Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches
Abstract In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. T...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25919-3 |
| _version_ | 1797977461665824768 |
|---|---|
| author | Vikas R. Bhat Basudha Pal H. Anitha Ananthakrishna Thalengala |
| author_facet | Vikas R. Bhat Basudha Pal H. Anitha Ananthakrishna Thalengala |
| author_sort | Vikas R. Bhat |
| collection | DOAJ |
| description | Abstract In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. The results of the deterministic method subjected to noise in the measurements are discussed and compared with the probabilistic models. Hierarchical Bayesian modeling with fixed Gaussian prior is employed to quantify the uncertainties in source reconstructions. A novel application of Variational Bayesian inference approach has been presented to estimate the heart sources. The reconstruction results of Variational Bayesian model with non-stationary priors are compared with solutions of simplistic Bayesian approach; and the performances are evaluated using Root Mean Square Error (RMSE) and correlation co-efficient metrics. The Bayesian solutions in the study are also extended to localize the MCG sources for two types of Myocardial infarction cases. |
| first_indexed | 2024-04-11T05:07:16Z |
| format | Article |
| id | doaj.art-41dbbe3b8a6242b69de3d8c9e2d9233c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T05:07:16Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-41dbbe3b8a6242b69de3d8c9e2d9233c2022-12-25T12:13:02ZengNature PortfolioScientific Reports2045-23222022-12-0112111610.1038/s41598-022-25919-3Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approachesVikas R. Bhat0Basudha Pal1H. Anitha2Ananthakrishna Thalengala3Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE)Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE)Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE)Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE)Abstract In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. The results of the deterministic method subjected to noise in the measurements are discussed and compared with the probabilistic models. Hierarchical Bayesian modeling with fixed Gaussian prior is employed to quantify the uncertainties in source reconstructions. A novel application of Variational Bayesian inference approach has been presented to estimate the heart sources. The reconstruction results of Variational Bayesian model with non-stationary priors are compared with solutions of simplistic Bayesian approach; and the performances are evaluated using Root Mean Square Error (RMSE) and correlation co-efficient metrics. The Bayesian solutions in the study are also extended to localize the MCG sources for two types of Myocardial infarction cases.https://doi.org/10.1038/s41598-022-25919-3 |
| spellingShingle | Vikas R. Bhat Basudha Pal H. Anitha Ananthakrishna Thalengala Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches Scientific Reports |
| title | Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches |
| title_full | Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches |
| title_fullStr | Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches |
| title_full_unstemmed | Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches |
| title_short | Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches |
| title_sort | localization of magnetocardiographic sources for myocardial infarction cases using deterministic and bayesian approaches |
| url | https://doi.org/10.1038/s41598-022-25919-3 |
| work_keys_str_mv | AT vikasrbhat localizationofmagnetocardiographicsourcesformyocardialinfarctioncasesusingdeterministicandbayesianapproaches AT basudhapal localizationofmagnetocardiographicsourcesformyocardialinfarctioncasesusingdeterministicandbayesianapproaches AT hanitha localizationofmagnetocardiographicsourcesformyocardialinfarctioncasesusingdeterministicandbayesianapproaches AT ananthakrishnathalengala localizationofmagnetocardiographicsourcesformyocardialinfarctioncasesusingdeterministicandbayesianapproaches |