Giulia Ciribilli
Wearable-Oriented Unsupervised Clustering of Heart Rate Variability and Body Position Features for Phenotyping Sleep Disorders.
Rel. Gabriella Olmo, Umberto Mosca. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2026
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview |
Abstract
REM Sleep Behaviour Disorder (RBD) is a parasomnia characterized by the loss of physiological muscle atonia during rapid eye movement sleep, leading to dream-enactment. Idiopathic RBD is strongly associated with α-synucleinopathies and frequently precedes the onset of motor or cognitive symptoms by several years, making it a valuable prodromal marker of neurodegenerative diseases, such as Parkinson’s disease. Given the substantial healthcare burden of neurodegenerative diseases, early identification of at-risk individuals is essential to enable timely interventions, positioning RBD as a critical target for early detection. However, the current gold standard for RBD diagnosis is video-polysomnography, an expensive and resource-intensive procedure, unsuitable for large-scale screening.
Heart Rate Variability (HRV), which reflects autonomic cardiac regulation, differs in individuals with neurodegenerative diseases compared to healthy controls (HC), positioning HRV-derived metrics as non-invasive approach for screening and early disease detection
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
URI
![]() |
Modifica (riservato agli operatori) |
