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

Research output: Chapter in Book/Report/Conference proceedingConference paper

Philip J. Aston, Jane V. Lyle, Esther Bonet-Luz, Christopher L.H. Huang, Yanmin Zhang, Kamalan Jeevaratnam, Manasi Nandi

Original languageEnglish
Title of host publication2019 Computing in Cardiology, CinC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728169361
DOIs
PublishedSep 2019
Event2019 Computing in Cardiology, CinC 2019 - Singapore, Singapore
Duration: 8 Sep 201911 Sep 2019

Publication series

NameComputing in Cardiology
Volume2019-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

Conference2019 Computing in Cardiology, CinC 2019
CountrySingapore
CitySingapore
Period8/09/201911/09/2019

King's Authors

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.

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