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Predicting Dyskinetic Events through Verified Multivariate Time Series Classification

Lookup NU author(s): Dr Giacomo BergamiORCiD, Emma Packer, Dr Kirsty ScottORCiD, Dr Silvia Del DinORCiD

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer Verlag, 2024.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

While monitoring Parkinsonian patients with wearable sensors and tracking their drug assumption patterns, we want to differentiate the behaviours distinguishing periods of relative well-being from dyskinetic events. This requires solving a novel problem, where an entire multivariate time series (MTS) has its class label varying in time, thus leading to a generalised formulation of multivariate time series classification (MTSC). To achieve explainability, we premier the composition of data trend (DT) analysis with DECLAREd, a log temporal declarative language, to derive human-readable correlations across different MTS dimensions' trends. This is mediated by a novel temporal data representation, polyadic logs, supporting both MTS raw data and concurrent activity-labelled durative activities (constituents) for representing event-based classes and concurrent DTs across MTS dimensions. Our validation over a real patient dataset shows that our MTCS algorithm, EMeriTAte, outperforms state-of-the-art MTSC for a novel patient classification task.


Publication metadata

Author(s): Bergami G, Packer E, Scott K, Del Din S

Editor(s): Chbeir R; Ilarri S; Manolopoulos Y; Revesz PZ; Bernardino J; Leung CK;

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: The 28th International Database Engineered Applications Symposium IDEAS 2024

Year of Conference: 2024

Pages: 49-62

Print publication date: 16/03/2025

Online publication date: 28/08/2024

Acceptance date: 30/07/2024

Date deposited: 04/10/2024

ISSN: 0302-9743

Publisher: Springer Verlag

URL: https://doi.org/10.1007/978-3-031-83472-1_4

DOI: 10.1007/978-3-031-83472-1_4

ePrints DOI: 10.57711/6y6g-cw67

Notes: 9783031834721 ebook ISBN.

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783031834714


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