Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise

Published: 09 July 2024 Publication History

Abstract

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F 2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F 2-scores remain high, yielding F 2 ≥ 0 . 931 as opposed to the best PM F 2 ≥ 0 . 635 across all 64 simulations.

Highlights

DGM obtains F2-scores ≥ 0.931 for 64 rotor simulations of various complexities.
DGM detects more rotors that are closer to the ground truth than PM.
PM suffers from false positives especially in high meandering, fibrosis and noise regimes.
DGM in essence generalises PM by detecting all possible loop shapes and sizes.

References

[1]
Lin Y.-J., Lo M.-T., Chang S.-L., Lo L.-W., Hu Y.-F., Chao T.-F., Chung F.-P., Liao J.-N., Lin C.-Y., Kuo H.-Y., et al., Benefits of atrial substrate modification guided by electrogram similarity and phase mapping techniques to eliminate rotors and focal sources versus conventional defragmentation in persistent atrial fibrillation, JACC: Clin. Electrophysiol. 2 (6) (2016) 667–678. [Online]. Available: https://doi.org/10.1016/j.jacep.2016.08.005.
[2]
Calvo D., Rubín J., Pérez D., Morís C., Ablation of rotor domains effectively modulates dynamics of human: long-standing persistent atrial fibrillation, Circ.: Arrhythm. Electrophysiol. 10 (12) (2017) [Online]. Available: https://doi.org/10.1161/circep.117.005740.
[3]
Morgan R., Colman M.A., Chubb H., Seemann G., Aslanidi O.V., Slow conduction in the border zones of patchy fibrosis stabilizes the drivers for atrial fibrillation: insights from multi-scale human atrial modeling, Front. Physiol. 7 (2016) 474. [Online]. Available: https://doi.org/10.3389/fphys.2016.00474.
[4]
Vigmond E., Pashaei A., Amraoui S., Cochet H., Hassaguerre M., Percolation as a mechanism to explain atrial fractionated electrograms and reentry in a fibrosis model based on imaging data, Heart Rhythm (2016) [Online]. Available: https://doi.org/10.1016/j.hrthm.2016.03.019.
[5]
Zahid S., Cochet H., Boyle P.M., Schwarz E.L., Whyte K.N., Vigmond E.J., Dubois R., Hocini M., Haïssaguerre M., Jaïs P., Trayanova N.A., Patient-derived models link reentrant driver localization in atrial fibrillation to fibrosis spatial pattern, Cardiovasc. Res. (2016) [Online]. Available: https://doi.org/10.1093/cvr/cvw073.
[6]
Sánchez J., Nothstein M., Unger L., Saiz J., Trénor B., Dössel O., Loewe A., Influence of fibrotic tissue arrangement on intracardiac electrograms during persistent atrial fibrillation, in: 2019 Computing in Cardiology, CinC, IEEE, 2019, pp. 1–4. [Online]. Available: https://doi.org/10.22489/CinC.2019.342.
[7]
Sim I., Razeghi O., Karim R., Chubb H., Whitaker J., O’Neill L., Mukherjee R.K., Roney C.H., Razavi R., Wright M., et al., Reproducibility of atrial fibrosis assessment using CMR imaging and an open source platform, JACC: Cardiovasc. Imaging 12 (10) (2019) 2076–2077. [Online]. Available: https://doi.org/10.1016/j.jcmg.2019.03.027.
[8]
Kircher S., Arya A., Altmann D., Rolf S., Bollmann A., Sommer P., Dagres N., Richter S., Breithardt O.-A., Dinov B., et al., Individually tailored vs. standardized substrate modification during radiofrequency catheter ablation for atrial fibrillation: a randomized study, Ep Eur. 20 (11) (2018) 1766–1775. [Online]. Available: https://doi.org/10.1093/europace/eux310.
[9]
Chen J., Arentz T., Cochet H., Müller-Edenborn B., Kim S., Moreno-Weidmann Z., Minners J., Kohl P., Lehrmann H., Allgeier J., et al., Extent and spatial distribution of left atrial arrhythmogenic sites, late gadolinium enhancement at magnetic resonance imaging, and low-voltage areas in patients with persistent atrial fibrillation: comparison of imaging vs. electrical parameters of fibrosis and arrhythmogenesis, EP Eur. 21 (10) (2019) 1484–1493. [Online]. Available: https://doi.org/10.1093/europace/euz159.
[10]
Zhao Y., Dagher L., Huang C., Miller P., Marrouche N.F., Cardiac MRI to manage atrial fibrillation, Arrhythm. Electrophysiol. Rev. 9 (4) (2020) 189. [Online]. Available: https://doi.org/10.15420/aer.2020.21.
[11]
Sánchez J., Luongo G., Nothstein M., Unger L.A., Saiz J., Trenor B., Luik A., Dössel O., Loewe A., Using machine learning to characterize atrial fibrotic substrate from intracardiac signals with a hybrid in silico and in vivo dataset, Front. Physiol. 12 (2021) [Online]. Available: https://doi.org/10.3389/fphys.2021.699291.
[12]
Haissaguerre M., Shah A.J., Cochet H., Hocini M., Dubois R., Efimov I., Vigmond E., Bernus O., Trayanova N., Intermittent drivers anchoring to structural heterogeneities as a major pathophysiological mechanism of human persistent atrial fibrillation, J. Physiol. 594 (9) (2016) 2387–2398. [Online]. Available: https://doi.org/10.1113/jp270617.
[13]
Winfree A.T., Varieties of spiral wave behavior: An experimentalist’s approach to the theory of excitable media, Chaos 1 (3) (1991) 303–334. [Online]. Available: https://doi.org/10.1063/1.165844.
[14]
Gray R.A., Pertsov A.M., Jalife J., Spatial and temporal organization during cardiac fibrillation, Nature 392 (6671) (1998) 75. [Online]. Available: https://doi.org/10.1038/32164.
[15]
Vijayakumar R., Vasireddi S.K., Cuculich P.S., Faddis M.N., Rudy Y., Methodology considerations in phase mapping of human cardiac arrhythmias, Circ.: Arrhythm. Electrophysiol. 9 (11) (2016) [Online]. Available: https://doi.org/10.1161/circep.116.004409.
[16]
Roney C.H., Cantwell C.D., Bayer J.D., Qureshi N.A., Lim P.B., Tweedy J.H., Kanagaratnam P., Peters N.S., Vigmond E.J., Ng F.S., Spatial resolution requirements for accurate identification of drivers of atrial fibrillation, Circ.: Arrhythm. Electrophysiol. 10 (5) (2017) [Online]. Available: https://doi.org/10.1161/CIRCEP.116.004899.
[17]
Martinez-Mateu L., Romero L., Ferrer-Albero A., Sebastian R., Matas J.F.R., Jalife J., Berenfeld O., Saiz J., Factors affecting basket catheter detection of real and phantom rotors in the atria: A computational study, PLoS Comput. Biol. 14 (3) (2018) [Online]. Available: https://doi.org/10.1371/journal.pcbi.1006017.
[18]
Almeida T.P., Nothstein M., Li X., Masè M., Ravelli F., Soriano D.C., Bezerra A.S., Schlindwein F.S., Yoneyama T., Dössel O., et al., Phase singularities in a cardiac patch model with a non-conductive fibrotic area during atrial fibrillation, in: 2020 Computing in Cardiology, IEEE, 2020, pp. 1–4. [Online]. Available: https://doi.org/10.22489/CinC.2020.121.
[19]
Jacquemet V., A statistical model of false negative and false positive detection of phase singularities, Chaos 27 (10) (2017) [Online]. Available: https://doi.org/10.1063/1.4999939.
[20]
Jacquemet V., Phase singularity detection through phase map interpolation: Theory, advantages and limitations, Comput. Biol. Med. 102 (2018) 381–389. [Online]. Available: https://doi.org/10.1016/j.compbiomed.2018.07.014.
[21]
Vandersickel N., Nieuwenhuyse E.V., Cleemput N.V., Goedgebeur J., Haddad M.E., Neve J.D., Demolder A., Strisciuglio T., Duytschaever M., Panfilov A.V., Directed networks as a novel way to describe and analyze cardiac excitation: Directed graph mapping, Front. Physiol. (2019) [Online]. Available: https://doi.org/10.3389/fphys.2019.01138.
[22]
He Y.-J., Li Q.-H., Zhou K., Jiang R., Jiang C., Pan J.-T., Zheng D., Zheng B., Zhang H., Topological charge-density method of identifying phase singularities in cardiac fibrillation, Phys. Rev. E 104 (2021) [Online]. Available: https://doi.org/10.1103/physreve.104.014213.
[23]
Kuklik P., Zeemering S., van Hunnik A., Maesen B., Pison L., Lau D.H., Maessen J., Podziemski P., Meyer C., Schaffer B., Crijns H., Willems S., Schotten U., Identification of rotors during human atrial fibrillation using contact mapping and phase singularity detection: Technical considerations, IEEE Trans. Bio-Med. Eng. 64 (2017) 310–318. [Online]. Available: https://doi.org/10.1109/TBME.2016.2554660.
[24]
Van Nieuwenhuyse E., Teresa S., Guiseppe L., Milad E.H., Goedgebeur Jan V.C.N., Duytschaever Mattias K.S., Nele V., Evaluation of directed graph mapping on complex Atrial Tachycardias, JACC EP (2020) [Online]. Available: https://doi.org/10.1016/j.jacep.2020.12.013.
[25]
Hawson J., Van Nieuwenhuyse E., Van Den Abeele R., Al-Kaisey A., Anderson R.D., Chieng D., Segan L., Watts T., Campbell T., Hendrickx S., et al., Directed graph mapping for ventricular tachycardia: A comparison to established mapping techniques, Clin. Electrophysiol. 9 (7_Part_1) (2023) 907–922. [Online]. Available: https://doi.org/10.1016/j.jacep.2022.08.013.
[26]
openCARP consortium J., Augustin C., Boyle P.M., Colin R., Gsell M., Houillon M., Huang Y.-L.C., Hustad K.G., Karabelas E., Loewe A., Neic A., Nothstein M., Plank G., Prassl A., Sánchez J., Seemann G., Stary T., Thangamani A., Tippmann N., Trevisan Jost T., Vigmond E., Wülfers E.M., openCARP, 2022, [Online]. Available: https://git.opencarp.org/openCARP/openCARP.
[27]
Plank G., Loewe A., Neic A., Augustin C., Huang Y.-L.C., Gsell M., Karabelas E., Nothstein M., Sánchez J., Prassl A., Seemann G., Vigmond E., The openCARP simulation environment for cardiac electrophysiology, Comput. Methods Programs Biomed. 208 (2021) [Online]. Available: https://doi.org/10.1016/j.cmpb.2021.106223.
[28]
Luo C., Rudy Y., A model of the ventricular cardiac action potential. Depolarization, repolarization, and their interaction., Circulation Research 68 (6) (1991) 1501–1526. [Online]. Available: https://doi.org/10.1161/01.res.68.6.1501.
[29]
Bishop M., Plank G., Bidomain ECG simulations using an augmented monodomain model for the cardiac source, IEEE Trans. Biomed. Eng. 58 (8) (2011) 2297–2307. [Online]. Available: https://doi.org/10.1109/TBME.2011.2148718.
[30]
Panfilov A., Dierckx H., Theory of rotors and arrhythmias, in: Zipes D., Jalife J., Stevenson W. (Eds.), Cardiac Electrophysiology : From Cell to Bedside, Elsevier, 2018, pp. 325–334. [Online]. Available: https://doi.org/10.1016/B978-0-323-44733-1.00034-1.
[31]
Ten Tusscher K.H., Panfilov A.V., Influence of diffuse fibrosis on wave propagation in human ventricular tissue, Europace 9 (suppl_6) (2007) vi38–vi45. [Online]. Available: https://doi.org/10.1093/europace/eum206.
[32]
de Jong S., van Veen T.A.B., van Rijen H.V.M., de Bakker J.M.T., Fibrosis and cardiac arrhythmias, J. Cardiovasc. Pharmacol. 57 (6) (2011) 630–638. [Online]. Available: https://doi.org/10.1097/fjc.0b013e318207a35f.
[33]
Kuklik P., Zeemering S., Maesen B., Maessen J., Crijns H.J., Verheule S., Ganesan A.N., Schotten U., Reconstruction of instantaneous phase of unipolar atrial contact electrogram using a concept of sinusoidal recomposition and Hilbert transform, IEEE Trans. Biomed. Eng. 62 (1) (2014) 296–302. [Online]. Available: https://doi.org/10.1109/tbme.2014.2350029.
[34]
Paul T., Moak J.P., Morris C., Garson A. Jr., Epicardial mapping: How to measure local activation?, Pacing Clin. Electrophysiol. 13 (3) (1990) 285–292. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-8159.1990.tb02042.x.
[35]
Clayton R.H., Nash M.P., Analysis of cardiac fibrillation using phase mapping, Card. Electrophysiol. Clin. 7 1 (2015) 49–58. [Online]. Available: https://doi.org/10.1016/j.ccep.2014.11.011.
[36]
Roney C.H., Cantwell C.D., Qureshi N.A., Chowdhury R.A., Dupont E., Lim P.B., Vigmond E.J., Tweedy J.H., Ng F.S., Peters N.S., Rotor tracking using phase of electrograms recorded during atrial fibrillation, Ann. Biomed. Eng. 45 (4) (2016) 910–923. [Online]. Available: https://doi.org/10.1007/s10439-016-1766-4.
[37]
Castells F., Cervigón R., Millet J., On the preprocessing of atrial electrograms in atrial fibrillation: Understanding botteron’s approach, Pacing Clin. Electrophysiol. 37 (2) (2014) 133–143. [Online]. Available: https://doi.org/10.1111/pace.12288.
[38]
Bray M.-A., Lin S.-F., Aliev R.R., Roth B.J., Wikswo J.P., Experimental and theoretical analysis of phase singularity dynamics in cardiac tissue, J. Cardiovasc. Electrophysiol. 12 (6) (2001) 716–722. [Online]. Available: https://doi.org/10.1046/j.1540-8167.2001.00716.x.
[39]
Li X., Almeida T.P., Dastagir N., Guillem M.S., Salinet J., Chu G.S., Stafford P.J., Schlindwein F.S., Ng G.A., Standardizing single-frame phase singularity identification algorithms and parameters in phase mapping during human atrial fibrillation, Front. Physiol. 11 (2020) [Online]. Available: https://doi.org/10.3389/fphys.2020.00869.
[40]
Fagerland M.W., Lydersen S., Laake P., The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional, BMC Med. Res. Methodol. 13 (1) (2013) [Online]. Available: https://doi.org/10.1186/1471-2288-13-91.
[41]
Nieuwenhuyse E.V., Martinez-Mateu L., Saiz J., Panfilov A.V., Vandersickel N., Directed graph mapping exceeds phase mapping in discriminating true and false rotors detected with a basket catheter in a complex in-silico excitation pattern, Comput. Biol. Med. 133 (2021) [Online]. Available: https://doi.org/10.1016/j.compbiomed.2021.104381.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Computers in Biology and Medicine
Computers in Biology and Medicine  Volume 171, Issue C
Mar 2024
1547 pages

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 09 July 2024

Author Tags

  1. DGM
  2. PM
  3. AF
  4. EGM
  5. SNR
  6. LAT
  7. CV
  8. DBSCAN
  9. TP
  10. TN
  11. FP
  12. FN

Author Tags

  1. Directed Graph Mapping
  2. Phase Mapping
  3. Performance comparison
  4. Simulated rotors
  5. Meandering
  6. Fibrosis
  7. Noise

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 30 Aug 2024

Other Metrics

Citations

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media