Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662927
Charalampos Eleftheriadis, G. Karakonstantis
In this paper, we propose an algorithm for fast and accurate Power Spectral Analysis of Heart Rate Variability based on the Lomb Periodogram. The previously introduced Fast-Lomb periodogram, may have reduced the computational complexity of PSA, however it still requires a large oversampling factor, which increases the complexity of the needed FFTs. In our approach, by utilising the Fast Gaussian Gridding method we produce accurate evenly spaced grids for the required FFTs by restricting the oversampling factor only to 2. By doing so, the required FFT size is reduced by up to 4 times without compromising the output accuracy. Our results indicate that the proposed spectral analysis system can achieve upto 76.55% savings in the number of operations or up-to 75.8% in terms of the total execution time.
{"title":"Fast and Accurate Power Spectral Analysis of Heart Rate Variability using Fast Gaussian Gridding","authors":"Charalampos Eleftheriadis, G. Karakonstantis","doi":"10.23919/cinc53138.2021.9662927","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662927","url":null,"abstract":"In this paper, we propose an algorithm for fast and accurate Power Spectral Analysis of Heart Rate Variability based on the Lomb Periodogram. The previously introduced Fast-Lomb periodogram, may have reduced the computational complexity of PSA, however it still requires a large oversampling factor, which increases the complexity of the needed FFTs. In our approach, by utilising the Fast Gaussian Gridding method we produce accurate evenly spaced grids for the required FFTs by restricting the oversampling factor only to 2. By doing so, the required FFT size is reduced by up to 4 times without compromising the output accuracy. Our results indicate that the proposed spectral analysis system can achieve upto 76.55% savings in the number of operations or up-to 75.8% in terms of the total execution time.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129990248","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662722
Muhammad Usman, P. Rajagopalan, Aryel Beck, Jennifer Nathania, T. Li, T. Lim
The relationship between heart rate variability (HRV) and hypertension is well established in multiple studies. However, there is a lack of investigation on the impact of demographics and other diseases related to cardiovascular health on the performance of HRV based hypertension detection models. This study aims to address these issues by determining the efficacy of such models in an unconstrained setting. 24 hours long ECG were recorded for 1377 subjects. HRV features from time, frequency and nonlinear domains were extracted from 1 minute long R-peak to R-peak intervals (RRIs). Demographic factors of age, gender and body mass index (BMI) were added one by one as additional features into logistic regression models. The performance of the models was analysed with respect to different age groups. The results show that inclusion of age into the HRV model increased its accuracy from 71.7% to 77.6%. However, the model's predictions were mostly similar to the ones that would be obtained with an age based threshold. This is due to the natural age bias in the data which makes age a confounder for HRV based hypertension detection. This highlights the importance of naturally occurring demographics imbalance and how this must be carefully considered when developing HRV models for hypertension.
{"title":"Impact of Demographics on Short-term Heart Rate Variability for Detecting Hypertension","authors":"Muhammad Usman, P. Rajagopalan, Aryel Beck, Jennifer Nathania, T. Li, T. Lim","doi":"10.23919/cinc53138.2021.9662722","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662722","url":null,"abstract":"The relationship between heart rate variability (HRV) and hypertension is well established in multiple studies. However, there is a lack of investigation on the impact of demographics and other diseases related to cardiovascular health on the performance of HRV based hypertension detection models. This study aims to address these issues by determining the efficacy of such models in an unconstrained setting. 24 hours long ECG were recorded for 1377 subjects. HRV features from time, frequency and nonlinear domains were extracted from 1 minute long R-peak to R-peak intervals (RRIs). Demographic factors of age, gender and body mass index (BMI) were added one by one as additional features into logistic regression models. The performance of the models was analysed with respect to different age groups. The results show that inclusion of age into the HRV model increased its accuracy from 71.7% to 77.6%. However, the model's predictions were mostly similar to the ones that would be obtained with an age based threshold. This is due to the natural age bias in the data which makes age a confounder for HRV based hypertension detection. This highlights the importance of naturally occurring demographics imbalance and how this must be carefully considered when developing HRV models for hypertension.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025013","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662821
S. Cabon, Raphaël Weber, Léa Cailleau, G. Carrault, P. Pladys, F. Porée
A newborn is preterm if birth occurred before a gestational age of 37 weeks. He has several immature functions, which implies a specific monitoring and, among others, the analysis of its sleep. Here we make a focus on Quiet Sleep (QS), whose increasing is primordial with age, and characterized by an absence of motion and a regular cardiorespiratory rhythm. A method to automatically detect QS is proposed, on the basis of a video analysis (detection of motion), supplemented by the estimation of ECG and respiration “qualities”. This approach combines feature extraction and machine learning methods. It was validated on a set of 15 newborns and 25 eight-hours recordings manually annotated. Best results were obtained by combining non-motion intervals and ECG quality, but showing also an overestimation of $QS (Se=88%, Sp=49%)$. However, regarding extracted features, we observed similar trends between manual and automated QS, with an increasing of average duration of QS intervals and percentage of time in QS with age, also approaching values of the full-term newborns. Finally, computation of QS on a larger set of 45 recordings confirmed the interest of the approach for maturation evaluation purposes.
{"title":"Automated Quiet Sleep Detection for Premature Newborns Based on Video and ECG Analysis","authors":"S. Cabon, Raphaël Weber, Léa Cailleau, G. Carrault, P. Pladys, F. Porée","doi":"10.23919/cinc53138.2021.9662821","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662821","url":null,"abstract":"A newborn is preterm if birth occurred before a gestational age of 37 weeks. He has several immature functions, which implies a specific monitoring and, among others, the analysis of its sleep. Here we make a focus on Quiet Sleep (QS), whose increasing is primordial with age, and characterized by an absence of motion and a regular cardiorespiratory rhythm. A method to automatically detect QS is proposed, on the basis of a video analysis (detection of motion), supplemented by the estimation of ECG and respiration “qualities”. This approach combines feature extraction and machine learning methods. It was validated on a set of 15 newborns and 25 eight-hours recordings manually annotated. Best results were obtained by combining non-motion intervals and ECG quality, but showing also an overestimation of $QS (Se=88%, Sp=49%)$. However, regarding extracted features, we observed similar trends between manual and automated QS, with an increasing of average duration of QS intervals and percentage of time in QS with age, also approaching values of the full-term newborns. Finally, computation of QS on a larger set of 45 recordings confirmed the interest of the approach for maturation evaluation purposes.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132949581","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662754
O. Özgül, B. Hermans, A. Hunnik, S. Verheule, U. Schotten, P. Bonizzi, S. Zeemering
Localized AF drivers with repetitive activity are candidate ablation targets for patients with persistent atrial fibrillation (AF). High-density mapping electrodes cover only a fraction of the atria but combining sequential recordings could provide a more comprehensive picture of common repetitive atrial conduction characteristics and enable AF driver localization. We developed a novel algorithm to merge overlapping local activation maps into larger composite maps using recurrence plots. The proposed algorithm was applied to atrial recordings in a goat model of AF (249-electrode mapping array, 2.4 mm inter-electrode distance, $n=16$). Sequential, overlapping recordings were generated by segmenting the mapping region into four spatially overlapping regions. Repetitive activation patterns were detected from recurrence plots generated from the recorded electrograms, and reconstructed with the proposed algorithm. Reconstruction quality was measured as the Pearson correlation between original and reconstructed activation patterns. The average correlation was 0.86. Among pattern properties, such as duration, area, complexity and cycle length, only duration was significantly correlated with the composite map quality ($r=0.126, p < 0.05$). The percentage of the cases where a composite map could be generated was 75.30% which was significantly higher for larger patterns ($p < 0.01$).
重复性活动的局部房颤驱动因素是持续性房颤(AF)患者的候选消融靶点。高密度映射电极仅覆盖心房的一小部分，但结合顺序记录可以提供更全面的常见重复心房传导特征，并使AF驱动程序定位。我们开发了一种新的算法，利用递归图将重叠的局部激活图合并成更大的复合图。将该算法应用于山羊AF模型的心房记录(249个电极映射阵列，电极间距2.4 mm， $n=16$)。通过将映射区域分割成四个空间重叠的区域，生成顺序的重叠记录。从记录的电图生成的递归图中检测到重复的激活模式，并使用所提出的算法重建。重建质量通过原始激活模式和重建激活模式之间的Pearson相关性来衡量。平均相关系数为0.86。在持续时间、面积、复杂性和周期长度等模式属性中，只有持续时间与复合地图质量显著相关(r=0.126, p < 0.05)。生成复合地图的比例为75.30%，对于较大的模式，这一比例明显更高(p < 0.01)。
{"title":"High Coverage and High-Resolution Mapping of Repetitive Patterns During Atrial Fibrillation","authors":"O. Özgül, B. Hermans, A. Hunnik, S. Verheule, U. Schotten, P. Bonizzi, S. Zeemering","doi":"10.23919/cinc53138.2021.9662754","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662754","url":null,"abstract":"Localized AF drivers with repetitive activity are candidate ablation targets for patients with persistent atrial fibrillation (AF). High-density mapping electrodes cover only a fraction of the atria but combining sequential recordings could provide a more comprehensive picture of common repetitive atrial conduction characteristics and enable AF driver localization. We developed a novel algorithm to merge overlapping local activation maps into larger composite maps using recurrence plots. The proposed algorithm was applied to atrial recordings in a goat model of AF (249-electrode mapping array, 2.4 mm inter-electrode distance, $n=16$). Sequential, overlapping recordings were generated by segmenting the mapping region into four spatially overlapping regions. Repetitive activation patterns were detected from recurrence plots generated from the recorded electrograms, and reconstructed with the proposed algorithm. Reconstruction quality was measured as the Pearson correlation between original and reconstructed activation patterns. The average correlation was 0.86. Among pattern properties, such as duration, area, complexity and cycle length, only duration was significantly correlated with the composite map quality ($r=0.126, p < 0.05$). The percentage of the cases where a composite map could be generated was 75.30% which was significantly higher for larger patterns ($p < 0.01$).","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130840394","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662891
F. Renna, Miguel Martins, M. Coimbra
In this work, we propose a novel algorithm for heart sound segmentation. The proposed approach is based on the combination of two families of state-of-the-art solutions for such problem, hidden Markov models and deep neural networks, in a single training framework. The proposed approach is tested with heart sounds from the PhysioNet dataset and it is shown to achieve an average sensitivity of 93.9% and an average positive predictive value of 94.2% in detecting the boundaries of fundamental heart sounds.
{"title":"Joint Training of Hidden Markov Model and Neural Network for Heart Sound Segmentation","authors":"F. Renna, Miguel Martins, M. Coimbra","doi":"10.23919/cinc53138.2021.9662891","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662891","url":null,"abstract":"In this work, we propose a novel algorithm for heart sound segmentation. The proposed approach is based on the combination of two families of state-of-the-art solutions for such problem, hidden Markov models and deep neural networks, in a single training framework. The proposed approach is tested with heart sounds from the PhysioNet dataset and it is shown to achieve an average sensitivity of 93.9% and an average positive predictive value of 94.2% in detecting the boundaries of fundamental heart sounds.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923847","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662746
Zhuoyang Xu, Yangming Guo, Tingting Zhao, Zhuo Liu, Xingzhi Sun
As part of the PhysioNet/Computing in Cardiology Challenge 2021, Our team, HeartBeats, developed an ensembled model based on SE-ResNet for identifying 30 kinds of cardiac abnormalities from different lead combinations of electrocardiograms (ECGs). At pre-processing stage, ECGs were down-sampled to 500 Hz and each record is normalized using Z-Score normalization. We then employed several residual neural network modules with squeeze-and-excitation blocks to learn from the first 15-second segments of the signals. We designed a multi-label loss to emphasize the impact of wrong predictions during training. We relabelled the dataset which contains only 9 classes using our baseline model build in last year's challenge. Five-fold cross-validation was used to assess the performance of our models. Our classifiers received the scores of 0.58, 0.55, 0.56, 0.53, and 0.53 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions with the Challenge evaluation metric. Our final model performed well on the test data. However, the results were not officially ranked because our training code may select the offline pre-trained models rather than using the training data if the pre-trained models performed better than the trained models on the training data. The model can therefore fail to learn from new training data.
作为PhysioNet/Computing in Cardiology Challenge 2021的一部分，我们的团队HeartBeats开发了一个基于SE-ResNet的集成模型，用于从不同的心电图(ecg)导联组合中识别30种心脏异常。在预处理阶段，将心电图降采样至500 Hz，并使用Z-Score归一化将每个记录归一化。然后，我们使用了几个带有挤压和激励块的残差神经网络模块，从信号的前15秒片段中学习。我们设计了一个多标签损失来强调训练过程中错误预测的影响。我们使用我们在去年的挑战中构建的基线模型重新标记了只包含9个类的数据集。使用五重交叉验证来评估我们的模型的性能。我们的分类器在挑战评估指标的12导联、6导联、4导联、3导联和2导联版本中获得的分数分别为0.58、0.55、0.56、0.53和0.53。我们的最终模型在测试数据上表现良好。然而，结果没有正式排名，因为如果预训练模型在训练数据上的表现优于训练模型，我们的训练代码可能会选择离线预训练模型，而不是使用训练数据。因此，该模型可能无法从新的训练数据中学习。
{"title":"Multi-Label Cardiac Abnormalities Classification on Selected Leads of ECG Signals","authors":"Zhuoyang Xu, Yangming Guo, Tingting Zhao, Zhuo Liu, Xingzhi Sun","doi":"10.23919/cinc53138.2021.9662746","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662746","url":null,"abstract":"As part of the PhysioNet/Computing in Cardiology Challenge 2021, Our team, HeartBeats, developed an ensembled model based on SE-ResNet for identifying 30 kinds of cardiac abnormalities from different lead combinations of electrocardiograms (ECGs). At pre-processing stage, ECGs were down-sampled to 500 Hz and each record is normalized using Z-Score normalization. We then employed several residual neural network modules with squeeze-and-excitation blocks to learn from the first 15-second segments of the signals. We designed a multi-label loss to emphasize the impact of wrong predictions during training. We relabelled the dataset which contains only 9 classes using our baseline model build in last year's challenge. Five-fold cross-validation was used to assess the performance of our models. Our classifiers received the scores of 0.58, 0.55, 0.56, 0.53, and 0.53 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions with the Challenge evaluation metric. Our final model performed well on the test data. However, the results were not officially ranked because our training code may select the offline pre-trained models rather than using the training data if the pre-trained models performed better than the trained models on the training data. The model can therefore fail to learn from new training data.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125436878","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662833
Cunjin Luo, Ying He, Kuanquan Wang, Henggui Zhang
Short QT syndrome (SQTS) is a cardiac disorder characterized by arrhythmia and even sudden cardiac death (SCD). SQTS variant 3 (SQT3) has been linked to the KCNJ2 gene mutations, which directly increasing the inward rectifier K+ current $left({{I_{{mathrm{K}}1}}} right)$. There have been many studies on the effects of the mutation KCNJ2 D172N that cause the SQT3, but the potential effect of the mutation KCNJ2 E299V is little known. Therefore, we aim to predict and compare the potential effects of ion channels blocking under the E299V mutation. In this study, a biophysically detailed computer model of the heart which was developed by was coupled with the KCNJ2 E299V mutant IK1 patch clamp data. Effects of a combined action of blocking of ${I_{text{K1}}}$ and ICaL was also simulated under the E299V mutant condition. Our simulation data showed that a combined action of blocking of ${I_{text{K1}}}$ and ${I_{text{CaL}}}$ prolonged the cardiac cell action potential duration (APD) and QT interval under SQT3 E299V condition, and demonstrated that blocking of ${I_{text{K1}}}$ and ${I_{text{CaL}}}$ produced a therapeutic effect under SQT3 E299V. This study provides new evidence that blocking of ${I_{text{K1}}}$ and ${I_{text{CaL}}}$ may be a potential treatment for SQTS patients.
{"title":"Computational Analysis of the Effects of KCNJ2-linked E299V Mutation Short QT Syndrome and Its Potential Therapeutic Targets","authors":"Cunjin Luo, Ying He, Kuanquan Wang, Henggui Zhang","doi":"10.23919/cinc53138.2021.9662833","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662833","url":null,"abstract":"Short QT syndrome (SQTS) is a cardiac disorder characterized by arrhythmia and even sudden cardiac death (SCD). SQTS variant 3 (SQT3) has been linked to the KCNJ2 gene mutations, which directly increasing the inward rectifier K+ current $left({{I_{{mathrm{K}}1}}} right)$. There have been many studies on the effects of the mutation KCNJ2 D172N that cause the SQT3, but the potential effect of the mutation KCNJ2 E299V is little known. Therefore, we aim to predict and compare the potential effects of ion channels blocking under the E299V mutation. In this study, a biophysically detailed computer model of the heart which was developed by was coupled with the KCNJ2 E299V mutant IK1 patch clamp data. Effects of a combined action of blocking of ${I_{text{K1}}}$ and ICaL was also simulated under the E299V mutant condition. Our simulation data showed that a combined action of blocking of ${I_{text{K1}}}$ and ${I_{text{CaL}}}$ prolonged the cardiac cell action potential duration (APD) and QT interval under SQT3 E299V condition, and demonstrated that blocking of ${I_{text{K1}}}$ and ${I_{text{CaL}}}$ produced a therapeutic effect under SQT3 E299V. This study provides new evidence that blocking of ${I_{text{K1}}}$ and ${I_{text{CaL}}}$ may be a potential treatment for SQTS patients.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125574802","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662826
Javier Saiz-Vivó, Mostafa Abdollahpur, L. Mainardi, V. Corino, M. Melis, F. Sandberg
The aim of this study is to characterize atrial fibrillatory rate (AFR) extracted from a cohort of continuously monitored atrial fibrillation (AF) patients as function of episode duration and onset time. The f-wave signal used to compute the AFR was extracted from a single lead ECG strip of the AF episodes stored in an Implantable Cardiac Monitor (ICM) in a cohort of 99 patients. The f-wave signals were obtained from 1400 AF episodes using a spatiotemporal QRST cancellation process and the AFR was estimated as the fundamental frequency of a model fitted to the extracted f-waves. We studied the relationship between AFR and episode duration and episode onset time, respectively. AFR (median (interquartile range)) was significantly lower (p-value<0.05) in short episodes (<20 min) (5.15 (0.66) Hz) than in longer episodes (5.30 (0.74) Hz). AFR was significantly higher for episodes with onset time at night (00-06) (5.34 (0.82) Hz) than for episodes with onset during the day (10-20) (5.21 (0.70) Hz). Significant differences were also found between the relative AFR (ratio between the AFR and the average AFR of the patient) and episode duration (Short: 99.2 (9.3) %; Long: 100.0 (8.9) %). Data extracted from ICMs shows that that nighttime AF onset and longer duration AF episodes are more common in patients with higher AFR.
{"title":"Atrial Fibrillatory Rate Characterization Extracted from Implanted Cardiac Monitor Data","authors":"Javier Saiz-Vivó, Mostafa Abdollahpur, L. Mainardi, V. Corino, M. Melis, F. Sandberg","doi":"10.23919/cinc53138.2021.9662826","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662826","url":null,"abstract":"The aim of this study is to characterize atrial fibrillatory rate (AFR) extracted from a cohort of continuously monitored atrial fibrillation (AF) patients as function of episode duration and onset time. The f-wave signal used to compute the AFR was extracted from a single lead ECG strip of the AF episodes stored in an Implantable Cardiac Monitor (ICM) in a cohort of 99 patients. The f-wave signals were obtained from 1400 AF episodes using a spatiotemporal QRST cancellation process and the AFR was estimated as the fundamental frequency of a model fitted to the extracted f-waves. We studied the relationship between AFR and episode duration and episode onset time, respectively. AFR (median (interquartile range)) was significantly lower (p-value<0.05) in short episodes (<20 min) (5.15 (0.66) Hz) than in longer episodes (5.30 (0.74) Hz). AFR was significantly higher for episodes with onset time at night (00-06) (5.34 (0.82) Hz) than for episodes with onset during the day (10-20) (5.21 (0.70) Hz). Significant differences were also found between the relative AFR (ratio between the AFR and the average AFR of the patient) and episode duration (Short: 99.2 (9.3) %; Long: 100.0 (8.9) %). Data extracted from ICMs shows that that nighttime AF onset and longer duration AF episodes are more common in patients with higher AFR.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126211188","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662840
Yizhou Liu, Yacong Li, Cunjin Luo, Henggui Zhang
The short QT syndrome (SQTS) is a genetic disease of heart which leads to an increased risk of atrial arrhythmias and sudden cardiac death. Cardiac alternans and high-frequency spiral waves are believed to be strongly associated with atrial arrhythmias. The present study aims to use computational models to investigate the alternans and re-entrant spiral wave in SQTS. The Colman-Zhang (CZ) human atria cell model was implemented for simulation. Newly developed ${I_{text{K1}}}$ formulations describing the wide type (WT) and two SQTS mutations (D172N and E299V) were incorporated. Alternans was studied in rapid heart beat rate conditions, and the S1-S2 protocol was used to initiated re-entrant excitation wave in 2D tissue. Alternans were found in the intracellular calcium concentration ${left[Ca^{2+}right]i}$ traces from basic cycle length (BCL) of 250 to 300 ms, and no alternans was observed in E299V mutation condition. In contrast, alternans emerged when BCL was smaller than 410 ms in WT condition. The minimal spatial lengths of S2 stimulus required to initiate re-entry was reduced by the mutations. Therefore, our findings show that there is a decreased observation of alternans phenomena and the spatial vulnerability to re-entry significantly increased in SQTS conditions.
{"title":"Alternans and 2-D Spiral Wave Dynamics in Human Atria with Short QT Syndrome Variant 3: A Simulation Study","authors":"Yizhou Liu, Yacong Li, Cunjin Luo, Henggui Zhang","doi":"10.23919/cinc53138.2021.9662840","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662840","url":null,"abstract":"The short QT syndrome (SQTS) is a genetic disease of heart which leads to an increased risk of atrial arrhythmias and sudden cardiac death. Cardiac alternans and high-frequency spiral waves are believed to be strongly associated with atrial arrhythmias. The present study aims to use computational models to investigate the alternans and re-entrant spiral wave in SQTS. The Colman-Zhang (CZ) human atria cell model was implemented for simulation. Newly developed ${I_{text{K1}}}$ formulations describing the wide type (WT) and two SQTS mutations (D172N and E299V) were incorporated. Alternans was studied in rapid heart beat rate conditions, and the S1-S2 protocol was used to initiated re-entrant excitation wave in 2D tissue. Alternans were found in the intracellular calcium concentration ${left[Ca^{2+}right]i}$ traces from basic cycle length (BCL) of 250 to 300 ms, and no alternans was observed in E299V mutation condition. In contrast, alternans emerged when BCL was smaller than 410 ms in WT condition. The minimal spatial lengths of S2 stimulus required to initiate re-entry was reduced by the mutations. Therefore, our findings show that there is a decreased observation of alternans phenomena and the spatial vulnerability to re-entry significantly increased in SQTS conditions.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121470804","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 : 2021-09-13DOI: 10.23919/cinc53138.2021.9662918
R. Molero, Carlos Fambuena, A. Climent, M. Guillem
Electrocardiographic imaging (ECGI) allows evaluating the complexity of atrial fibrillation (AF) signals using the Boundary Element Method and Tikhonov regularization. An accurate ECGI reconstruction is dependent on a proper selection of the regularization parameter $(lambda)$. In this work, two ranges of $lambda$ are explored to evaluate the effect of $lambda$ on the quality of the ECGI reconstruction. ECGIs of 20 AF patients were computed using zero (TO), first (T1) and second (T2) order Tikhonov regularization (TR) for two ranges ofv: from 10–9 to 102 and 10–12 to 10–4. Dominant frequencies (DF) and the number of rotors obtained with the two ranges and methods were compared. Zero-order Tikhonov showed to be more robust in $lambda$ selection for different $lambda$ ranges. For lower $lambda$ ranges, higher DF was found $(T2, p < 0.05)$ and more rotors were detected for T1 and $T2(p < 0.01)$. Differences between TR methods compared by $lambda$ ranges showed more variability in derived metrics for lower $lambda$ range $(p < 0.01)$. Optimal ranges for $lambda$ search differ among T0, T1 and T2. Election of lower than optimal $lambda$ values result in an increased estimated electrical complexity.
{"title":"Electrocardiographic Imaging in Atrial Fibrillation: Selection of the Optimal Tikhonov-Regularization Parameter","authors":"R. Molero, Carlos Fambuena, A. Climent, M. Guillem","doi":"10.23919/cinc53138.2021.9662918","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662918","url":null,"abstract":"Electrocardiographic imaging (ECGI) allows evaluating the complexity of atrial fibrillation (AF) signals using the Boundary Element Method and Tikhonov regularization. An accurate ECGI reconstruction is dependent on a proper selection of the regularization parameter <tex>$(lambda)$</tex>. In this work, two ranges of <tex>$lambda$</tex> are explored to evaluate the effect of <tex>$lambda$</tex> on the quality of the ECGI reconstruction. ECGIs of 20 AF patients were computed using zero (TO), first (T1) and second (T2) order Tikhonov regularization (TR) for two ranges ofv: from 10–<sup>9</sup> to 10<sup>2</sup> and 10–<sup>12</sup> to 10–<sup>4</sup>. Dominant frequencies (DF) and the number of rotors obtained with the two ranges and methods were compared. Zero-order Tikhonov showed to be more robust in <tex>$lambda$</tex> selection for different <tex>$lambda$</tex> ranges. For lower <tex>$lambda$</tex> ranges, higher DF was found <tex>$(T2, p < 0.05)$</tex> and more rotors were detected for T1 and <tex>$T2(p < 0.01)$</tex>. Differences between TR methods compared by <tex>$lambda$</tex> ranges showed more variability in derived metrics for lower <tex>$lambda$</tex> range <tex>$(p < 0.01)$</tex>. Optimal ranges for <tex>$lambda$</tex> search differ among T0, T1 and T2. Election of lower than optimal <tex>$lambda$</tex> values result in an increased estimated electrical complexity.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126575982","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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