Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patterns Article Swipe
Laura I. Tanner
,
Anna Busatto
,
Jake Bergquist
,
Wilson Good
,
Brian Zenger
,
Gernot Plank
,
Akil Narayan
,
Karli Gillette
,
Rob MacLeod
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1113/jp287746
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1113/jp287746
Predictive models and computational simulations of cardiac electrophysiology depend on precise anatomical representations, including the local myocardial fibre structure. However, obtaining patient‐specific fibre information is challenging. In addition, the influence of physiological variability in fibre orientation on cardiac activation simulations is poorly understood. We implemented rule‐based algorithms to generate fibres and robust uncertainty quantification methods to determine model output variability with respect to ventricular activation sequences. We used polynomial chaos, which reduces computational demands by using an emulator to approximate the underlying forward model. Our study examined activation sequences in response to nine stimuli and five metrics quantifying essential features of the activation sequence. The results indicated that the primary fibre orientation impacts the overall spread of activation, which could impact more complex patterns of activation; however, there is minimal impact on the location of discrete activation features, such as breakthrough sites. For free wall stimuli, the standard deviation (STD) was highest near the stimulus site, diminishing with distance. Apical stimuli showed complementary STD patterns, with epicardial pacing maximizing STD in the right basal area and endocardial pacing in the left. Ventricular junction stimuli exhibited symmetrical STD patterns, low near the stimulus but increasing sharply towards the apex, peaking on the left in the apical region. Furthermore, variability in the imbrication or helix angle did not impact the activation sequences. We conclude that in many relevant modelling contexts, the variability in myocardial fibre orientation can play an important role in the resulting activation sequences and should be accounted for. image Key points The primary fibre orientation has modest impact on activation duration and location of most discrete activation features for ectopic stimuli, but introduces variability in activation sequence. For free wall stimuli, the standard deviation (STD) was highest on the stimulated surface, indicating that deviations are largest in early activation and diminish as activation reaches remote heart regions. For apical stimuli, activation patterns were insensitive to fibre orientation variations, but the STD had complementary maxima, strongly dependent on pacing surface. Epicardial pacing produced largest STD in right basal area while endocardial pacing affected left basal area, indicating strong dependence on fibre structure. For stimuli at both anterior and posterior ventricular junctions, STD patterns were symmetrical, with low values near stimulus and sharp increases towards apex, peaking in left apical region. The helical fibre orientation showed no relevant fluctuations in activation sequence.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1113/jp287746
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1113/JP287746
- OA Status
- hybrid
- Cited By
- 1
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413450215
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- OpenAlex ID
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https://openalex.org/W4413450215Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1113/jp287746Digital Object Identifier
- Title
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Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patternsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-08-22Full publication date if available
- Authors
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Laura I. Tanner, Anna Busatto, Jake Bergquist, Wilson Good, Brian Zenger, Gernot Plank, Akil Narayan, Karli Gillette, Rob MacLeodList of authors in order
- Landing page
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https://doi.org/10.1113/jp287746Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1113/JP287746Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1113/JP287746Direct OA link when available
- Concepts
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Polynomial chaos, Orientation (vector space), Cardiology, Polynomial, CHAOS (operating system), Internal medicine, Mathematics, Medicine, Computer science, Mathematical analysis, Statistics, Geometry, Computer security, Monte Carlo methodTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.indicating | 293, 347 |
| abstract_inverted_index.introduces | 274 |
| abstract_inverted_index.junctions, | 361 |
| abstract_inverted_index.maximizing | 169 |
| abstract_inverted_index.myocardial | 17, 232 |
| abstract_inverted_index.polynomial | 69 |
| abstract_inverted_index.sequences. | 66, 220 |
| abstract_inverted_index.stimulated | 291 |
| abstract_inverted_index.structure. | 19, 352 |
| abstract_inverted_index.underlying | 82 |
| abstract_inverted_index.Ventricular | 182 |
| abstract_inverted_index.activation, | 118 |
| abstract_inverted_index.activation; | 126 |
| abstract_inverted_index.approximate | 80 |
| abstract_inverted_index.diminishing | 157 |
| abstract_inverted_index.endocardial | 177, 341 |
| abstract_inverted_index.imbrication | 211 |
| abstract_inverted_index.implemented | 45 |
| abstract_inverted_index.information | 24 |
| abstract_inverted_index.insensitive | 315 |
| abstract_inverted_index.orientation | 36, 112, 234, 256, 318, 384 |
| abstract_inverted_index.quantifying | 98 |
| abstract_inverted_index.simulations | 5, 40 |
| abstract_inverted_index.symmetrical | 186 |
| abstract_inverted_index.uncertainty | 53 |
| abstract_inverted_index.understood. | 43 |
| abstract_inverted_index.variability | 33, 60, 208, 230, 275 |
| abstract_inverted_index.variations, | 319 |
| abstract_inverted_index.ventricular | 64, 360 |
| abstract_inverted_index.Furthermore, | 207 |
| abstract_inverted_index.breakthrough | 141 |
| abstract_inverted_index.challenging. | 26 |
| abstract_inverted_index.fluctuations | 388 |
| abstract_inverted_index.rule‐based | 46 |
| abstract_inverted_index.symmetrical, | 365 |
| abstract_inverted_index.complementary | 163, 324 |
| abstract_inverted_index.computational | 4, 73 |
| abstract_inverted_index.physiological | 32 |
| abstract_inverted_index.quantification | 54 |
| abstract_inverted_index.representations, | 13 |
| abstract_inverted_index.electrophysiology | 8 |
| abstract_inverted_index.patient‐specific | 22 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5013341304 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I223532165 |
| citation_normalized_percentile.value | 0.91079371 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |