A Variational Approach to Sparse Model Error Estimation in Cardiac Electrophysiological Imaging

Ghimire, Sandesh; Sapp, John L.; Horacek, Milan; Wang, Linwei

doi:10.1007/978-3-319-66185-8_84

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is well known that there is no unique solution to Equation (3) because the number of electrodes is extremely small compared to the size of the current density vector. Thus, we represent the electrical activity of the heart within sparse time/spatial domains [ 44 , 45 , 46 ]. The equivalent cardiac source is localized by the OMP, which is a sparse modeling technique [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

Nakano

Rashed

Nakane

et al. 2021

Sensors

View full text Add to dashboard Cite

The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.

show abstract

Section: Methodsmentioning

confidence: 99%

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

Nakano

Rashed

Nakane

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The method relies on a two-variable propagation model with fixed parameters in a volumetric myocardium. It was then improved in Ghimire et al (2017). Note that in these studies constraints in the spatial distribution are considered.…”

Section: Introductionmentioning

confidence: 99%

Impact of the Endocardium in a Parameter Optimization to Solve the Inverse Problem of Electrocardiography

et al. 2019

View full text Add to dashboard Cite

Electrocardiographic imaging aims at reconstructing cardiac electrical events from electrical signals measured on the body surface. The most common approach relies on the inverse solution of the Laplace equation in the torso to reconstruct epicardial potential maps from body surface potential maps. Here we apply a method based on a parameter identification problem to reconstruct both activation and repolarization times. From an ansatz of action potential, based on the Mitchell-Schaeffer ionic model, we compute body surface potential signals. The inverse problem is reduced to the identification of the parameters of the Mitchell-Schaeffer model. We investigate whether solving the inverse problem with the endocardium improves the results or not. We solved the parameter identification problem on two different meshes: one with only the epicardium, and one with both the epicardium and the endocardium. We compared the results on both the heart (activation and repolarization times) and the torso. The comparison was done on validation data of sinus rhythm and ventricular pacing. We found similar results with both meshes in 6 cases out of 7: the presence of the endocardium slightly improved the activation times. This was the most visible on a sinus beat, leading to the conclusion that inclusion of the endocardium would be useful in situations where endo-epicardial gradients in activation or repolarization times play an important role.

show abstract

“…To fix these model parameters in optimization/inference, as is common in existing approaches [12], model errors may be introduced decreasing the accuracy of the estimated electrical activity [12]. To adapt these model parameters to the observed data, as is desired for accurate inference, is however difficult due to their high-dimensionality and nonlinear relationship with the observed ECG data [3].…”

Section: Introductionmentioning

confidence: 99%

Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential

Ghimire

Dhamala

Gyawali

et al. 2018

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is powerful for incorporating rich physiological knowledge about spatiotemporal TMP dynamics. These models are controlled by high-dimensional physical parameters which, if fixed, can introduce model errors and reduce the accuracy of TMP reconstruction. Simultaneous adaptation of these parameters during TMP reconstruction, however, is difficult due to their high dimensionality. We introduce a novel model-constrained inference framework that replaces conventional physiological models with a deep generative model trained to generate TMP sequences from low-dimensional generative factors. Using a variational auto-encoder (VAE) with long short-term memory (LSTM) networks, we train the VAE decoder to learn the conditional likelihood of TMP, while the encoder learns the prior distribution of generative factors. These two components allow us to develop an efficient algorithm to simultaneously infer the generative factors and TMP signals from ECG data. Synthetic and real-data experiments demonstrate that the presented method significantly improve the accuracy of TMP reconstruction compared with methods constrained by conventional physiological models or without physiological constraints.

show abstract

A Variational Approach to Sparse Model Error Estimation in Cardiac Electrophysiological Imaging

Cited by 5 publications

References 12 publications

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

Impact of the Endocardium in a Parameter Optimization to Solve the Inverse Problem of Electrocardiography

Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential

Contact Info

Product

Resources

About