| 1. |
- Barquero-Perez, Oscar, et al.
(author)
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On the influence of heart rate and coupling interval prematurity on heart rate turbulence
- 2017
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In: IEEE Transactions on Biomedical Engineering. - 1558-2531. ; 64:2, s. 302-309
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Journal article (peer-reviewed)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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| 2. |
- Behjat, Hamid, et al.
(author)
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Domain-Informed Spline Interpolation
- 2019
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In: IEEE Transactions on Signal Processing. - 1053-587X. ; 67:15, s. 3909-3921
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Journal article (peer-reviewed)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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| 3. |
- Henriksson, Mikael, et al.
(author)
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Model-based Assessment of f-Wave Signal Quality in Patients with Atrial Fibrillation
- 2018
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In: IEEE Transactions on Biomedical Engineering. - 1558-2531. ; 65:11, s. 2600-2611
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Journal article (peer-reviewed)abstract
- Objective: The detection and analysis of atrial fibrillation (AF) in the ECG is greatly influenced by signal quality. The present study proposes and evaluates a model-based f-wave signal quality index (SQI), denoted S, for use in the QRST-cancelled ECG signal. Methods: S is computed using a harmonic f-wave model, allowing for variation in frequency and amplitude. The properties of S are evaluated on both f-waves and P-waves using 378 12-lead ECGs, 1875 single-lead ECGs, and simulated signals. Results: S decreases monotonically when noise is added to f-wave signals, even for noise which overlaps spectrally with f-waves. Moreover, S is shown to be closely associated with the accuracy of AF frequency estimation, where S>0.3 implies accurate estimation. When S is used as a measure of f-wave presence, AF detection performance improves: the sensitivity increases from 97.0% to 98.1% and the specificity increases from 97.4% to 97.8% when compared to the reference detector. Conclusion: The proposed SQI represents a novel approach to assessing f-wave signal quality, as well as to determining whether f-waves are present. Significance: The use of S improves the detection of AF and benefits the analysis of noisy ECGs.
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| 4. |
- Simaityte, Monika, et al.
(author)
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Quantitative Evaluation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation
- 2018
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In: Computing in Cardiology Conference, CinC 2018. - 9781728109589 ; 45
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Conference paper (peer-reviewed)abstract
- Flow velocity in left atrial appendage decreases when paroxysmal atrial fibrillation (PAF) progresses to longer episodes, suggesting that the temporal PAF episode pattern may be related to risk of thrombus formation. This study investigates the feasibility of discriminating episode patterns based on two descriptors: the aggregation characterizes the temporal distribution of PAF episodes, whereas the Gini coefficient characterizes differences in episode duration. The descriptors were studied on three PhysioNet databases with annotated PAF episodes, resulting in a total of 102 recordings. Three types of patterns were defined: congregation of several episodes in a single and multiple clusters, and episodes dispersed over the entire monitoring period. The results show that the aggregation descriptor achieves large values for patterns with a single and multiple clusters (0.76± 0.07 and 0.60± 0.08, respectively). In contrast, much lower values are obtained for dispersed episode patterns (0.10± 0.05). The Gini coefficient is better suited for discriminating among the patterns with high PAF burden and, therefore, represents a descriptor which is complementary to aggregation. Both descriptors may have relevance when studying the relationship between episode pattern and the risk of thrombus formation.
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