Literature DB >> 20410046

12-lead surface electrocardiogram reconstruction from implanted device electrograms.

G Stuart Mendenhall1, Samir Saba.   

Abstract

AIM: Reconstruction of the surface electrocardiogram (EKG) from voltage recordings from implanted leads is not performed by current pacemakers or cardioverter-defibrillators. We investigated the feasibility and accuracy of reconstruction of a full 12-lead surface EKG from an implanted biventricular device. METHODS AND
RESULTS: We applied three techniques for surface EKG reconstruction from multiple intracardiac (IC) vector recordings from implanted cardiac leads: single fixed dipole modelling via exact solution, exhaustive best-fit solution, and time-independent association using a transfer matrix. Recordings were performed at biventricular generator change in 10 patients. Overdetermined projection transformation resulted in high fidelity surface EKG reproduction for left-sided implanted devices (correlation coefficient 0.84+/-0.13) with computationally lightweight reconstruction.
CONCLUSION: After individual post-implantation correlation with the surface EKG, reconstruction using a time-independent transfer matrix accurately reproduces the surface EKG, is free from gating requirements, and retains validity during aberrant depolarization. These findings have significant implications for further study relating IC electrogram to surface tracings. The techniques may be used for real-time or remote monitoring and diagnosis of rhythm disturbances, cardiac ischaemia, and lead integrity and stability.

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Year:  2010        PMID: 20410046     DOI: 10.1093/europace/euq115

Source DB:  PubMed          Journal:  Europace        ISSN: 1099-5129            Impact factor:   5.214


  3 in total

1.  Surface electrocardiogram reconstruction from intracardiac electrograms using a dynamic time delay artificial neural network.

Authors:  Fabienne Porée; Amar Kachenoura; Guy Carrault; Renzo Dal Molin; Philippe Mabo; Alfredo I Hernandez
Journal:  IEEE Trans Biomed Eng       Date:  2012-10-18       Impact factor: 4.538

2.  RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms.

Authors:  Yongan Zhang; Anton Banta; Yonggan Fu; Mathews M John; Allison Post; Mehdi Razavi; Joseph Cavallaro; Behnaam Aazhang; Yingyan Lin
Journal:  ACM J Emerg Technol Comput Syst       Date:  2022-03-16       Impact factor: 2.013

3.  A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa.

Authors:  Anton Banta; Romain Cosentino; Mathews M John; Allison Post; Skylar Buchan; Mehdi Razavi; Behnaam Aazhang
Journal:  Artif Intell Med       Date:  2021-07-16       Impact factor: 7.011

  3 in total

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