2022 Computing in Cardiology (CinC)最新文献

Classification of Atrial Tachycardia Types Using Dimensional Transforms of ECG Signals and Machine Learning 基于心电信号维数变换和机器学习的房性心动过速类型分类

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.349

S. Ruipérez-Campillo, J. Millet-Roig, F. Castells

引用次数: 0

Effect of Contact Force on Local Electrical Impedance in Atrial Tissue - an In Silico Evaluation 接触力对心房组织局部电阻抗的影响及其计算机评价

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.337

C. Antón, Jorge Sánchez, Andreas Heinkele, L. Unger, A. Haas, K. Schmidt, A. Luik, A. Loewe, O. Doessel

{"title":"Effect of Contact Force on Local Electrical Impedance in Atrial Tissue - an In Silico Evaluation","authors":"C. Antón, Jorge Sánchez, Andreas Heinkele, L. Unger, A. Haas, K. Schmidt, A. Luik, A. Loewe, O. Doessel","doi":"10.22489/CinC.2022.337","DOIUrl":"https://doi.org/10.22489/CinC.2022.337","url":null,"abstract":"Regions with pathologically altered substrate have been identified as potential drivers for atrial fibrillation (AF) maintenance. Recently, local impedance (LI) measurements have gained attention as surrogate for atrial substrate assessment as it does not rely on electrical activity of the heart. However, an appropriate electrode-tissue contact force (CF) is needed and its effect on the LI measurements has not yet been characterized in depth. In this study, we applied several CF to a catheter in contact with a tissue patch modeled as healthy and scar atrial myocardium whose thickness was varied in anatomical ranges to study the impact of the mechanical deformation the LI measurements. When applying CF between 0 and 6g, in silico LI ranged from 160 $Omega$ to 175 $Omega$ in healthy my-ocardium, whereas 148 $Omega$ and 151 $Omega$ for scar tissue. Increasing CF in scar tissue up to 25 g, increased LI up to 156 $Omega$. The model was validated against clinically measured LI at different CF from AF patients. Simulation results applying identical CF in both tissues yielded lower LI values in scar. Moreover, LI increased in healthy and scar tissue when the thickness and CF were increased. Given the results of our study, we conclude that in silico experiments can not only distinguish between healthy and scar tissue by combining CF and LI, but also that our simulation environment represents clinical LI measurements with and without mechanical deformation in a tissue model.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"336 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653246","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}

引用次数: 0

The Effect of Heart Rate and Atrial Contraction on Left Ventricular Function 心率和心房收缩对左心室功能的影响

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.212

Rosie Barrows, M. Strocchi, Christoph M. Augustin, M. Gsell, C. Roney, J. Solís-Lemus, Hao Xu, K. Gillette, R. Rajani, J. Whitaker, E. Vigmond, M. Bishop, G. Plank, S. Niederer

{"title":"The Effect of Heart Rate and Atrial Contraction on Left Ventricular Function","authors":"Rosie Barrows, M. Strocchi, Christoph M. Augustin, M. Gsell, C. Roney, J. Solís-Lemus, Hao Xu, K. Gillette, R. Rajani, J. Whitaker, E. Vigmond, M. Bishop, G. Plank, S. Niederer","doi":"10.22489/CinC.2022.212","DOIUrl":"https://doi.org/10.22489/CinC.2022.212","url":null,"abstract":"Heart rate (HR) and effective atrial contraction affect left ventricular (LV) output. This is particularly relevant in atrial fibrillation (AF) patients, where HR is fast and irregular and atrial contraction almost completely absent. The effect of AF on the LV remains understudied, although a better understanding of these mechanisms could improve AF patient care. We have used a four-chamber electromechanics model to quantify how AF impacts LV function. Our model accounts for the effect of the pericardium and the coupling with the circulatory system, represented as a closed loop, providing physiological preload and afterload for the heart. The heart model was used for a factorial study with two HRs (70 bpm and 120 bpm) and in the presence and in the absence of atrial contraction. We found that an increased HR and lack of atrial contraction alone led to a small decrease in ejection fraction (42% to 40% and 42% to 41%, respectively). However, the interaction between an increased HR and lack of atrial contraction led to a drop in ejection fraction from 42% to 36%. This study demonstrates that our four-chamber heart models can be used to investigate the effect of rapid HR and ineffective atrial contraction on LV output and that AF can significantly impact LV function. This motivates further studies investigating the effect of AF on the whole heart.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116780075","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}

引用次数: 0

Autocorrelation Function for Predicting Arrhythmic Recurrences in Patients Undergoing Persistent Atrial Fibrillation Ablation 预测持续性房颤消融患者心律失常复发的自相关功能

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.424

R. Abad, E. Franco, S. Ruipérez-Campillo, C. Lozano, F. Castells, J. Moreno

引用次数: 0

Modelling and Simulation Reveals Density-Dependent Re-Entry Risk in The Infarcted Ventricles After Stem Cell-Derived Cardiomyocyte Delivery 模型和模拟揭示了干细胞衍生心肌细胞输送后梗死心室的密度依赖性再入风险

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.392

Leto L. Riebel, Z. Wang, H. Martinez-Navarro, C. Trovato, J. Biasetti, R. S. Oliveira, R. D. Santos, Blanca A Rodríguez

{"title":"Modelling and Simulation Reveals Density-Dependent Re-Entry Risk in The Infarcted Ventricles After Stem Cell-Derived Cardiomyocyte Delivery","authors":"Leto L. Riebel, Z. Wang, H. Martinez-Navarro, C. Trovato, J. Biasetti, R. S. Oliveira, R. D. Santos, Blanca A Rodríguez","doi":"10.22489/CinC.2022.392","DOIUrl":"https://doi.org/10.22489/CinC.2022.392","url":null,"abstract":"Delivery of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) is a potential therapy to improve cardiac function after injury. However, hPSCCMs express immature electrophysiological and structural properties and may be pro-arrhythmic. Our goal is to identify key factors determining arrhythmic risk of hPSC-CM therapy in the infarcted human ventricles through modelling and simulation. We model three densities of hPSC-CMs covering 4%, 22%, and 39% of the infarct and border zone and induce re-entry through ectopic stimulation. We furthermore simulate the effect of different therapeutic agents on re-entry susceptibility. Due to the increased refractory period of the hPSC-CMs, the vulnerable window increases from 20ms in control, to 60ms in the low- and 80ms in the medium- and high-density scenarios. Our results highlight the density-dependent effect of hPSC-CM delivery on arrhythmic risk after myocardial infarction and show the effect of therapeutic strategies on this increased re-entry susceptibility.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117343241","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}

引用次数: 0

Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram 心音图检测心脏杂音的有效神经网络结构研究

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.130

Hao Wen, Ji-Su Kang

{"title":"Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram","authors":"Hao Wen, Ji-Su Kang","doi":"10.22489/CinC.2022.130","DOIUrl":"https://doi.org/10.22489/CinC.2022.130","url":null,"abstract":"Aim: The George B. Moody PhysioNet Challenge 2022 raised problems of heart murmur detection and related abnormal cardiac function identification from phonocardiograms (PCGs). This work describes the novel approaches developed by our team, Revenger, to solve these problems. Methods: PCGs were resampled to 1000 $Hz$, then filtered with a Butterworth band-pass filter of order 3, cut-off frequencies 25 - 400 $H{z}$, and z-score normalized. $We$ used the multi-task learning $(MTL)$ method via hard parameter sharing to train one neural network (NN) model for all the Challenge tasks. We performed neural architecture searching among a set of network backbones, including multi-branch convolutional neural networks (CNNs), SE-ResNets, TResNets, simplified $wav2vec2$, etc. Based on a stratified splitting of the subjects, 20% of the public data was left out as a validation set for model selection. The AdamW optimizer was adopted, along with the OneCycle scheduler, to optimize the model weights. Results: Our murmur detection classifier received a weighted accuracy score of 0.736 (ranked 14th out of 40 teams) and a Challenge cost score of 12944 (ranked 19th out of 39 teams) on the hidden validation set. Conclusion: We provided a practical solution to the problems of detecting heart murmurs and providing clinical diagnosis suggestions from PCGs.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127289174","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}

引用次数: 1

Automatic Sleep Arousal Detection Using Heart Rate From a Single-Lead Electrocardiogram 利用单导联心电图的心率自动检测睡眠唤醒

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.080

Franz Ehrlich, Johannes Bender, Hagen Malberg, Miriam Goldammer

{"title":"Automatic Sleep Arousal Detection Using Heart Rate From a Single-Lead Electrocardiogram","authors":"Franz Ehrlich, Johannes Bender, Hagen Malberg, Miriam Goldammer","doi":"10.22489/CinC.2022.080","DOIUrl":"https://doi.org/10.22489/CinC.2022.080","url":null,"abstract":"Arousals during sleep give deep insights into the patho-physiology of sleep disorders and sleep quality. Detecting arousals is a time-consuming process manually per-formed by a trained expert. The required measurement is performed on an inpatient basis and is uncomfortable for the patient. As arousals relate to the autonomic nervous system, they also reflect in the electrocardiogram, which is therefore a promising alternative biosignal. In this study, we developed a deep learning model for automatic detection of sleep arousals from heart rate. We developed our algorithm using 5323 recordings from the Sleep Heart Health Study. 1003 of them were held-out as test data. We derived RR intervals from the ECG and interpolated them into a 4 Hz signal. Next, we developed a convolutional neural network (CNN) for end-to-end event detection. Model output is a continuous arousal probabil-ity with a frequency of 1 Hz. The optimization resulted in a twelve-layer CNN that achieved a Cohens kappa of 0.47, an area under the precision-recall curve of 0.54 on hold-out test data. This study demonstrates the ability of machine learning to detect arousals during sleep from heart rate. As our approach uses only the heart rate, it is potentially trans-ferable to other signals, e.g. the photoplethysmogram.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918010","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}

引用次数: 2

Automated Detection of Ventricular Heartbeats from Electrocardiogram (ECG) Acquired During Magnetic Resonance Imaging (MRI) 从磁共振成像(MRI)获得的心电图(ECG)中自动检测心室心跳

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.192

Pierre G Aublin, J. Felblinger, J. Oster

{"title":"Automated Detection of Ventricular Heartbeats from Electrocardiogram (ECG) Acquired During Magnetic Resonance Imaging (MRI)","authors":"Pierre G Aublin, J. Felblinger, J. Oster","doi":"10.22489/CinC.2022.192","DOIUrl":"https://doi.org/10.22489/CinC.2022.192","url":null,"abstract":"ECGs are highly distorted by the MRI environment, making automated ECG analysis highly difficult. This study aimed at implementing a machine-learning (ML) based heartbeat classifier, using hand-crafted features, for the automatic detection of ventricular heartbeats during MRI. A model was trained on the MIT-BIH Arrhythmia Database and assessed on an in-house database of ECG acquired inside a 1.5T MRI (ECG-MRI). Features were extracted for each heartbeat from single-lead ECG signals including QRS morphological features based on Hermite functions, and RR interval-based features. A support vector machine was trained to classify normal (N) and ventricular ectopic beats (V‘). The classifier achieved F1 scores of 0.85 on the V' class on the validation fold on the MIT-BIH database, while it only achieved F1 scores of 0.15 on the ECG-MRI database. The proposed heartbeat classifier was developed on the MIT-BIH arrhythmia database using temporal features and QRS morphological features based on the assumption they would be less distorted by the MRI environment. However, even if performance on MIT-BIH were acceptable (although slightly lower than state-of-the-art approaches), results were poor on the ECG-MRI database. The results highlight the need for further developments by suppressing MRI-related artifacts, and by retraining on MRI specific datasets.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126037047","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}

引用次数: 0

Maiby's Algorithm: A Two-Stage Deep Learning Approach for Murmur Detection in Mel Spectrograms for Automatic Auscultation of Congenital Heart Disease 迈比算法:一种用于先天性心脏病自动听诊Mel谱图中杂音检测的两阶段深度学习方法

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.249

"Matheus Araujo, Dewen Zeng, João Palotti, Xinrong Hu, Yiyu Shi, L. Pyles, Q. Ni

{"title":"Maiby's Algorithm: A Two-Stage Deep Learning Approach for Murmur Detection in Mel Spectrograms for Automatic Auscultation of Congenital Heart Disease","authors":"\"Matheus Araujo, Dewen Zeng, João Palotti, Xinrong Hu, Yiyu Shi, L. Pyles, Q. Ni","doi":"10.22489/CinC.2022.249","DOIUrl":"https://doi.org/10.22489/CinC.2022.249","url":null,"abstract":"Congenital heart disease (CHD) is a major cause of death for newborns, especially in low resources countries, due to limited access to heart specialists for timely diagnosis. As part of the George B. Moody PhysioNet Challenge 2022, we propose an automatic algorithm to detect CHD murmurs from digitally recorded heart sounds annotated by specialists. To train and validate our model, we use a dataset with 5282 heart sounds collected from 1568 children in the Paraiba state of Brazil recorded from multiple auscultation locations. Our team, named One_Heart_Health, used a two-stage strategy that combines embeddings from Mel spectrograms generated from audio segments and a final classifier that combine those embeddings to deliver the final classification per individual. On the official hidden test, we reached a weighted accuracy score of 0.729 (ranked 17th out of 40) and a challenge cost score of 13283 (ranked 23th out of 39). In our internal 5-fold cross-validation experiments, our approach reached a sensitivity of 0.76 ± 0.10 and a specificity of 0.85 ± 0.11. We have shown that a deep learning approach for murmur detection has the potential to mimic heart specialists to provide timely identification of CHD.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126131542","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}

引用次数: 3

Harnessing Dermal Blood Flow to Mitigate Skin Heating Effects in Wireless Transdermal Energy Systems for Driving Heart Pumps 在驱动心脏泵的无线透皮能量系统中利用皮肤血流减轻皮肤发热效应

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.409

Mohammad L. Karim, A. Bosnjak, J. Mclaughlin, P. Crawford, D. McEneaney, O. Escalona

引用次数: 0