Aldosari, Hanadi, Coenen, Frans 
ORCID: 0000-0003-1026-6649, Lip, Gregory YH 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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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) |
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| 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: | 18 Jan 2023 21:37 |
| DOI: | 10.1007/978-3-030-86534-4_22 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3128586 |
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