Thesis Project Form
Title (tentative): Signal Processing and Bio-Fingerprinting applied to Polysomnographic data| Thesis advisor(s): Dellepiane Silvana, Marco Trombini (DITEN), Francesca D. Faraci (SUPSI) | E-mail: |
| Address: Via all'Opera Pia 11 (ex-CNR) - 16145 Genova, Italy | Phone: (+39) 010 33 52754 |
Description
Motivation and application domain
The Polysomnography (PSG) is a technique used to diagnose, or rule out, many types of sleep disorders, including narcolepsy, idiopathic hypersomnia, periodic limb movement disorder (PLMD), REM behavior disorder, parasomnias, and sleep apnea.
Many sleep-wake signal patterns (alpha waves, vertex sharp waves, sleep spindles, K-complexes, saw-tooth waves, arousals, slow eye movements -SEMs, rapid eye movements - REMs ) are well defined for healthy subjects, but not for un-healthy one.
Many sleep-wake signal patterns (alpha waves, vertex sharp waves, sleep spindles, K-complexes, saw-tooth waves, arousals, slow eye movements -SEMs, rapid eye movements - REMs ) are well defined for healthy subjects, but not for un-healthy one.
General objectives and main activities
The Polysomnography (PSG) monitors brain activity (EEG), eye movements (EOG), muscle activity or skeletal muscle activation (EMG derivations for chin and legs), body position (video camera) and heart rhythm (ECG). Breathing functions (respiratory airflow, oxygen saturation, respiratory effort indicators) are also measured.
The candidate should aim at developing an automated patient-personalized pattern recognition system (e.g. alpha waves, vertex sharp waves, sleep spindles, K-complexes, saw-tooth waves, arousals, slow eye movements -SEMs, rapid eye movements - REMs). Extensive literature and shared codes on this subject are available.
The candidate should aim at developing an automated patient-personalized pattern recognition system (e.g. alpha waves, vertex sharp waves, sleep spindles, K-complexes, saw-tooth waves, arousals, slow eye movements -SEMs, rapid eye movements - REMs). Extensive literature and shared codes on this subject are available.
Training Objectives (technical/analytical tools, experimental methodologies)
_ Study and comprehension of state-of-the-art results and algorithms.
_ Definition of sleep-wake signal patterns for un-healthy subjects.
_ Development of an automated patient-personalized pattern recognition system.
_ Definition of sleep-wake signal patterns for un-healthy subjects.
_ Development of an automated patient-personalized pattern recognition system.
Place(s) where the thesis work will be carried out: DITEN (UNIGE); Università di Lugano
Additional information
Pre-requisite abilities/skills: Experience in computational science (or willing to learn) with Python programming environment is preferable.
Maximum number of students: 2
Financial support/scholarship: Available