Interpolating Low Amplitude ECG Signals Combined with Filtering According to International Standards Improves Inverse Reconstruction of Cardiac Electrical Activity

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Abstract

In this paper, the effect of reducing noise from ECG signals is investigated by applying filters in compliance with the standards for ECG devices, removing and interpolating low amplitude signals. Torso-tank experiment data was used with electrical activity recorded simultaneously from 128 tank electrodes and 108 epicardial sock electrodes. Subsequently, 10 representative beats were selected for analysis. Tikhonov zero-order regularization method was used to solve the inverse problem for the following groups; raw fullset, filtered fullset, raw low amplitude removed, filtered low amplitude removed, raw low amplitude interpolated, filtered low amplitude interpolated torso signals. Pearson’s correlation was used for comparison between measured and computed electrograms and between activation maps derived from them. Filtering the signal according to the standards improved the reconstructed electrograms. In addition, removal of low amplitude signals and replacing them with interpolated signals combined with filtering according to the standard significantly improved the reconstructed electrograms and derived activation maps.

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Electrocardiography
Cardiac
Filtering
Chemical activation
Electrodes
Inverse problems
Electrode
Activation
Beat
Regularization Method
Compliance
Standards
Electrocardiogram
Inverse Problem
Experiments
Filter
Zero
Experiment

Keywords

  • signal processing
  • ECGs

Cite this

@article{2e3a391cb8b6441a95b863f90ec95d3a,
title = "Interpolating Low Amplitude ECG Signals Combined with Filtering According to International Standards Improves Inverse Reconstruction of Cardiac Electrical Activity",
abstract = "In this paper, the effect of reducing noise from ECG signals is investigated by applying filters in compliance with the standards for ECG devices, removing and interpolating low amplitude signals. Torso-tank experiment data was used with electrical activity recorded simultaneously from 128 tank electrodes and 108 epicardial sock electrodes. Subsequently, 10 representative beats were selected for analysis. Tikhonov zero-order regularization method was used to solve the inverse problem for the following groups; raw fullset, filtered fullset, raw low amplitude removed, filtered low amplitude removed, raw low amplitude interpolated, filtered low amplitude interpolated torso signals. Pearson’s correlation was used for comparison between measured and computed electrograms and between activation maps derived from them. Filtering the signal according to the standards improved the reconstructed electrograms. In addition, removal of low amplitude signals and replacing them with interpolated signals combined with filtering according to the standard significantly improved the reconstructed electrograms and derived activation maps.",
keywords = "signal processing, ECGs",
author = "Ali Rababah and D Finlay and Laura Bear and RR Bond and Khaled Rjoob and James McLaughlin",
note = "Part of the Lecture Notes in Computer Science book series (LNCS, volume 11504). ISSN 0302-9743",
year = "2019",
month = "5",
day = "30",
doi = "10.1007/978-3-030-21949-9_13",
language = "English",
volume = "11504",
pages = "112--120",
journal = "Lecture Notes in Artificial Intelligence",
issn = "0302-9743",

}

TY - JOUR

T1 - Interpolating Low Amplitude ECG Signals Combined with Filtering According to International Standards Improves Inverse Reconstruction of Cardiac Electrical Activity

AU - Rababah, Ali

AU - Finlay, D

AU - Bear, Laura

AU - Bond, RR

AU - Rjoob, Khaled

AU - McLaughlin, James

N1 - Part of the Lecture Notes in Computer Science book series (LNCS, volume 11504). ISSN 0302-9743

PY - 2019/5/30

Y1 - 2019/5/30

N2 - In this paper, the effect of reducing noise from ECG signals is investigated by applying filters in compliance with the standards for ECG devices, removing and interpolating low amplitude signals. Torso-tank experiment data was used with electrical activity recorded simultaneously from 128 tank electrodes and 108 epicardial sock electrodes. Subsequently, 10 representative beats were selected for analysis. Tikhonov zero-order regularization method was used to solve the inverse problem for the following groups; raw fullset, filtered fullset, raw low amplitude removed, filtered low amplitude removed, raw low amplitude interpolated, filtered low amplitude interpolated torso signals. Pearson’s correlation was used for comparison between measured and computed electrograms and between activation maps derived from them. Filtering the signal according to the standards improved the reconstructed electrograms. In addition, removal of low amplitude signals and replacing them with interpolated signals combined with filtering according to the standard significantly improved the reconstructed electrograms and derived activation maps.

AB - In this paper, the effect of reducing noise from ECG signals is investigated by applying filters in compliance with the standards for ECG devices, removing and interpolating low amplitude signals. Torso-tank experiment data was used with electrical activity recorded simultaneously from 128 tank electrodes and 108 epicardial sock electrodes. Subsequently, 10 representative beats were selected for analysis. Tikhonov zero-order regularization method was used to solve the inverse problem for the following groups; raw fullset, filtered fullset, raw low amplitude removed, filtered low amplitude removed, raw low amplitude interpolated, filtered low amplitude interpolated torso signals. Pearson’s correlation was used for comparison between measured and computed electrograms and between activation maps derived from them. Filtering the signal according to the standards improved the reconstructed electrograms. In addition, removal of low amplitude signals and replacing them with interpolated signals combined with filtering according to the standard significantly improved the reconstructed electrograms and derived activation maps.

KW - signal processing

KW - ECGs

UR - http://www.scopus.com/inward/record.url?scp=85067201760&partnerID=8YFLogxK

UR - https://link.springer.com/chapter/10.1007/978-3-030-21949-9_13

U2 - 10.1007/978-3-030-21949-9_13

DO - 10.1007/978-3-030-21949-9_13

M3 - Conference article

VL - 11504

SP - 112

EP - 120

JO - Lecture Notes in Artificial Intelligence

T2 - Lecture Notes in Artificial Intelligence

JF - Lecture Notes in Artificial Intelligence

SN - 0302-9743

ER -