Computing in cardiology最新文献

{"title":"Deep Learning System for Left Ventricular Assist Device Candidate Assessment from Electrocardiograms.","authors":"Antonio Mendoza, Mehdi Razavi, Joseph R Cavallaro","doi":"10.22489/cinc.2023.180","DOIUrl":"https://doi.org/10.22489/cinc.2023.180","url":null,"abstract":"<p><p>Left Ventricular Assist Devices (LVADs) are increasingly used as long-term implantation therapy for advanced heart failure patients, where candidacy assessment is crucial for successful treatment and recovery. A Deep Learning system based on Electrocardiogram (ECG) diagnoses criteria to stratify candidacy is proposed, implementing multi-model processing, interpretability, and uncertainty estimation. The approach includes beat segmentation for single-lead classification, 12-lead analysis, and semantic segmentation, achieving state-of-the-art results on the classification evaluation of each model, with multilabel average AUC results of 0.9924, 0.9468, and 0.9956, respectively, presenting a novel approach for LVAD candidacy assessment, serving as an aid for decision-making.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11021018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140867808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Unlabeled Electroencephalographic Data to Predict Neurological Recovery for Comatose Patients Following Cardiac Arrest.","authors":"Isaac Sears, Augusto Garcia-Agundez, George Zerveas, William Rudman, Laura Mercurio, Corey E Ventetuolo, Adeel Abbasi, Carsten Eickhoff","doi":"10.22489/CinC.2023.308","DOIUrl":"10.22489/CinC.2023.308","url":null,"abstract":"<p><p>In response to the 2023 George B. Moody PhysioNet Challenge, we propose an automated, unsupervised pre-training approach to boost the performance of models that predict neurologic outcomes after cardiac arrest. Our team, (BrownBAI), developed a model architecture consisting of three parts: a pre-processor to convert raw electroencephalograms (EEGs) into two-dimensional spectrograms, a three-layer convolutional neural network (CNN) encoder for unsupervised pre-training, and a time series transformer (TST) model. We trained the CNN encoder on unlabeled five-minute EEG samples from the Temple University EEG Corpus (TUEG), which included more than 20x the patients available in the PhysioNet competition training dataset. We then incorporated the pre-trained encoder into the TST as a base layer and trained the composite model as a classifier on EEGs from the 2023 PhysioNet Challenge dataset. Our team was not able to submit an official competition entry and was therefore not scored on the test set. However, in a side-by-side comparison on the competition training dataset, our model performed better with a pretrained (competition score 0.351), rather than randomly initialized (competition score 0.211) CNN encoder layer. These results show the potential benefits of leveraging unlabeled data to boost task-specific performance of predictive EEG models.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11544604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Biventricular Pacing Locations on Anti-Tachycardia Pacing Success in a Patient-Specific Model.","authors":"Eric N Paccione, Matthias Lange, Benjamin A Orkild, Jake A Bergquist, Eugene Kwan, Bram Hunt, Derek Dosdall, Rob S Macleod, Ravi Ranjan","doi":"10.22489/CinC.2023.369","DOIUrl":"10.22489/CinC.2023.369","url":null,"abstract":"<p><p>Patients with drug-refractory ventricular tachycardia (VT) often undergo implantation of a cardiac defibrillator (ICD). While life-saving, shock from an ICD can be traumatic. To combat the need for defibrillation, ICDs come equipped with low-energy pacing protocols. These anti-tachycardia pacing (ATP) methods are conventionally delivered from a lead inserted at the apex of the right ventricle (RV) with limited success. Recent studies have shown the promise of biventricular leads placed in the left ventricle (LV) for ATP delivery. This study tested the hypothesis that stimulating ATP from multiple biventricular locations will improve termination rates in a patient-specific computational model. VT was first induced in the model, followed by ATP delivery from 1-4 biventricular stimulus sites. We found that combining stimulation sites does not alter termination success so long as a critical stimulus site is included. Combining the RV stimulus site with any combination of LV sites did not affect ATP success except for one case. Including the RV site may allow biventricular ATP to be a robust approach across different scar distributions without affecting the efficacy of other stimulation sites. Combining sites may increase the likelihood of including a critical stimulus site when such information cannot be ascertained.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10906957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140023540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty Quantification of the Effect of Variable Conductivity in Ventricular Fibrotic Regions on Ventricular Tachycardia.","authors":"Jake A Bergquist, Matthias Lange, Brian Zenger, Ben Orkild, Eric Paccione, Eugene Kwan, Bram Hunt, Jiawei Dong, Rob S MacLeod, Akil Narayan, Ravi Ranjan","doi":"10.22489/cinc.2023.141","DOIUrl":"10.22489/cinc.2023.141","url":null,"abstract":"<p><p>Ventricular tachycardia (VT) is a life-threatening cardiac arrhythmia for which a common treatment pathway is electroanatomical mapping and ablation. Recent advances in both noninvasive ablation techniques and computational modeling have motivated the development of patient-specific computational models of VT. Such models are parameterized by a wide range of inputs, each of which is associated with an often unknown amount of error and uncertainty. Uncertainty quantification (UQ) is a technique to assess how variability in the inputs to a model affects its outputs. UQ has seen increased attention in computational cardiology as an avenue to further improve, understand, and develop patient-specific models. In this study we applied polynomial chaos-based UQ to explore the effect of varying the tissue conductivity of fibrotic border zones in a patient-specific model on the resulting VT simulation. We found that over a range of inputs, the model was most sensitive to fibrotic sheet direction, and uncertainty in fibrotic conductivity resulted in substantial variability in the VT reentry duration and cycle length. Overall, this study paves the way for future UQ applications to improve and understand VT models.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Hypoxic-Ischemic Encephalopathy Using Events in Fetal Heart Rate and Uterine Pressure.","authors":"Johann Vargas-Calixto, Yvonne W Wu, Michael Kuzniewicz, Marie-Coralie Cornet, Heather Forquer, Lawrence Gerstley, Emily Hamilton, Philip Warrick, Robert Kearney","doi":"10.22489/cinc.2023.380","DOIUrl":"10.22489/cinc.2023.380","url":null,"abstract":"<p><p>The objective of this work was to evaluate the utility of using intrapartum fetal heart rate (FHR) and uterine pressure (UP) events to detect infants at risk of hypoxic-ischemic encephalopathy (HIE). We analyzed data from 40,976 term births from three groups: 374 infants that developed HIE, 3,056 that developed fetal acidosis without HIE, and 37,546 healthy infants. We counted the transitions between FHR events and the length of FHR and UP events. Then, we used these features to train a random forest classifier to discriminate between the healthy and the pathological (acidosis or HIE) groups. Compared to the Caesarean delivery rates for each group, our system detected 6.9% more HIE cases (54.9% vs 61.8%, p<0.001) and 10.7% more acidosis cases (37.6% vs 48.3%, p<0.001), with no increase in the false positive rates in the healthy group (38.9% vs 38.8%, p=0.26). Importantly, over 3/4 of the HIE detections were made 3 hours or more before delivery. It is reasonable to expect that this would be enough lead time to permit clinical intervention to improve the outcome of birth.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rotors Drift Toward and Stabilize in Low Power Regions in Heterogeneous Models of Atrial Fibrillation.","authors":"Laura Martinez-Mateu,&nbsp;Javier Saiz,&nbsp;Omer Berenfeld","doi":"10.22489/cinc.2022.366","DOIUrl":"https://doi.org/10.22489/cinc.2022.366","url":null,"abstract":"<p><p>Atrial fibrillation (AF) afflicts more than 33 million people worldwide. Success of therapy strategies remains poor and better understanding of the arrhythmia and how to device more effective therapies are needed. The aim of this work is to study the role of electric power distributions in rotors and AF dynamics. For this purpose, single cell and tissue simulations were performed to study the effect of ionic currents gradients and fibrosis in rotor's drifting. The root mean square of the ionic (P_ion), capacitance (P_c) and electrotonic (P_ele) power was computed over action potentials. Single cell simulations were performed for different values of I_K1 and I_CaL and number of coupled myofibroblasts. Tissue simulations were performed in presence of I_K1 and I_CaL gradients and diffused fibrosis. Single cell simulations showed that P_ion and P_c increased with I_K1, while decreased by increasing I_CaL. Increasing the number of coupled myofibroblasts reduced P_ion and P_c, whereas P_ele increased. Finally, in tissue simulations rotors drifted to regions with low power and anchored in regions with higher density of blunted ionic induced power gradients.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"49 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411388/pdf/nihms-1906756.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10349389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Segmentation Variability in Forward ECG Simulation.","authors":"Beata Ondrusova,&nbsp;Machteld Boonstra,&nbsp;Jana Svehlikova,&nbsp;Dana Brooks,&nbsp;Peter van Dam,&nbsp;Ali Salman Rababah,&nbsp;Akil Narayan,&nbsp;Rob MacLeod,&nbsp;Nejib Zemzemi,&nbsp;Jess Tate","doi":"10.22489/cinc.2022.325","DOIUrl":"10.22489/cinc.2022.325","url":null,"abstract":"<p><p>Segmentation of patient-specific anatomical models is one of the first steps in Electrocardiographic imaging (ECGI). However, the effect of segmentation variability on ECGI remains unexplored. In this study, we assess the effect of heart segmentation variability on ECG simulation. We generated a statistical shape model from segmentations of the same patient and generated 262 cardiac geometries to run in an ECG forward computation of body surface potentials (BSPs) using an equivalent dipole layer cardiac source model and 5 ventricular stimulation protocols. Variability between simulated BSPs for all models and protocols was assessed using Pearson's correlation coefficient (CC). Compared to the BSPs of the mean cardiac shape model, the lowest variability (average CC = 0.98 ± 0.03) was found for apical pacing whereas the highest variability (average CC = 0.90 ± 0.23) was found for right ventricular free wall pacing. Furthermore, low amplitude BSPs show a larger variation in QRS morphology compared to high amplitude signals. The results indicate that the uncertainty in cardiac shape has a significant impact on ECGI.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"49 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552847/pdf/nihms-1884572.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a Quasi-dynamic Pulsed Field Electroporation Numerical Model for Cardiac Ablation: Predicting Tissue Conductance Changes and Ablation Lesion Patterns.","authors":"Richard Simon,&nbsp;Nishaki K Mehta,&nbsp;Kuldeep B Shah,&nbsp;David E Haines,&nbsp;Cristian A Linte","doi":"10.22489/CinC.2022.233","DOIUrl":"https://doi.org/10.22489/CinC.2022.233","url":null,"abstract":"<p><p>Pulsed field ablation (PFA) has the potential to evolve into an efficient alternative to traditional RF ablation for atrial fibrillation treatment. However, achieving irreversible tissue electroporation is critical to suppressing arrhythmic pathways, raising the need for accurate lesion characterization. To understand the physics behind the tissue response PFA, we propose a quasi-dynamic model that quantifies tissue conductance at end-electroporation and identifies regions that have undergone fully irreversible electroporation (IRE). The model uses several parameters and numerically solves the electrical field diffusion into the tissue by iteratively updating the tissue conductance until equilibrium at end-electroporation. The model yields a steady-state tissue conductance map used to identify the irreversible lesion. We conducted numerical experiments mimicking a lasso catheter featuring nine 3-mm electrodes spaced circumferentially at 3.75 mm and fired sequentially using a 1500 V and 3000 V pulse amplitude. The IRE lesion region has a surface area and volume of 780 mm² and 1411 mm³, respectively, at 1500 V, and 1178 mm² and 2760 mm³, respectively, at 3000 V. Lesion discontinuity was observed at 5.0 mm depth with 1500 V, and 7.2 mm depth with 3000 V. This quasi-dynamic model yields tissue conductance maps, predicts irreversible lesion and lesion penumbra at end-electroporation, and confirms larger lesions with higher pulse amplitudes.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"2022 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134894/pdf/nihms-1892746.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9391928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization.","authors":"Narimane Gassa, Machteld Boonstra, Beata Ondrusova, Jana Svehlikova, Dana Brooks, Akil Narayan, Ali Salman Rababah, Peter van Dam, Rob MacLeod, Jess Tate, Nejib Zemzemi","doi":"10.22489/cinc.2022.275","DOIUrl":"10.22489/cinc.2022.275","url":null,"abstract":"<p><p>Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fully-automated yet and is in consequence operator-dependent. We expect that the inter-operator variation in cardiac segmentation would influence the ECGI solution. This effect remains however non quantified. In the present work, we study the effect of segmentation variability on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Therefore, we designed two test cases: with and without shape model uncertainty. Moreover, we used four cases for ectopic ventricular excitation and compared the ECGI results in terms of reconstructed activation times and excitation origins. The preliminary results indicate that a small variation of the activation maps can be observed with a model uncertainty but no significant effect on the source localization is observed.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"49 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544807/pdf/nihms-1884573.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41156365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Distributions of Parameters for Cardiac Action Potential Models Using the Hamiltonian Monte Carlo Method.","authors":"Alejandro Nieto Ramos,&nbsp;Conner J Herndon,&nbsp;Flavio H Fenton,&nbsp;Elizabeth M Cherry","doi":"10.23919/cinc53138.2021.9662836","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662836","url":null,"abstract":"<p><strong>Aims: </strong>Cardiac action potential (AP) models are typically given with a single set of parameter values; however, this approach does not consider variability and uncertainty across individuals and experimental conditions. As an alternative to single-value parameter fitting, we sought to use a Bayesian approach, the Hamiltonian Monte Carlo (HMC) algorithm, to find distributions of physiological parameter values for cardiac AP models across a range of cycle lengths (CLs) and dynamics.</p><p><strong>Methods: </strong>To assess HMC's accuracy for cardiac data, we applied it to synthetic APs from the Mitchell-Shaeffer (MS) and Fenton-Karma (FK) models with added noise over a range of physiological CLs, some of which included alternans. To show the applicability of HMC to experimental data, we calculated parameter distributions for both models using micro-electrode recordings of zebrafish APs from a range of CLs.</p><p><strong>Results: </strong>For synthetic APs generated from three CLs using the MS (FK) models, HMC produced unimodal quasi-symmetric distributions for all five (13) parameters. APs generated by setting all parameters in the MS (FK) model to the modes of their corresponding marginal distributions yielded errors in voltage traces below 5.0% (0.6%). We also obtained distributions for the MS (FK) model parameters using zebrafish data to construct the first minimal model of the zebrafish AP, with voltage trace errors below 4.8% (3.4%).</p><p><strong>Conclusion: </strong>We have shown that HMC can identify not only a single set of parameter values but also viable distributions for cardiac AP model parameters using synthetic and experimental data. Because HMC generates samples from the parameter distributions based on input data, it can produce families of parameterizations that can be used in population-based modeling approaches without the need for rejecting a large number of randomly generated candidate parameterizations. HMC also has the potential to provide quantitative measures of spatial/individual variability and uncertainty.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228588/pdf/nihms-1815728.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}