0000000000018558

AUTHOR

Eduardo J. Godoy

showing 2 related works from this author

Analysis of in-silico body surface P-wave integral maps show important differences depending on the connections between coronary sinus and left atrium

2016

The electrical connections between the atrial coronary sinus (CS) and the left atrial (LA) myocardium have an effect on the overall atrial activation pattern and the P-wave morphology. In this study, we use our validated multi-scale 3D human atrial-torso model to elucidate which electro-anatomical configuration of connections between CS and LA more accurately reproduces a set of body surface P-wave integral maps (BSPiM) acquired in the clinic. We performed atrial biophysical simulations by pacing in distal and proximal LA sites. The corresponding in-silico BSPiM were then computed and compared with published clinical patterns obtained from patients. Important differences in BSPiM were obser…

medicine.medical_specialty0206 medical engineeringP waveLeft atrium02 engineering and technologyAnatomyAtrial activation020601 biomedical engineering030218 nuclear medicine & medical imaging03 medical and health sciencesOstium0302 clinical medicinemedicine.anatomical_structureLeft atrialInternal medicineBody surfacecardiovascular systemmedicineCardiologycardiovascular diseasesCoronary sinusMathematics
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Combining Biophysical Modeling and Machine Learning to Predict Location of Atrial Ectopic Triggers

2018

The search for focal ectopic activity in the atria triggered from non-standard regions can be time consuming. The use of body surface potential maps to plan the intervention can be helpful, but require an advance processing of the data, that usually involves to solve an ill-posed inverse problem. In addition, changes in maps due to pathological substrate such as fibrosis might affect the expected electrical patterns. In this work, we use a machine learning approach to relate ectopic focus activity in different atrial regions with body surface potential maps, and consider the effects of fibrosis in various densities and distributions. Results show that as fibrosis increases over 15% the syst…

Computer sciencebusiness.industry0206 medical engineering02 engineering and technology030204 cardiovascular system & hematologyInverse problemmedicine.diseaseMachine learningcomputer.software_genre020601 biomedical engineering03 medical and health sciences0302 clinical medicineFibrosismedicineArtificial intelligenceFocus (optics)businesscomputerAtrial ectopic2018 Computing in Cardiology Conference (CinC)
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