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We are pleased to inform that PhySense has a new Doctor!! Dr. Guillermo Jimenez Pérez

We are pleased to inform that PhySense has a new Doctor!! Dr. Guillermo Jimenez Pérez

Congratulations to Dr. Jiménez Pérez, who has been awarded with the Mention "Cum Laude". Well done!! 

Dr. Jiménez presented his dissertation on February 17th, 2022. 




Pr. Oscar Camara (Universitat Pompeu Fabra, Barcelona, Spain)

Thesis title: 

Deep learning and unsupervised machine learning for the quantification and interpretation of electrocardiographic signals


Electrocardiographic signals, either acquired on the patient’s skin (surface electrocardiogam, ECG) or invasively through catheterization (intracavitary electrocardiogram, iECG) offer a rich insight into the patient’s cardiac condition and function given their ability to represent the electrical activity of the heart. However, the interpretation of ECG and iECG signals is a complex task that requires years of experience, difficulting the correct diagnosis for non-specialists, during stress-related situations such as in the intensive care unit, or in radiofrequency ablation (RFA) procedures where the physician has to interpret hundreds or thousands of individual signals. From the computational point of view, the development of high-performing pipelines from data analysis suffer from lack of large-scale annotated databases and from the “black-box” nature of state-of-the-art analysis approaches. This thesis attempts at developing machine learning-based algorithms that aid physicians in the task of automatic ECG and iECG interpretation. The contributions of this thesis are fourfold. Firstly, an ECG delineation tool has been developed for the markup of the onsets and offsets of the main cardiac waves (P, QRS and T waves) in recordings comprising any configuration of leads. Secondly, a novel synthetic data augmentation algorithm has been developed for palliating the impact of small-scale datasets in the development of robust delineation algorithms. Thirdly, this methodology was applied to similar data, intracavitary electrocardiographic recordings, with the objective of marking the onsets and offsets of events for facilitating the localization of suitable ablation sites. For this purpose, the ECG delineation algorithm previously developed was employed to pre-process the data and mark the QRS detection fiducials. Finally, the ECG delineation approach was employed alongside a dimensionality reduction algorithm, Multiple Kernel Learning, for aggregating the information of 12-lead ECGs with the objective of developing a pipeline for risk stratification of sudden cardiac death in patients with hypertrophic cardiomyopathy.








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