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MultimoDal tEnsor fuSion and Completion foR patient-taIlored catheter aBlation of persistEnt atrial fibrillation

CORDIS · observation · Publication date unknown

MultimoDal tEnsor fuSion and Completion foR patient-taIlored catheter aBlation of persistEnt atrial fibrillation Tensor analysis plays a central role in signal processing and machine learning for the representation, analysis, fusion, and classification of data. Responsible for up to 25% of brain strokes, atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia and remains the last great frontier of cardiac electrophysiology. Catheter ablation is the most attractive therapeutic option for persistent AF, although the identification of suitable target areas is strongly dependent on practitioners subjectivity. Multi-electrode catheters are increasingly used in ablation as they facilitate the electroanatomical mapping of the atria, but often deliver incomplete data due to lack of contact with the atrial wall. This project aims to improve the personalized characterization and management of AF by proposing novel tensor-based methods for multimodal data fusion in a possibly missing information scenario. New coupled tensor models will be introduced

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recordType

award

status

SIGNED

region

EU

value

105877.44

unit

EUR