Advanced signal processing and machine learning tools for non-invasive foetal electrocardiography and intracardiac electrophysiology
Creators: name: BALDAZZI, GIULIA
Publisher: name: Universit� degli studi di Genova
Year: 2022

description: In the last decades, bioengineering research promoted the improvement in human health and wellbeing through the development, optimization and evaluation of innovative technologies and medical devices for both diagnosis and therapy. In this context, the exploitation of biomedical technology advances plays a key role in the study and treatment of heart disorders.
This PhD thesis focuses on two main application areas: on one hand, foetal cardiac physiology and electrocardiography and, on the other, intracardiac electrophysiology, substrate mapping and radiofrequency ablation. There, it aims at providing new instruments and insights to improve the knowledge and go beyond the current state of the art by the development of novel signal processing and machine learning tools that aim at supporting the diagnosis and treatment of cardiac diseases. 
Non-invasive foetal ECG (fECG) is a long-standing niche research topic characterized by the continuous demand of improved solutions to solve the problem of recovering high-quality fECG signals from non-invasive trans-abdominal recordings. This PhD thesis focused on the development of algorithms for non-invasive fECG extraction and enhancement. Specifically, in collaboration with the Prof. Hau-Tieng Wu (Department of Mathematics and Statistical Science, Duke University, Durham, NC, USA), a novel algorithm for the extraction of morphologically preserved multi-channel fECG signals was conceived. Furthermore, wavelet denoising was deeply investigated for the post-processing of the fECG recordings, to quantitatively evaluate the noise-removal and morphology-preservation effects of different wavelet denoising approaches, expressly tailored for this application domain. 
Intracardiac electrophysiology is a branch of interventional cardiology aimed at the diagnosis and treatment of arrhythmias by catheter-based techniques exploiting electroanatomic substrate mapping and ablation. In this exciting scenario, this PhD thesis focused on post-ischemic ventricular tachycardia, which is a life-threatening arrhythmia. Being the electrophysiological studies and ablations very time-consuming and operator-dependent, the first applied-research goal was the development of an effective tool able to support clinical experts in the recognition of the ablation targets during clinical procedures. Moreover, a detailed spectral characterization of post-ischaemic signals was performed, thus paving the way to the development of novel approaches in terms of advanced signal analysis, automatic recognition of the arrhythmogenic substrates, study of the substrate and, in general, to a deeper understanding of the arrhythmogenic mechanisms.
Beyond the scientific content, this PhD thesis gives an important contribution from an industrial perspective in both fields. In fact, automated signal processing tools for the non-invasive fECG signals can improve the detection capabilities of current tools, to be clinically exploited for low-cost antenatal screening. At the same time, novel methods for ablation targets recognition in cardiac electrophysiology could be embedded in future medical electroanatomic mapping systems as plug-in to enhance current computer-aided methods.