Dataset Information

Intrinsically stretchable electrode array enabled in vivo electrophysiological mapping of atrial fibrillation at cellular resolution.

ABSTRACT: Electrophysiological mapping of chronic atrial fibrillation (AF) at high throughput and high resolution is critical for understanding its underlying mechanism and guiding definitive treatment such as cardiac ablation, but current electrophysiological tools are limited by either low spatial resolution or electromechanical uncoupling of the beating heart. To overcome this limitation, we herein introduce a scalable method for fabricating a tissue-like, high-density, fully elastic electrode (elastrode) array capable of achieving real-time, stable, cellular level-resolution electrophysiological mapping in vivo. Testing with acute rabbit and porcine models, the device is proven to have robust and intimate tissue coupling while maintaining its chemical, mechanical, and electrical properties during the cardiac cycle. The elastrode array records epicardial atrial signals with comparable efficacy to currently available endocardial-mapping techniques but with 2 times higher atrial-to-ventricular signal ratio and >100 times higher spatial resolution and can reliably identify electrical local heterogeneity within an area of simultaneously identified rotor-like electrical patterns in a porcine model of chronic AF.

SUBMITTER: Liu J

PROVIDER: S-EPMC7334471 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Intrinsically stretchable electrode array enabled in vivo electrophysiological mapping of atrial fibrillation at cellular resolution.

Liu Jia J Zhang Xinyuan X Liu Yuxin Y Rodrigo Miguel M Loftus Patrick D PD Aparicio-Valenzuela Joy J Zheng Jukuan J Pong Terrence T Cyr Kevin J KJ Babakhanian Meghedi M Hasi Jasmine J Li Jinxing J Jiang Yuanwen Y Kenney Christopher J CJ Wang Paul J PJ Lee Anson M AM Bao Zhenan Z

Proceedings of the National Academy of Sciences of the United States of America 20200615 26

Electrophysiological mapping of chronic atrial fibrillation (AF) at high throughput and high resolution is critical for understanding its underlying mechanism and guiding definitive treatment such as cardiac ablation, but current electrophysiological tools are limited by either low spatial resolution or electromechanical uncoupling of the beating heart. To overcome this limitation, we herein introduce a scalable method for fabricating a tissue-like, high-density, fully elastic electrode (elastro ...[more]

PMID: 32541030

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Project description:BackgroundAtrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.MethodsTemporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm2 resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning were extracted from each Fourier spectrum.ResultsTargeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P<0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together).ConclusionsThe machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Dataset Information

Intrinsically stretchable electrode array enabled in vivo electrophysiological mapping of atrial fibrillation at cellular resolution.

Publications

Intrinsically stretchable electrode array enabled in vivo electrophysiological mapping of atrial fibrillation at cellular resolution.

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