Μεταπτυχιακές εργασίες / Master Theses

Μόνιμο URI για αυτήν τη συλλογή

https://apothesis.hmu.gr/handle/20.500.12688/9246

Περιηγούμαι

Πλοήγηση Μεταπτυχιακές εργασίες / Master Theses ανά Θέμα "artificial intelligence"

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  • Μικρογραφία εικόνας
    Τεκμήριο
    Parkinson’s disease prediction using artificial intelligence
    (ΕΛΜΕΠΑ, Σχολή Μηχανικών (ΣΜΗΧ), ΠΜΣ Μηχανικών Πληροφορικής, 2024-04-08) Panagiotakis, Georgios; Παναγιωτάκης, Γεώργιος; Tsiknakis, Emmanouil; Τσικνάκης, Εμμανουήλ
    Precise diagnosis of Parkinson’s disease (PD) is crucial for effective treatment and management of this progressive neurological condition. Existing diagnostic methods face obstacles due to overlapping symptoms with other neurological disorders and the lack of a conclusive diagnostic test. Sleep disorders are common among PD patients, and nocturnal sleep electroencephalography (EEG) data hold significant insights into the connection between PD and sleep disturbances, providing opportunities for early diagnosis and disease tracking. This research utilizes deep learning methodologies to examine nocturnal sleep EEG data for the differentiation of PD subjects and healthy individuals. An extensive review of current literature is performed to evaluate the state-of-the-art in PD, sleep disorders, EEG data analysis, and deep learning applications for neurological disorder classification. A dataset of nocturnal sleep EEG recordings from PD patients and healthy subjects is obtained, preprocessed, and divided into sleep stages, followed by the extraction of pertinent features. Several deep learning models, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Gated Recurrent Units (GRU) networks, are explored for their appropriateness in the classification task. The chosen models are developed, executed, and optimized to differentiate PD subjects from healthy controls using nocturnal sleep EEG data. Model performance is assessed using relevant metrics (e.g., accuracy, precision, recall, F1-score) and compared with existing methods found in the literature.