Addressing the Challenge of Data Heterogeneity Using a Homogeneous Feature Vector Representation: A Study Using Time Series and Cardiovascular Disease Classification



Aldosari, Hanadi, Coenen, Frans ORCID: 0000-0003-1026-6649, Lip, Gregory YH and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2021) Addressing the Challenge of Data Heterogeneity Using a Homogeneous Feature Vector Representation: A Study Using Time Series and Cardiovascular Disease Classification. .

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Abstract

An investigation into the use of a unifying Homogeneous Feature Vector Representation (HFVR), to address the challenge of applying machine learning and/or deep learning to heterogeneous data, is presented. To act as a focus, Atrial Fibrillation classification is considered which features both tabular and Electrocardiogram (ECG) time series data. The challenge of constructing HFVRs is the process for selecting features. A mechanism where by this can be achieved, in terms of motifs and discords, with respect to ECG time series data is presented. The presented evaluation demonstrates that more effective AF classification can be achieved using the idea of HFVR than would otherwise be achieved.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Unifying homogeneous feature vector representations, Time series feature extraction and analysis, Atrial fibrillation classification
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 12 Oct 2021 10:47
Last Modified: 18 Jan 2023 21:27
DOI: 10.1007/978-3-030-91100-3_21
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3140164