Motif Based Feature Vectors: Towards a Homogeneous Data Representation for Cardiovascular Diseases Classification

Aldosari, Hanadi, Coenen, Frans ORCID: 0000-0003-1026-6649, Lip, Gregory YH ORCID: 0000-0002-7566-1626 and Zheng, Yalin ORCID: 0000-0002-7873-0922 (2021) Motif Based Feature Vectors: Towards a Homogeneous Data Representation for Cardiovascular Diseases Classification. .

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Official URL: http://dx.doi.org/10.1007/978-3-030-86534-4_22

Abstract

A process for generating a unifying motif-based homogeneous feature vector representation is described and evaluated. The motivation was to determine the viability of this representation as a unifying representation for heterogeneous data classification. The focus for the work was cardiovascular disease classification. The reported evaluation indicates that the proposed unifying representation is a viable one, producing better classification results than when a Recurrent Neural Network (RNNs) was applied to just ECG time series data.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Motifs, Feature extraction and selection, Cardiovascular disease classification
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	05 Jul 2021 13:51
Last Modified:	20 Nov 2023 15:22
DOI:	10.1007/978-3-030-86534-4_22
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3128586