Benedetta Perrone was a Postgraduate Research Fellow at the Department of Control and Computer Engineering of Politecnico di Torino. Her research lay at the intersection of biomedical signal processing, sleep analysis, and machine learning, with a focus on developing algorithms for physiological monitoring.

Bachelor and Master

Benedetta Perrone obtained her B.Sc. (2019–2022) and M.Sc. (2022–2024) degrees in Biomedical Engineering from Politecnico di Torino, with her Master’s degree specializing in e-Health. During her studies, she developed a strong interest in biomedical signal analysis, wearable technologies, and data-driven methods for healthcare applications.

Her Master’s thesis, “Voice Classification in Parkinson’s Disease Using Transformer Models and Error Rate Metrics”, investigated speech-based biomarkers of Parkinson’s disease (PD), focusing on both disease detection and severity assessment. The study applied Vision Transformer-based models to vocal recordings from PD patients and healthy controls.

Benedetta is an inducted member of IEEE-HKN Mu Nu Chapter.

Working Experience and Research Activities

Benedetta was a Postgraduate Research Fellow at the Department of Control and Computer Engineering of Politecnico di Torino.

Her project, in collaboration with Molinette Hospital, focused on the analysis of polysomnographic recordings. Together with Ilaria Ciampa, she designed signal processing pipelines to extract HRV features from ECG and PPG signals. Her work included implementing experimental protocols for tachogram measurement during polysomnographic exams and developing algorithms for automated sleep monitoring, with a particular focus on studying the correlations between HRV features derived from PPG and ECG signals.