Symmetric Projection Attractor Reconstruction analysis of murine electrocardiograms Retrospective prediction of Scn5a+⁄- genetic mutation attributable to Brugada syndrome
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Bonet-Luz, Esther, Lyle, Jane V., Huang, Christopher L.-H., Zhang, Yanmin, Nandi, Manasi, Jeevaratnam, Kamalan and Aston, Philip J. (2020) Symmetric Projection Attractor Reconstruction analysis of murine electrocardiograms Retrospective prediction of Scn5a+⁄- genetic mutation attributable to Brugada syndrome Heart Rhythm.
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Abstract
Background: Life threatening arrhythmias resulting from genetic mutations are often missed in current ECG analysis. We combined a new method for ECG analysis that uses all the waveform data with machine learning to improve detection of such mutations from short ECG signals in a mouse model.
Objective: We sought to detect consequences of Na+ channel deficiencies known to compromise action potential conduction in comparisons of Scn5a+⁄- mutant and wild-type mice using short ECG signals, examining novel and standard features derived from Lead I and II ECG recordings by machine learning algorithms.
Methods: Lead I and II ECG signals from anaesthetised wild type and Scn5a+⁄- mutant mice of length 130s were analysed by extracting various groups of features which were used by machine learning to classify the mice as wild type or mutant. The features used were standard ECG intervals and amplitudes, as well as features derived from attractors generated using the novel Symmetric Projection Attractor Reconstruction method which reformulates the whole signal as a bounded, symmetric two-dimensional attractor. All the features were also combined as a single feature group.
Results: Classification of genotype using the attractor features gave higher accuracy than using either the ECG intervals or the intervals and amplitudes. However, the highest accuracy (96%) was obtained using all the features. Accuracies for different subgroups of the data were obtained and compared.
Conclusion: Detection of the Scn5a+⁄- mutation from short mouse ECG signals with high accuracy is possible using our Symmetric Projection Attractor Reconstruction method.
| Item Type: | Article | ||||||||||||||||||||||||
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| Divisions : | Faculty of Engineering and Physical Sciences > Mathematics | ||||||||||||||||||||||||
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| Date : | 2020 | ||||||||||||||||||||||||
| Funders : | Engineering and Physical Sciences Research Council (EPSRC) | ||||||||||||||||||||||||
| Grant Title : | Impact Acceleration Account | ||||||||||||||||||||||||
| Copyright Disclaimer : | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | ||||||||||||||||||||||||
| Uncontrolled Keywords : | Brugada syndrome; Scn5a+/- mutation; Symmetric Projection Attractor Reconstruction; ECG signals; Machine learning | ||||||||||||||||||||||||
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| Depositing User : | Clive Harris | ||||||||||||||||||||||||
| Date Deposited : | 07 Sep 2020 14:24 | ||||||||||||||||||||||||
| Last Modified : | 07 Sep 2020 14:24 | ||||||||||||||||||||||||
| URI: | http://epubs.surrey.ac.uk/id/eprint/858549 |
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