Effects of ECG Signal Processing on the Inverse Problem of Electrocardiography

Bear, Laura; Doğrusöz, Yeşim Serinağaoğlu; Svehlikova, Jana; Coll‐Font, Jaume; Good, Wilson W.; Dam, Eelco van; MacLeod, Robert S.; Abell, Emma; Walton, Richard D.; Coronel, Ruben; Haı̈ssaguerre, Michel; Dubois, Rémi

doi:10.22489/cinc.2018.070

Cited by 16 publications

(16 citation statements)

References 6 publications

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“…Bear et al have shown in their work about the impact of filtering on inverse reconstruction of epicardial potentials that the removal of high frequency noise by applying signal averaging or low pass filtering improved the computation of activation times [9]. This suggests investigating the effect of signal averaging and low pass filtering at 40 Hz combined with interpolating low amplitude signals on the reconstructed activation maps.…”

Section: Discussionmentioning

confidence: 99%

“…To solve the inverse problem, a forward transformation matrix that relates the epicardial potentials (894 nodes) to torso tank surface potentials (107 or 128 nodes) was calculated using the boundary element method (BEM) assuming the conductivity of the volume between heart surface and tank surface is homogenous [9]. We solved the inverse problem using Tikhonov zero-order regularization method from a toolkit for forward/inverse problems in electrocardiography within the SCIRun environment [3].…”

Section: Inverse Reconstruction Of Epicardial Potentialsmentioning

confidence: 99%

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

Rababah

Finlay

Bear

et al. 2019

Lecture Notes in Computer Science

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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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Section: Discussionmentioning

confidence: 99%

Section: Inverse Reconstruction Of Epicardial Potentialsmentioning

confidence: 99%

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

Rababah

Finlay

Bear

et al. 2019

Lecture Notes in Computer Science

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“…Experimental data came from a Langendorff-perfused pig heart suspended in a human-shaped torso-tank. Epicardial electrograms were recorded during RV pacing using a 108-electrode array, simultaneously with ECGs from 128 electrodes embedded in the tank surface [6]. Data consisted of 30 seconds of ECG recordings, with 31 beats.…”

Section: Methodsmentioning

confidence: 99%

Effects of Interpolation on the Inverse Problem of Electrocardiography

Doğrusöz¹,

Bear²,

Bergquist³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Electrocardiographic Imaging (ECGI) aims to reconstruct electrograms from the body surface potential measurements. Bad leads are usually excluded from the inverse problem solution. Alternatively, interpolation can be applied. This study explores how sensitive ECGI is to different bad-lead configurations and interpolation methods. Experimental data from a Langendorff-perfused pig heart suspended in a human-shaped torso-tank was used. Six different bad lead cases were designed based on clinical experience. Inverse problem was solved by applying Tikhonov regularization i) using the complete data, ii) badleads-removed data, and iii) interpolated data, with 5 different methods. Our results showed that ECGI accuracy of an interpolation method highly depends on the location of the bad leads. If they are in the high-potential-gradient regions of the torso, a highly accurate interpolation method is needed to achieve an ECGI accuracy close to using complete data. If the BSP reconstruction of the interpolation method is poor in these regions, the reconstructed electrograms also have lower accuracy, suggesting that bad leads should be removed instead of interpolated. The inverseforward method was found to be the best among all interpolation methods applied in this study in terms of both missing BSP lead reconstruction and ECGI accuracy, even for the bad leads located over the chest.

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“…Epicar-dial electrograms were recorded during RV pacing using a 108-electrode array, simultaneously with ECGs from 128 electrodes embedded in the tank surface. A more detailed explanation of the experimental procedure can be found in [8] and the references therein. Data consisted of 30 minutes of ECG recordings, with 31 beats.…”

Section: Methodsmentioning

confidence: 99%

Reduction of Effects of Noise on the Inverse Problem of Electrocardiography With Bayesian Estimation

Doğrusöz

Bear

Svehlikova

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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To overcome the ill-posed nature of the inverse problem of electrocardiography (ECG) and stabilize the solutions, regularization is used. Despite several studies on noise, effect of prefiltering of ECG signals on the regularized inverse solutions has not been explored. We used Bayesian estimation for solving the inverse ECG problem with and without applying various prefiltering methods, and evaluated our results using experimental data that came from a Langendorff-perfused pig heart suspended in a human-shaped torso-tank. Epicardial electrograms were recorded during RV pacing using a 108-electrode array, simultaneously with ECGs from 128 electrodes embedded in the tank surface. Leave-one-beat-out protocol was used to obtain the prior probability density function (pdf) of electro-grams and noise statistics. Noise pdf was assumed to be zero mean-Gaussian, with covariance assumptions: a) independent and identically distributed (noi-iid), b) correlated (noi-corr). Reconstructed electrograms and activation times were compared to those directly recorded by the sock for 3 beats selected from the recording. Noi-corr is superior to noi-iid when the training set is a good match to data, but for applications requiring activation time derivation, careful selection of preprocessing methods, in particular to adequately remove high-frequency noise, and an appropriate noise model is needed.

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Effects of ECG Signal Processing on the Inverse Problem of Electrocardiography

Cited by 16 publications

References 6 publications

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

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

Effects of Interpolation on the Inverse Problem of Electrocardiography

Reduction of Effects of Noise on the Inverse Problem of Electrocardiography With Bayesian Estimation

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