ArticleIEEE transactions on bio-medical engineering2018
A Potential-Based Inverse Spectral Method to Noninvasively Localize Discordant Distributions of Alternans on the Heart From the ECG.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Interpretable manifold learning for T-wave alternans assessment with electrocardiographic imaging.Engineering applications of artificial intelligence · 2025Article
- Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-wave Alternans.Sensors (Basel, Switzerland) · 2020Article
- A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models.Computing in cardiology · 2018Article
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3 authors.
Funding
Abstract
T-wave alternans (TWA), defined as the beat-to-beat alternation in amplitude of the T-waves, has been shown to be linked to ventricular fibrillation (VF). However, current TWA tests have high sensitivity but low specificity in determining who is at risk. To overcome this limitation, it might be helpful to determine the spatial distribution of any regions on the heart that alternate in opposite phase. Understanding these spatial distributions in relation to the regular activation of the heart could help explain the mechanism for the genesis of VF and thus disambiguate the low specificity of TWA. GOAL: Image the spatial distribution of TWA on the heart surface from ECG measurements.
methodsWe introduced the inverse spectral method (ISM), a tailored inverse (or ElectroCardioGraphic Imaging) solution designed specifically to noninvasively image cases of TWA on the heart.
resultsWe evaluate the ISM on its capacity to reliably detect the spatial distributions of TWA compared against a standard TWA detection method applied directly to the electrograms on the heart surface. We report on results from both a series of synthetic simulations of TWA generated using the ECGSIM software and a set of continuous epicardial surface voltage recordings from a canine experiment. ISM detected TWA distributions that matched the phase of the true underlying out-of-phase regions over and of the heart surface, respectively.
conclusionOur results suggest that ISM is capable of reliably detecting the different regions present in a TWA distribution across a wide variety of TWA locations on the heart in simulation and in the face of transients and nonidealities in the canine recordings.
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