Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective

Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sha, Qun [verfasserIn] Elliott, Luizetta Zhang, Xiangming Levy, Tzachi Sharma, Tushar Abdelaal, Ahmed

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Atrial fibrillation Electrical drivers Rotational activation Cardiac mapping Mutual information Local efficiency

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: Journal of interventional cardiac electrophysiology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997, 64(2022), 3 vom: 04. Jan., Seite 649-660
Übergeordnetes Werk:	volume:64 ; year:2022 ; number:3 ; day:04 ; month:01 ; pages:649-660

Links:	Volltext

DOI / URN:	10.1007/s10840-021-01101-z

Katalog-ID:	SPR048087084

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100	1		\|a Sha, Qun \|e verfasserin \|0 (orcid)0000-0002-2335-9062 \|4 aut
245	1	0	\|a Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
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520			\|a Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation.
650		4	\|a Atrial fibrillation \|7 (dpeaa)DE-He213
650		4	\|a Electrical drivers \|7 (dpeaa)DE-He213
650		4	\|a Rotational activation \|7 (dpeaa)DE-He213
650		4	\|a Cardiac mapping \|7 (dpeaa)DE-He213
650		4	\|a Mutual information \|7 (dpeaa)DE-He213
650		4	\|a Local efficiency \|7 (dpeaa)DE-He213
700	1		\|a Elliott, Luizetta \|0 (orcid)0000-0002-6411-0239 \|4 aut
700	1		\|a Zhang, Xiangming \|4 aut
700	1		\|a Levy, Tzachi \|4 aut
700	1		\|a Sharma, Tushar \|4 aut
700	1		\|a Abdelaal, Ahmed \|4 aut
773	0	8	\|i Enthalten in \|t Journal of interventional cardiac electrophysiology \|d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 \|g 64(2022), 3 vom: 04. Jan., Seite 649-660 \|w (DE-627)320457869 \|w (DE-600)2006887-6 \|x 1572-8595 \|7 nnns
773	1	8	\|g volume:64 \|g year:2022 \|g number:3 \|g day:04 \|g month:01 \|g pages:649-660
856	4	0	\|u https://dx.doi.org/10.1007/s10840-021-01101-z \|z kostenfrei \|3 Volltext
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912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
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912			\|a GBV_ILN_73
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912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
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912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
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matchkey_str	article:15728595:2022----::tilirlainrvrdniiainhogrgoamtaifraine
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allfields	10.1007/s10840-021-01101-z doi (DE-627)SPR048087084 (SPR)s10840-021-01101-z-e DE-627 ger DE-627 rakwb eng Sha, Qun verfasserin (orcid)0000-0002-2335-9062 aut Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Atrial fibrillation (dpeaa)DE-He213 Electrical drivers (dpeaa)DE-He213 Rotational activation (dpeaa)DE-He213 Cardiac mapping (dpeaa)DE-He213 Mutual information (dpeaa)DE-He213 Local efficiency (dpeaa)DE-He213 Elliott, Luizetta (orcid)0000-0002-6411-0239 aut Zhang, Xiangming aut Levy, Tzachi aut Sharma, Tushar aut Abdelaal, Ahmed aut Enthalten in Journal of interventional cardiac electrophysiology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 64(2022), 3 vom: 04. Jan., Seite 649-660 (DE-627)320457869 (DE-600)2006887-6 1572-8595 nnns volume:64 year:2022 number:3 day:04 month:01 pages:649-660 https://dx.doi.org/10.1007/s10840-021-01101-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 64 2022 3 04 01 649-660
spelling	10.1007/s10840-021-01101-z doi (DE-627)SPR048087084 (SPR)s10840-021-01101-z-e DE-627 ger DE-627 rakwb eng Sha, Qun verfasserin (orcid)0000-0002-2335-9062 aut Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Atrial fibrillation (dpeaa)DE-He213 Electrical drivers (dpeaa)DE-He213 Rotational activation (dpeaa)DE-He213 Cardiac mapping (dpeaa)DE-He213 Mutual information (dpeaa)DE-He213 Local efficiency (dpeaa)DE-He213 Elliott, Luizetta (orcid)0000-0002-6411-0239 aut Zhang, Xiangming aut Levy, Tzachi aut Sharma, Tushar aut Abdelaal, Ahmed aut Enthalten in Journal of interventional cardiac electrophysiology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 64(2022), 3 vom: 04. Jan., Seite 649-660 (DE-627)320457869 (DE-600)2006887-6 1572-8595 nnns volume:64 year:2022 number:3 day:04 month:01 pages:649-660 https://dx.doi.org/10.1007/s10840-021-01101-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 64 2022 3 04 01 649-660
allfields_unstemmed	10.1007/s10840-021-01101-z doi (DE-627)SPR048087084 (SPR)s10840-021-01101-z-e DE-627 ger DE-627 rakwb eng Sha, Qun verfasserin (orcid)0000-0002-2335-9062 aut Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Atrial fibrillation (dpeaa)DE-He213 Electrical drivers (dpeaa)DE-He213 Rotational activation (dpeaa)DE-He213 Cardiac mapping (dpeaa)DE-He213 Mutual information (dpeaa)DE-He213 Local efficiency (dpeaa)DE-He213 Elliott, Luizetta (orcid)0000-0002-6411-0239 aut Zhang, Xiangming aut Levy, Tzachi aut Sharma, Tushar aut Abdelaal, Ahmed aut Enthalten in Journal of interventional cardiac electrophysiology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 64(2022), 3 vom: 04. Jan., Seite 649-660 (DE-627)320457869 (DE-600)2006887-6 1572-8595 nnns volume:64 year:2022 number:3 day:04 month:01 pages:649-660 https://dx.doi.org/10.1007/s10840-021-01101-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 64 2022 3 04 01 649-660
allfieldsGer	10.1007/s10840-021-01101-z doi (DE-627)SPR048087084 (SPR)s10840-021-01101-z-e DE-627 ger DE-627 rakwb eng Sha, Qun verfasserin (orcid)0000-0002-2335-9062 aut Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Atrial fibrillation (dpeaa)DE-He213 Electrical drivers (dpeaa)DE-He213 Rotational activation (dpeaa)DE-He213 Cardiac mapping (dpeaa)DE-He213 Mutual information (dpeaa)DE-He213 Local efficiency (dpeaa)DE-He213 Elliott, Luizetta (orcid)0000-0002-6411-0239 aut Zhang, Xiangming aut Levy, Tzachi aut Sharma, Tushar aut Abdelaal, Ahmed aut Enthalten in Journal of interventional cardiac electrophysiology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 64(2022), 3 vom: 04. Jan., Seite 649-660 (DE-627)320457869 (DE-600)2006887-6 1572-8595 nnns volume:64 year:2022 number:3 day:04 month:01 pages:649-660 https://dx.doi.org/10.1007/s10840-021-01101-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 64 2022 3 04 01 649-660
allfieldsSound	10.1007/s10840-021-01101-z doi (DE-627)SPR048087084 (SPR)s10840-021-01101-z-e DE-627 ger DE-627 rakwb eng Sha, Qun verfasserin (orcid)0000-0002-2335-9062 aut Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. Atrial fibrillation (dpeaa)DE-He213 Electrical drivers (dpeaa)DE-He213 Rotational activation (dpeaa)DE-He213 Cardiac mapping (dpeaa)DE-He213 Mutual information (dpeaa)DE-He213 Local efficiency (dpeaa)DE-He213 Elliott, Luizetta (orcid)0000-0002-6411-0239 aut Zhang, Xiangming aut Levy, Tzachi aut Sharma, Tushar aut Abdelaal, Ahmed aut Enthalten in Journal of interventional cardiac electrophysiology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 64(2022), 3 vom: 04. Jan., Seite 649-660 (DE-627)320457869 (DE-600)2006887-6 1572-8595 nnns volume:64 year:2022 number:3 day:04 month:01 pages:649-660 https://dx.doi.org/10.1007/s10840-021-01101-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 64 2022 3 04 01 649-660
language	English
source	Enthalten in Journal of interventional cardiac electrophysiology 64(2022), 3 vom: 04. Jan., Seite 649-660 volume:64 year:2022 number:3 day:04 month:01 pages:649-660
sourceStr	Enthalten in Journal of interventional cardiac electrophysiology 64(2022), 3 vom: 04. Jan., Seite 649-660 volume:64 year:2022 number:3 day:04 month:01 pages:649-660
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Atrial fibrillation Electrical drivers Rotational activation Cardiac mapping Mutual information Local efficiency
isfreeaccess_bool	true
container_title	Journal of interventional cardiac electrophysiology
authorswithroles_txt_mv	Sha, Qun @@aut@@ Elliott, Luizetta @@aut@@ Zhang, Xiangming @@aut@@ Levy, Tzachi @@aut@@ Sharma, Tushar @@aut@@ Abdelaal, Ahmed @@aut@@
publishDateDaySort_date	2022-01-04T00:00:00Z
hierarchy_top_id	320457869
id	SPR048087084
language_de	englisch
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We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. 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author	Sha, Qun
spellingShingle	Sha, Qun misc Atrial fibrillation misc Electrical drivers misc Rotational activation misc Cardiac mapping misc Mutual information misc Local efficiency Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
authorStr	Sha, Qun
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topic_title	Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective Atrial fibrillation (dpeaa)DE-He213 Electrical drivers (dpeaa)DE-He213 Rotational activation (dpeaa)DE-He213 Cardiac mapping (dpeaa)DE-He213 Mutual information (dpeaa)DE-He213 Local efficiency (dpeaa)DE-He213
topic	misc Atrial fibrillation misc Electrical drivers misc Rotational activation misc Cardiac mapping misc Mutual information misc Local efficiency
topic_unstemmed	misc Atrial fibrillation misc Electrical drivers misc Rotational activation misc Cardiac mapping misc Mutual information misc Local efficiency
topic_browse	misc Atrial fibrillation misc Electrical drivers misc Rotational activation misc Cardiac mapping misc Mutual information misc Local efficiency
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title	Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
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title_full	Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
author_sort	Sha, Qun
journal	Journal of interventional cardiac electrophysiology
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author_browse	Sha, Qun Elliott, Luizetta Zhang, Xiangming Levy, Tzachi Sharma, Tushar Abdelaal, Ahmed
container_volume	64
format_se	Elektronische Aufsätze
author-letter	Sha, Qun
doi_str_mv	10.1007/s10840-021-01101-z
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title_sort	atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
title_auth	Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
abstract	Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. © The Author(s) 2021
abstractGer	Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. © The Author(s) 2021
abstract_unstemmed	Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. © The Author(s) 2021
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container_issue	3
title_short	Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
url	https://dx.doi.org/10.1007/s10840-021-01101-z
remote_bool	true
author2	Elliott, Luizetta Zhang, Xiangming Levy, Tzachi Sharma, Tushar Abdelaal, Ahmed
author2Str	Elliott, Luizetta Zhang, Xiangming Levy, Tzachi Sharma, Tushar Abdelaal, Ahmed
ppnlink	320457869
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1007/s10840-021-01101-z
up_date	2024-07-03T16:55:04.462Z
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We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × $ 10^{−4} $ vs. 0.0032 ± 1 × $ 10^{−4} $, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × $ 10^{−5} $ vs. 0.0014 ± 4 × $ 10^{−5} $, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × $ 10^{−5} $ vs. 0.0019 ± 6 × $ 10^{−5} $, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × $ 10^{−5} $ vs. 0.0016 ± 4 × $ 10^{−5} $, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. 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Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective

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