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Identifier 000425811
Title Automated dynamical analysis of multivariate physiological data for personalized healthcare
Alternative Title Αυτοματοποιημένη δυναμική ανάλυση πολυπαραγοντικών φυσιολογικών δεδομένων για εξατομικευμένη περίθαλψη υγείας
Author Ζερβού, Μιχαέλα-Αρετή Σ.
Thesis advisor Τσακαλίδης, Παναγιώτης
Reviewer Παπαδούλη, Μαρία
Τζαγκαράκης, Γεώργιος
Abstract Wearable body sensors along with data analytics have been recently employed in the domain of personalized healthcare, since wearable technology can monitor physical activity, collect data and deliver real-time feedback to individuals or experts. However, there are several challenges associated with the monitoring of patient performance using body sensor data. Specifically, the captured sensor data might be too patient-specific and inadequate for the differentiation of normal and abnormal behavior, which is formulated as a classification problem. Thus, a generic approach is required to analyze patient activity in an unsupervised or semi-supervised manner for identifying patterns and also being scalable across multiple heterogeneous sensors capturing different data. Multivariate time series analysis is considered as a difficult problem due to the complexity of the data types. The main challenges of processing time series data involve the high dimensionality, the presence of noise and redundancy in the data. The majority of existing diagnosis techniques employ features extracted from symbolic or frequency-domain representations of the associated data, whilst ignoring completely the behavior of the underlying data generating the dynamical system. Moreover, those techniques lack the capability of concurrently processing multiple dimensions. To address this problem, this thesis proposes a novel self-tuned architecture for feature extraction, by modeling directly the inherent dynamics of multidimensional wearable sensor time series data in higher-dimensional phasespaces, which encode state recurrences considering a specific task. In essence, our work extends the state-of-the-art multidimensional recurrence quantification analysis (RQA),that is based on state vectors, to a more generic state matrix-based architecture. State matrices are considered more appropriate for describing multi-dimensional signals from a mathematical perspective, enabling them to model the correlations not only within a signal but also between different signals. It is worth mentioning that in contrast to the state-of-the-art, the herein proposed framework is directly applicable to several data structures such as images, hyper-spectral data or video streams. Furthermore, this study employs machine learning techniques to design an efficient feature extraction scheme for the discovery of information-rich patterns that best capture the underlying data dynamics of multidimensional time series data for dyslexia detection. Dyslexia is a developmental learning disorder of single word reading accuracy and/or fluency, with compelling research directed towards understanding the contributions of the visual system. While dyslexia is not an oculomotor disease, readers with dyslexia have shown different eye movements than typically developing students during text reading. Experimental evaluation on real data of eye-tracking for diagnosing dyslexia demonstrates an improved performance of our method in terms of classification accuracy, F-score and robustness in the presence of noise when compared against a stateof-the-art unidimensional and vector-based multidimensional RQA.
Language English
Subject Multivariate timeseries
Αυτόματη δυναμική ανάλυση
Πολυμορφικές χρονοσειρές
Issue date 2019-07-26
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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