Διάγνωση συμπτωμάτων της νόσου Πάρκινσον με βάση πολυτροπικά δεδομένα και μεθόδους βαθιάς μηχανικής μάθησης

This thesis examines the application of sophisticated deep learning (DL) methodologies for the detection and surveillance of Parkinson’s disease (PD) via multi-modal digital biomarkers. A comprehensive dataset was created, consisting of recordings of upper limb kinematics, facial expressivity, gait dynamics, and speech acoustics, gathered under clinically relevant conditions from 183 subjects across the Hoehn & Yahr (H&Y) spectrum. Uni-modal analyses utilized specialized architectures, including attention-based Bidirectional Long Short-Term Memory networks (BiLSTM), dual-stream transformers, hybrid Convolutional Neural Network–Support Vector Machine (CNN–SVM) frameworks, and Vision Transformers (ViTs) with generative augmentation, to identify modality-specific indicators of tremor, hypomimia, speech impairment, and locomotor dysfunction, attaining state-ofthe- art performance. The thesis presents and assesses a multi-modal DL framework that incorporates complementary signals via late, attention-based, and transformer-driven fusion techniques, along with controlled pruning for enhanced deployment efficiency. The results indicate that multi-modal integration significantly enhances predictive accuracy compared to uni-modal methods, especially for clinically relevant tasks like fall-risk prediction, where adaptive fusion attained f1-scores reaching 94%. Interpretability analyses established a connection between model decisions and neurologically pertinent aspects, thereby augmenting clinician trust. Ultimately, the thesis provides a validated multimodal dataset, innovative uni-modal and fusion structures, and clinically significant applications that enhance the role of digital biomarkers in individualized PD care and facilitate scalable, real-world monitoring systems.