment shape. Our algorithm was evaluated on a 12-lead ECG database collected in two medical centers. Our ST segment polynomial regression model exhibited significant improvement in concavity detection versus a simple conventional method.
{"title":"Modeling and Classification of the ST Segment Morphology for Enhanced Detection of Acute Myocardial Infarction","authors":"R. Firoozabadi, R. Gregg, S. Babaeizadeh","doi":"10.23919/CinC49843.2019.9005782","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005782","url":null,"abstract":"A number of cardiac conditions such as acute pericarditis (PC) and early repolarization (ER) cause ST elevation which mimics ST-segment Elevation Myocardial Infarction (STEMI). Current guidelines recommend analyzing ST segment morphology to distinguish STEMI from these confounders. ST elevation in PC and ER (and possibly in STEMI) is concave (upward) in the JTpeak interval, while a convex or straight ECG ST segment is associated with the diagnosis of STEMI. We developed an algorithm to classify concavity characteristic of the ST segment. A quadratic polynomial regression algorithm was introduced to model the shape of JTpeak interval. Our diagnostic algorithm generated representative beats and measured the fiducial points and standard measurements such as ST level in 12-lead 10-sec segments of ECG recordings. JTpeak interval was modeled by a parabola using a least-squares polynomial regression algorithm. Classifier features such as curvature, parabola direction and vertex, model fit error, and the noise measure were determined. A bootstrap-aggregated tree ensemble classifier determined the ST segment shape. Our algorithm was evaluated on a 12-lead ECG database collected in two medical centers. Our ST segment polynomial regression model exhibited significant improvement in concavity detection versus a simple conventional method.","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":"86007718","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/CinC49843.2019.9005785
Ana González-Ascaso, P. Olcina, Mireia Garcia-Daras, J. F. R. Matas, J. M. Ferrero
Hyperkalemia, acidosis and hypoxia are the three main components of acute myocardial ischemia. In particular, the increase of extracellular K+ concentration (hyperkalemia), has been proved to be very proarrhythmic because it sets the stage for ventricular fibrillation. However, the intimate mechanisms remain partially unknown. The aim of this work was to investigate, using computational simulation, the relationship between the different phases of hiperkalemia, the activity of the ion channels and the changes related to the action potential in the absence of coronary flow. Our results show that the partial inhibition of the sodium-potassium pump is the main cause of extracellular potassium accumulation. However, the cause of the plateau phase could be due to the appearance of action potential alternans, which reduces the net potassium efflux and limits the increase of extracellular potassium concentration.
{"title":"Why Does Extracellular Potassium Rise in Acute Ischemia? Insights from Computational Simulations","authors":"Ana González-Ascaso, P. Olcina, Mireia Garcia-Daras, J. F. R. Matas, J. M. Ferrero","doi":"10.23919/CinC49843.2019.9005785","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005785","url":null,"abstract":"Hyperkalemia, acidosis and hypoxia are the three main components of acute myocardial ischemia. In particular, the increase of extracellular K+ concentration (hyperkalemia), has been proved to be very proarrhythmic because it sets the stage for ventricular fibrillation. However, the intimate mechanisms remain partially unknown. The aim of this work was to investigate, using computational simulation, the relationship between the different phases of hiperkalemia, the activity of the ion channels and the changes related to the action potential in the absence of coronary flow. Our results show that the partial inhibition of the sodium-potassium pump is the main cause of extracellular potassium accumulation. However, the cause of the plateau phase could be due to the appearance of action potential alternans, which reduces the net potassium efflux and limits the increase of extracellular potassium concentration.","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":"76855160","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/CinC49843.2019.9005512
Pauline Migerditichan, M. Potse, N. Zemzemi
The method of fundamental solutions (MFS) has been extensively used for the electrocardiographic imaging (ECGI) inverse problem. One of its advantages is that it is a meshless method. We remarked that the using cm instead of mm as a space unit has a high impact on the reconstructed inverse solution. Our purpose is to refine this observation, by introducing a rescaling coefficient in space and study its effect on the MFS inverse solution. Results are provided using simulated test data prepared using a reaction-diffusion model. We then computed the ECGI inverse solution for rescaling coefficient values varying from 1 to 100, and computed the relative error (RE) and correlation coefficient (CC). This approach improved the RE and CC by at least 10 % but can go up to 40 % independently of the pacing site. We concluded that the optimal coefficient depends on the heterogeneity and anisotropy of the torso and does not depend on the stimulation site. This suggests that it is related to an optimal equivalent conductivity estimation in the torso domain.
{"title":"Space Rescaling in the Method of Fundamental Solution Improves the ECGI Reconstruction","authors":"Pauline Migerditichan, M. Potse, N. Zemzemi","doi":"10.23919/CinC49843.2019.9005512","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005512","url":null,"abstract":"The method of fundamental solutions (MFS) has been extensively used for the electrocardiographic imaging (ECGI) inverse problem. One of its advantages is that it is a meshless method. We remarked that the using cm instead of mm as a space unit has a high impact on the reconstructed inverse solution. Our purpose is to refine this observation, by introducing a rescaling coefficient in space and study its effect on the MFS inverse solution. Results are provided using simulated test data prepared using a reaction-diffusion model. We then computed the ECGI inverse solution for rescaling coefficient values varying from 1 to 100, and computed the relative error (RE) and correlation coefficient (CC). This approach improved the RE and CC by at least 10 % but can go up to 40 % independently of the pacing site. We concluded that the optimal coefficient depends on the heterogeneity and anisotropy of the torso and does not depend on the stimulation site. This suggests that it is related to an optimal equivalent conductivity estimation in the torso domain.","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":"85894956","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/CinC49843.2019.9005804
G. García-Isla, V. Corino, L. Mainardi
Tachyarrhythmia detection through RR interval analysis could improve performance of monitoring devices. In this paper a Poincaré plot-based image approach is presented. Three cardiac rhythms were analyzed in this study: normal sinus rhythm (NSR), atrial fibrillation (AF) and atrial bigeminy (AB). Using different MIT-BIH databases, 27955, 3363 and 76 images were generated for NSR, AF and AB respectively using a 2-minute window with 50 % overlap. The 80 % of the data available for each rhythm was used to create a reference rhythm image atlas. The remaining 20 % was classified into one of the three categories using mutual information. The process was iterated 10 times, in which images used to construct the atlas and used to create the test set were randomly selected. AF was correctly classified 94.12 % ± 0.45, AB 72.00 % ± 11.24 and NSR 80.70 %±0.54. The results of the present study suggest that Poincaré plot-based image analysis is a promising path for classifying different rhythms using only ventricular activity.
{"title":"Cardiac Tachyarrhythmia Detection by Poincaré Plot-Based Image Analysis","authors":"G. García-Isla, V. Corino, L. Mainardi","doi":"10.23919/CinC49843.2019.9005804","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005804","url":null,"abstract":"Tachyarrhythmia detection through RR interval analysis could improve performance of monitoring devices. In this paper a Poincaré plot-based image approach is presented. Three cardiac rhythms were analyzed in this study: normal sinus rhythm (NSR), atrial fibrillation (AF) and atrial bigeminy (AB). Using different MIT-BIH databases, 27955, 3363 and 76 images were generated for NSR, AF and AB respectively using a 2-minute window with 50 % overlap. The 80 % of the data available for each rhythm was used to create a reference rhythm image atlas. The remaining 20 % was classified into one of the three categories using mutual information. The process was iterated 10 times, in which images used to construct the atlas and used to create the test set were randomly selected. AF was correctly classified 94.12 % ± 0.45, AB 72.00 % ± 11.24 and NSR 80.70 %±0.54. The results of the present study suggest that Poincaré plot-based image analysis is a promising path for classifying different rhythms using only ventricular activity.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"116 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":"77972312","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/CinC49843.2019.9005451
S. Sajitha, Minimol Balakrishnan, M. G. Mini
Heart Rate Variability (HRV) is the subtle beat to beat changes in heart rate. Autonomic Nervous System (ANS) regulates heart rate by controlling the neurotransmitters, mainly Norepinephrine (NE) and Acetyl choline (Ach) from sympathetic and parasympathetic branches respectively. HRV analysis is a noninvasive tool for assessing the integrity of ANS. HRV changes are observed in the onset of heart disease and in a number of disease conditions like sleep apnea, psychiatric disorders, diabetes, hypertension etc. An understanding of the relationship between kinetics at sympathetic and parasympathetic sites and HRV helps to identify biological changes associated with various autonomic imbalance conditions and hence help in targeted diagnosis and therapy. A computational model of ANS for heart rate regulation is proposed in this study. Fitzhugh Nagumo (FHN) model is used as the successive stage of proposed model to generate a discrete time heart beat interval series. HRV data from a group of healthy individuals having balanced sympathetic and parasympathetic activities were studied. The results were in agreement with parameters derived from model synthesized data for the same autonomic state.
{"title":"A Computational Model of Autonomic Nervous System for Heart Rate Variability","authors":"S. Sajitha, Minimol Balakrishnan, M. G. Mini","doi":"10.23919/CinC49843.2019.9005451","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005451","url":null,"abstract":"Heart Rate Variability (HRV) is the subtle beat to beat changes in heart rate. Autonomic Nervous System (ANS) regulates heart rate by controlling the neurotransmitters, mainly Norepinephrine (NE) and Acetyl choline (Ach) from sympathetic and parasympathetic branches respectively. HRV analysis is a noninvasive tool for assessing the integrity of ANS. HRV changes are observed in the onset of heart disease and in a number of disease conditions like sleep apnea, psychiatric disorders, diabetes, hypertension etc. An understanding of the relationship between kinetics at sympathetic and parasympathetic sites and HRV helps to identify biological changes associated with various autonomic imbalance conditions and hence help in targeted diagnosis and therapy. A computational model of ANS for heart rate regulation is proposed in this study. Fitzhugh Nagumo (FHN) model is used as the successive stage of proposed model to generate a discrete time heart beat interval series. HRV data from a group of healthy individuals having balanced sympathetic and parasympathetic activities were studied. The results were in agreement with parameters derived from model synthesized data for the same autonomic state.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"7 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":"73403111","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/CinC49843.2019.9005809
Janmajay Singh, Kentaro Oshiro, R. Krishnan, Masahiro Sato, T. Ohkuma, N. Kato
Aims: Physicians have to routinely make crucial decisions about patients’ health in the ICU. Sepsis affects about 35% of ICU patients, killing approximately 25% of the afflicted. In this paper, we aim to predict the occurrence of sepsis early by studying the missingness of physiological variables and using it with the overall trends in data.Methods: We chose XGBoost as our base model and tried several variations by changing hyperparameters, window sizes and imputation methods. To further improve the model, we used masking vectors to represent the missingness of features in the dataset. Additional modifications include shifting the Sepsis Label to earlier time steps and tuning the classification probability threshold to further improve the model’s performance.Results: The XGBoost model with a sliding window of size 5, no imputation, utilizing informative missingness of all temporal variables and trained on labels shifted by 3 hours before toptimal, achieved a Utility Score of 0.337 on the full test set. We identified as "CTL-Team" in the challenge and were officially ranked 5th on the basis of this score.
{"title":"Utilizing Informative Missingness for Early Prediction of Sepsis","authors":"Janmajay Singh, Kentaro Oshiro, R. Krishnan, Masahiro Sato, T. Ohkuma, N. Kato","doi":"10.23919/CinC49843.2019.9005809","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005809","url":null,"abstract":"Aims: Physicians have to routinely make crucial decisions about patients’ health in the ICU. Sepsis affects about 35% of ICU patients, killing approximately 25% of the afflicted. In this paper, we aim to predict the occurrence of sepsis early by studying the missingness of physiological variables and using it with the overall trends in data.Methods: We chose XGBoost as our base model and tried several variations by changing hyperparameters, window sizes and imputation methods. To further improve the model, we used masking vectors to represent the missingness of features in the dataset. Additional modifications include shifting the Sepsis Label to earlier time steps and tuning the classification probability threshold to further improve the model’s performance.Results: The XGBoost model with a sliding window of size 5, no imputation, utilizing informative missingness of all temporal variables and trained on labels shifted by 3 hours before toptimal, achieved a Utility Score of 0.337 on the full test set. We identified as \"CTL-Team\" in the challenge and were officially ranked 5th on the basis of this score.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"43 1 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":"77060670","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/CinC49843.2019.9005647
Yingjing Feng, Mirabeau Saha, M. Hocini, E. Vigmond
Almost 40% of paroxysmal atrial fibrillation (AF) patients experience arrhythmia recurrence within a year after initial ablation success. The rich spatiotemporal information provided by body surface potential maps (BSPMs) can reveal AF dynamics. We hypothesised that the dipole direction of the heart during AF can be traced by the centroid trajectory of the principal "activated" electrode patch from the BSPM, where an electrode is defined as "activated" when its signal exhibits a local peak. This hypothesis was first verified using simulated and patient data, indicating that the trajectory has a high correlation with atrial electrical activity. The trajectory was then used as a spatiotemporal feature to predict one-year AF recurrence (22 negative and 23 positive) after ablation among 45 paroxysmal AF patients. The trajectories were segmented according to AF cycles for prediction in a multiple instance classification framework, using a Gaussian mixture regression (GMR) and a linear support vector machine (SVM) with L1 penalty for classification. A leave-one-out test showed 0.73 accuracy, 0.70 sensitivity and 0.77 specificity, and the area under the curve (AUC) of the receiver operating characteristic (ROC) as 0.84. The work suggests that with the proposed trajectory extracted from the BSPM, the prediction for paroxysmal AF ablation follow-up could be improved.
{"title":"Noninvasive One-Year Ablation Outcome Prediction for Paroxysmal Atrial Fibrillation Using Trajectories of Activation From Body Surface Potential Maps","authors":"Yingjing Feng, Mirabeau Saha, M. Hocini, E. Vigmond","doi":"10.23919/CinC49843.2019.9005647","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005647","url":null,"abstract":"Almost 40% of paroxysmal atrial fibrillation (AF) patients experience arrhythmia recurrence within a year after initial ablation success. The rich spatiotemporal information provided by body surface potential maps (BSPMs) can reveal AF dynamics. We hypothesised that the dipole direction of the heart during AF can be traced by the centroid trajectory of the principal \"activated\" electrode patch from the BSPM, where an electrode is defined as \"activated\" when its signal exhibits a local peak. This hypothesis was first verified using simulated and patient data, indicating that the trajectory has a high correlation with atrial electrical activity. The trajectory was then used as a spatiotemporal feature to predict one-year AF recurrence (22 negative and 23 positive) after ablation among 45 paroxysmal AF patients. The trajectories were segmented according to AF cycles for prediction in a multiple instance classification framework, using a Gaussian mixture regression (GMR) and a linear support vector machine (SVM) with L1 penalty for classification. A leave-one-out test showed 0.73 accuracy, 0.70 sensitivity and 0.77 specificity, and the area under the curve (AUC) of the receiver operating characteristic (ROC) as 0.84. The work suggests that with the proposed trajectory extracted from the BSPM, the prediction for paroxysmal AF ablation follow-up could be improved.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"29 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":"77124617","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/CinC49843.2019.9005643
Matti Molkkari, M. Tenhunen, A. Tarniceriu, A. Vehkaoja, S. Himanen, Esa Räsänen
We assess the feasibility of heart rate variability (HRV) estimated from interbeat interval (IBI) data measured with wrist-worn photoplethysmography device for sleep stage classification. In particular, we examine fractal correlations in the IBIs as the function of both time and scale.Optical heart rate sensor by PulseOn Ltd was utilized for monitoring IBIs from 18 healthy young adult subjects. Reference ambulatory polysomnography recordings were scored by a sleep physician. The HRV was studied by detrended fluctuation analysis by computing scale-dependent spectra of scaling exponents α(s). Dynamic changes were tracked by calculating the spectra α(s, t) in moving temporal windows whose length varied with the scale.The dynamic landscapes of the alpha spectra show distinctive fractal correlations according to the underlying sleep stages. Respiratory effects, blood pressure variations, and thermoregulatory influence appear to be discernible as well. Classification of the alpha spectra yields up to 73 %, 60 % and 54 % average accuracies for 3-class (wake, REM, NREM), 4-class (wake, REM, N1+2, N3) and 5-class (wake, REM, N1, N2, N3) cases, respectively.
{"title":"Non-Linear Heart Rate Variability Measures in Sleep Stage Analysis with Photoplethysmography","authors":"Matti Molkkari, M. Tenhunen, A. Tarniceriu, A. Vehkaoja, S. Himanen, Esa Räsänen","doi":"10.23919/CinC49843.2019.9005643","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005643","url":null,"abstract":"We assess the feasibility of heart rate variability (HRV) estimated from interbeat interval (IBI) data measured with wrist-worn photoplethysmography device for sleep stage classification. In particular, we examine fractal correlations in the IBIs as the function of both time and scale.Optical heart rate sensor by PulseOn Ltd was utilized for monitoring IBIs from 18 healthy young adult subjects. Reference ambulatory polysomnography recordings were scored by a sleep physician. The HRV was studied by detrended fluctuation analysis by computing scale-dependent spectra of scaling exponents α(s). Dynamic changes were tracked by calculating the spectra α(s, t) in moving temporal windows whose length varied with the scale.The dynamic landscapes of the alpha spectra show distinctive fractal correlations according to the underlying sleep stages. Respiratory effects, blood pressure variations, and thermoregulatory influence appear to be discernible as well. Classification of the alpha spectra yields up to 73 %, 60 % and 54 % average accuracies for 3-class (wake, REM, NREM), 4-class (wake, REM, N1+2, N3) and 5-class (wake, REM, N1, N2, N3) cases, respectively.","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":"82513758","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/CinC49843.2019.9005827
Matthias Schaufelberger, S. Schuler, L. Bear, M. Cluitmans, Jaume Coll-Font, Ö. N. Onak, O. Dössel, D. Brooks
Activation times (AT) represent the sequence of cardiac depolarization and are one of the most important parameters of cardiac electrical activity. However, estimation of ATs is challenging due to multiple sources of noise. If ATs are estimated from signals reconstructed using electrocardiographic imaging (ECGI), additional problems can arise due to over-smoothing or ambiguities in the inverse problem. Resulting AT maps can show falsely homogeneous regions or artificial lines of block. As ATs are not only important clinically, but are also used for evaluation of ECGI, it is important to understand where these errors come from.We present results from a community effort to compare AT estimation methods on a common dataset of simulated ventricular pacings. ECGI reconstructions were performed in terms of transmembrane voltages as well as epiendo and pericardial potentials, all using 2nd-order Tikhonov and 6 regularization parameters. ATs were then estimated by the participants and compared to the truth.While the pacing site had the largest effect on AT correlation coefficients (CC), there were also differences between methods and source models that were poorly reflected in CCs. Results indicate that artificial lines of block are most severe for purely temporal methods. Compared to the other source models, ATs estimated from transmembrane voltages are more precise and less prone to artifacts.
{"title":"Comparison of Activation Times Estimation for Potential-Based ECG Imaging","authors":"Matthias Schaufelberger, S. Schuler, L. Bear, M. Cluitmans, Jaume Coll-Font, Ö. N. Onak, O. Dössel, D. Brooks","doi":"10.23919/CinC49843.2019.9005827","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005827","url":null,"abstract":"Activation times (AT) represent the sequence of cardiac depolarization and are one of the most important parameters of cardiac electrical activity. However, estimation of ATs is challenging due to multiple sources of noise. If ATs are estimated from signals reconstructed using electrocardiographic imaging (ECGI), additional problems can arise due to over-smoothing or ambiguities in the inverse problem. Resulting AT maps can show falsely homogeneous regions or artificial lines of block. As ATs are not only important clinically, but are also used for evaluation of ECGI, it is important to understand where these errors come from.We present results from a community effort to compare AT estimation methods on a common dataset of simulated ventricular pacings. ECGI reconstructions were performed in terms of transmembrane voltages as well as epiendo and pericardial potentials, all using 2nd-order Tikhonov and 6 regularization parameters. ATs were then estimated by the participants and compared to the truth.While the pacing site had the largest effect on AT correlation coefficients (CC), there were also differences between methods and source models that were poorly reflected in CCs. Results indicate that artificial lines of block are most severe for purely temporal methods. Compared to the other source models, ATs estimated from transmembrane voltages are more precise and less prone to artifacts.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"22 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":"86945035","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/CinC49843.2019.9005835
R. Arathy, P. Nabeel, J. Jayaraj, V. AbhidevV., M. Sivaprakasam
Non-invasive, continuous measurement of arterial stiffness indices has established utility in cardiovascular risk stratification. This study aims to develop a subject-specific model of soft tissue sandwich from the common carotid artery wall to the skin surface layer using acceleration plethysmograph (APG) waveforms. It was then used to estimate the lumen arterial diameter waveform using APG for stiffness evaluation. The carotid APG waveforms were collected using the developed accelerometer probe and its associated measurement system. The relationship between carotid diameter and APG from the neck was evaluated via mathematical models using system identification in MATLAB. The performance of the developed model for non-invasive assessment of carotid diameter and stiffness indices was validated on 15 subjects.The developed model was implemented in real-time and continuously evaluated carotid diameter using APG from the neck. An RMSE of less than 0.14 mm was observed between the constructed carotid diameter waveform (using APG) when compared with an actual diameter measured using the ultrasound-based system. The study results demonstrated the feasibility of a subject-specific skin-tissue model with APG waveforms for arterial diameter measurement and estimation of the vessel stiffness.
{"title":"Evaluation of Arterial Diameter by Mathematical Transformation of APG for Ambulatory Stiffness","authors":"R. Arathy, P. Nabeel, J. Jayaraj, V. AbhidevV., M. Sivaprakasam","doi":"10.23919/CinC49843.2019.9005835","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005835","url":null,"abstract":"Non-invasive, continuous measurement of arterial stiffness indices has established utility in cardiovascular risk stratification. This study aims to develop a subject-specific model of soft tissue sandwich from the common carotid artery wall to the skin surface layer using acceleration plethysmograph (APG) waveforms. It was then used to estimate the lumen arterial diameter waveform using APG for stiffness evaluation. The carotid APG waveforms were collected using the developed accelerometer probe and its associated measurement system. The relationship between carotid diameter and APG from the neck was evaluated via mathematical models using system identification in MATLAB. The performance of the developed model for non-invasive assessment of carotid diameter and stiffness indices was validated on 15 subjects.The developed model was implemented in real-time and continuously evaluated carotid diameter using APG from the neck. An RMSE of less than 0.14 mm was observed between the constructed carotid diameter waveform (using APG) when compared with an actual diameter measured using the ultrasound-based system. The study results demonstrated the feasibility of a subject-specific skin-tissue model with APG waveforms for arterial diameter measurement and estimation of the vessel stiffness.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"537 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":"87110531","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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