Francesca Librale
Combination of Explainable AI with Uncertainty Quantification for Arrhythmia Detection in PPG-Based Diagnostics.
Rel. Massimo Salvi, Silvia Seoni. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
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Abstract
Photoplethysmography (PPG) is a non-invasive optical technique enabling the measurement of various physiological parameters. With its versatility, ease of acquisition, and cost-effectiveness, PPG has become an increasingly promising tool for modern diagnostic systems. Different pathologies can be unveiled by PPG signals and arrhythmia is one of these. Given the asymptomatic nature and wide variability of arrhythmias, continuous monitoring paired with highly accurate detection of multiple arrhythmia types may offer a winning strategy for dealing with it. Artificial intelligence (AI) algorithms support the demand for high performance, even in complex, multi-label tasks. Deep Learning (DL), a powerful AI approach, has shown potential to achieve human-comparable performance.
However, the adoption of AI tools in clinical settings remains challenging due to their "black box" nature, which limits the transparency of the generated outputs
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