Yingjing Feng

Yingjing Feng
University of Birmingham · Centre for Systems Modelling and Quantitative Biomedicine

PhD
Postdoctoral Research Fellow at Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham

About

12
Publications
4,697
Reads
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449
Citations
Additional affiliations
June 2019 - July 2019
King's College London
Position
  • PhD Student
Description
  • Visit the Cardiac Electro Mechanics Research Group with Prof. Steven Niederer and Dr. Caroline Roney, on in-silico study of body surface maps for AF patients.
May 2018 - present
University of Bordeaux
Position
  • PhD Student
Description
  • I am working on my PhD thesis of "Personalized atrial fibrillation modelling from non-invasive mapping with machine learning", where I combined modelling and machine learning on studying the body surface potential map for AF treatment.
September 2015 - September 2016
Position
  • Master's Student
Description
  • I finished my Master in Computing (Machine Learning) degree and conducted my Master's project on "An Efficient Cardiac Mapping Strategy for Radiofrequency Catheter Ablation with Active Learning", which was published in IJCARS journeal.
Education
May 2018 - December 2021
University of Bordeaux
Field of study
  • Applied Mathematics and Scientific Computing
September 2015 - September 2016
Independent Researcher
Independent Researcher
Field of study
  • Computing (Machine Learning)

Publications

Publications (12)
Article
Full-text available
Objective: About half of patients experience recurrence of atrial fibrillation (AF) within three to five years after a single catheter ablation procedure. The suboptimality of the long-term outcomes likely results from the inter-patient variability of AF mechanisms, which can be remedied by improved patient screening. We aim to improve the interpre...
Article
Full-text available
Focal sources (FS) are believed to be important triggers and a perpetuation mechanism for paroxysmal atrial fibrillation (AF). Detecting FS and determining AF sustainability in atrial tissue can help guide ablation targeting. We hypothesized that sustained rotors during FS-driven episodes indicate an arrhythmogenic substrate for sustained AF, and t...
Thesis
Full-text available
Atrial fibrillation (AF), the rapid and irregular activation of the atria, is the most common clinical arrhythmia. Catheter ablation therapy is the most effective treatment and improves the quality of life, but standard protocols show sub-optimal long-term success, substantiating the need for personalized ablation. Body surface potential maps (BSPM...
Article
Full-text available
Reliable patient-specific ventricular repolarization times (RTs) can identify regions of functional block or afterdepolarizations, indicating arrhythmogenic cardiac tissue and the risk of sudden cardiac death. Unipolar electrograms (UEs) record electric potentials, and the Wyatt method has been shown to be accurate for estimating RT from a UE. High...
Article
Full-text available
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twi...
Article
Full-text available
Objective A major challenge in radiofrequency catheter ablation procedures is the voltage and activation mapping of the endocardium, given a limited mapping time. By learning from expert interventional electrophysiologists (operators), while also making use of an active-learning framework, guidance on performing cardiac voltage mapping can be provi...

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