| 1. |
- Bachi, Lorenzo, et al.
(författare)
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ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions
- 2023
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 70:12, s. 3449-3460
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Tidskriftsartikel (refereegranskat)abstract
- The present paper proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance.
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| 2. |
- Barquero-Perez, Oscar, et al.
(författare)
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On the influence of heart rate and coupling interval prematurity on heart rate turbulence
- 2017
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Ingår i: IEEE Transactions on Biomedical Engineering. - 1558-2531. ; 64:2, s. 302-309
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Heart rate turbulence (HRT) has been successfully explored for cardiac risk stratification. While HRT is known to be influenced by the heart rate (HR) and the coupling interval (CI), nonconcordant results have been reported on how the CI influences HRT. The purpose of this study is to investigate HRT changes in terms of CI and HR by means of an especially designed protocol. Methods: A dataset was acquired from 11 patients with structurally normal hearts for which CI was altered by different pacing trains and HR by isoproterenol during electrophysiological study (EPS). The protocol was designed so that, first, the effect of HR changes on HRT and, second, the combined effect of HR and CI could be explored. As a complement to the EPS dataset, a database of 24-h Holters from 61 acute myocardial infarction (AMI) patients was studied for the purpose of assessing risk. Data analysis was performed by using different nonlinear ridge regression models, and the relevance of model variables was assessed using resampling methods. The EPS subjects, with and without isoproterenol, were analyzed separately. Results: The proposed nonlinear regression models were found to account for the influence of HR and CI on HRT, both in patients undergoing EPS without isoproterenol and in low-risk AMI patients, whereas this influence was absent in high-risk AMI patients. Moreover, model coefficients related to CI were not statistically significant, p > 0.05, on EPS subjects with isoproterenol. Conclusion: The observed relationship between CI and HRT, being in agreement with the baroreflex hypothesis, was statistically significant (p < 0.05), when decoupling the effect of HR and normalizing the CI by the HR. Significance: The results of this study can help to provide new risk indicators that take into account physiological influence on HRT, as well as to model how this influence changes in different cardiac conditions.
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| 3. |
- Behjat, Hamid, et al.
(författare)
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Domain-Informed Spline Interpolation
- 2019
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Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 67:15, s. 3909-3921
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Tidskriftsartikel (refereegranskat)abstract
- Standard interpolation techniques are implicitly based on the assumption that the signal lies on a single homogeneous domain. In contrast, many naturally occurring signals lie on an inhomogeneous domain, such as brain activity associated to different brain tissue. We propose an interpolation method that instead exploits prior information about domain inhomogeneity, characterized by different, potentially overlapping, subdomains. As proof of concept, the focus is put on extending conventional shift-invariant B-spline interpolation. Given a known inhomogeneous domain, B-spline interpolation of a given order is extended to a domain-informed, shift-variant interpolation. This is done by constructing a domain-informed generating basis that satisfies stability properties. We illustrate example constructions of domain-informed generating basis and show their property in increasing the coherence between the generating basis and the given inhomogeneous domain. By advantageously exploiting domain knowledge, we demonstrate the benefit of domain-informed interpolation over standard B-spline interpolation through Monte Carlo simulations across a range of B-spline orders. We also demonstrate the feasibility of domain-informed interpolation in a neuroimaging application where the domain information is available by a complementary image contrast. The results show the benefit of incorporating domain knowledge so that an interpolant consistent to the anatomy of the brain is obtained.
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| 4. |
- Ben-Moshe, Noam, et al.
(författare)
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RawECGNet : Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG
- 2024
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Ingår i: IEEE Journal of Biomedical and Health Informatics. - 2168-2194. ; , s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Results: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91–0.94 in RBDB and 0.93 in SHDB, compared to 0.89–0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.
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| 5. |
- Biton, Shany, et al.
(författare)
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Estimation of f-wave Dominant Frequency Using a Voting Scheme
- 2022
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Ingår i: Computing in Cardiology, CinC 2022. - 9798350300970
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Konferensbidrag (refereegranskat)abstract
- Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF as the single input feature of the RF model. Results: Performance on the test set, expressed in terms of AF/non-AF classification, was the best when the DAF was computed computed the three best-performing extraction methods. Using these three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the DAFs were mostly spread around 6 Hz, 5.66 (4.83-7.47). Conclusions: This study has two novel contributions: (1) a method for assessing the performance of f-wave extraction algorithms, and (2) a voting scheme for improved DAF estimation.
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| 6. |
- Butkuviene, Monika, et al.
(författare)
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Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance
- 2021
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Ingår i: 2021 Computing in Cardiology, CinC 2021. - 2325-8861 .- 2325-887X. - 9781665479165 ; 2021-September
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Konferensbidrag (refereegranskat)abstract
- Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured.
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| 7. |
- Butkuviene, Monika, et al.
(författare)
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Characterization of Atrial Fibrillation Episode Patterns : A Comparative Study
- 2024
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 71:1, s. 106-113
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. Methods: To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality. Results: The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability. Conclusions: Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.
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| 8. |
- Butkuviene, Monika, et al.
(författare)
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Considerations on Performance Evaluation of Atrial Fibrillation Detectors
- 2021
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 68:11, s. 3250-3260
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Tidskriftsartikel (refereegranskat)abstract
- Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.
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| 9. |
- Corino, Valentina D.A., et al.
(författare)
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Non-invasive evaluation of the effect of metoprolol on the atrioventricular node during permanent atrial fibrillation
- 2014. - January
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Ingår i: Computing in Cardiology 2014. - : Oxford University Press (OUP). - 2325-8861. - 9781479943463 - 9781479943470 ; 41, s. 889-892
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Konferensbidrag (refereegranskat)abstract
- The aim of this study was to evaluate changes in AV nodal properties during administration of metoprolol, using a novel ECG-based method for parameter estimation. The AV nodal parameters account for the probability of an impulse not passing through the fast pathway, the absolute refractory periods of the slow and fast pathways (aRPs and aRPf), representing the functional refractory period, and related prolongation in the respective refractory periods. Twenty patients (age 71±8 years, 14 men) with permanent AF from the RATe control in Atrial Fibrillation (RATAF) database were included in this study. Recordings during baseline and metoprolol administration were analyzed. Furthermore, simulated RR series were generated mimicking metoprolol administration. During metoprolol administration, aRP was significantly prolonged in both pathways (aRPs: 342±39 vs. 408±81 ms, p<0.001; aRPf: 432±74 vs. 527±83 ms, p<0.001). Similar results were found for the simulated RR series: both aRPs and aRPf were significantly prolonged with metoprolol. The AV nodal parameters reflect expected changes after metoprolol administration, i.e., a prolongation in functional refractory period. The simulations suggest that aRP may serve as an estimate of the functional refractory period.
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| 10. |
- Corino, Valentina D A, et al.
(författare)
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Statistical modeling of atrioventricular nodal function during atrial fibrillation : An update
- 2013
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Ingår i: BIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. - 9789898565365 ; , s. 25-29
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a number of advancements of our recently proposed model of atrioventricular (AV) node function during atrial fibrillation (AF). The model is defined by parameters characterizing the arrival rate of atrial impulses, the probability of an impulse choosing either one of the two AV nodal pathways, the refractory periods of these pathways, and their prolongation. In the updated model, the characterization of AV nodal pathways is made more detailed and the number of pathways is determined by the Bayesian information criterion. The performance is evaluated on ECG data acquired from twenty-five AF patients during rest and head-up tilt test. The results show that the refined AV node model provides significantly better fit than did the original model. During tilt, the AF frequency increased (6.25 ±0.58 Hz vs. 6.32 ±0.61 Hz, p < 0.05, rest vs. tilt) and the prolongation of the refractory periods decreased for both pathways (slow pathway: 0.23 ±0.20 s vs. 0.11 ±0.10 s, p < 0.001, rest vs. tilt; fast pathway: 0.24±0.31 s vs. 0.16±0.19 s, p < 0.05, rest vs. tilt). These results show that AV node characteristics can be assessed noninvasively for the purpose of quantifying changes induced by autonomic stimulation.
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