Deep Learning Applied to Attractor Images Derived from ECG Signals for Detection of Genetic Mutation

Aston, Philip, Lyle, Jane, Bonet-Luz, Esther, Huang, Christopher, Zhang, Yanmin, Jeevaratnam, Kamalan and Nandi, Manasi (2020) Deep Learning Applied to Attractor Images Derived from ECG Signals for Detection of Genetic Mutation In: International Conference in Computing in Cardiology 2019 (CinC 2019), 8-11 September 2019, Matrix, Singapore.

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Abstract

The aim of this work is to distinguish between wild-type mice and Scn5a +/- mutant mice using short ECG signals. This mutation results in impaired cardiac sodium channel function and is associated with increased ventricular arrhythmogenic risk which can result in sudden cardiac death. Lead I and Lead II ECG signals from wild-type and Scn5a +/- mice are used and the mice are also grouped as female/male and young/old.We use our novel Symmetric Projection Attractor Reconstruction (SPAR) method to generate an attractor from the ECG signal using all of the available waveform data. We have previously manually extracted a variety of quantitative measures from the attractor and used machine learning to classify each animal as either wild-type or mutant. In this work, we take the attractor images and use these as input to a deep learning algorithm in order to perform the same classification. As there is only data available from 42 mice, we use a transfer learning approach in which a network that has been pretrained on millions of images is used as a starting point and the last few layers are changed in order to fine tune the network for the attractor images.The results for the transfer learning approach are not as good as for the manual features, which is not too surprising as the networks have not been trained on attractor images. However, this approach shows the potential for using deep learning for classification of attractor images.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Mathematics
Authors :	Name Email ORCID Aston, Philip P.Aston@surrey.ac.uk Lyle, Jane j.lyle@surrey.ac.uk Bonet-Luz, Esther Huang, Christopher Zhang, Yanmin Jeevaratnam, Kamalan k.jeevaratnam@surrey.ac.uk Nandi, Manasi
Date :	July 2020
Funders :	European Union's Horizon 2020
DOI :	10.23919/CinC49843.2019.9005823
Grant Title :	EMPIR programme
Copyright Disclaimer :	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords :	Electrocardiography; Manuals; Feature extraction; Machine learning; Mice; Lead
Additional Information :	Printed proceedings published by Curran Associates Inc. at http://www.proceedings.com/52968.html
Depositing User :	James Marshall
Date Deposited :	04 Feb 2020 14:05
Last Modified :	02 Oct 2020 17:20
URI:	http://epubs.surrey.ac.uk/id/eprint/853644

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