Federico Delrio

Ph.D. candidate in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 38th cycle (2022-2025)
Department of Electronics and Telecommunications (DET)

Profile

PhD

Research topic

Machine Learning Enhanced Analysis of ECG and Clinical Data

Tutors

Research presentation

Poster

Research interests

Big Data, Machine Learning, Neural Networks and Data Science
Biomedical devices and applications

Biography

Federico Delrio is currently a PhD student in Electrical, Electronic, and Communication Engineering at the Neuronica Lab research group at Politecnico di Torino.
He earned a master’s degree in Astrophysics at the University of Turin, with the thesis “Bounds on ultra-light axions from the neutral hydrogen auto-correlation function”. The aim of this thesis is to set limits on certain aspects of dark matter by assuming that a part of it is composed of ultra-light axions (ULAs).
His current line of research involves using machine learning/deep learning and artificial intelligence methods to improve the analysis of medical data.
The main research topics Federico Delrio is working on are as follows:
  • Non-Invasive Arterial Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Signals Using a Conv1D-BiLSTM Neural Network: This research consists of finding a non-invasive method for measuring arterial blood pressure using neural networks starting from ECG and possibly PPG as inputs.
  • Enhancing ECG Analysis with a Hybrid Deep Learning Approach: Automatic Detection of Significant Features: This research aims to use neural networks to identify the main features of the ECG, such as the length of the QRS complex, P wave, and T wave.
  • Transformer interpretability: The interpretability of artificial intelligence models is still an open problem; therefore, this research is aimed at developing a new type of Transformer that is more easily interpretable.

Teaching

Teachings

Master of Science

MostraNascondi A.A. passati

Bachelor of Science

MostraNascondi A.A. passati

Publications

Latest publications View all publications in Porto@Iris