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

TY - CHAP

T1 - Deep Learning Applied to Attractor Images Derived from ECG Signals for Detection of Genetic Mutation

AU - Aston, Philip J.

AU - Lyle, Jane V.

AU - Bonet-Luz, Esther

AU - Huang, Christopher L.H.

AU - Zhang, Yanmin

AU - Jeevaratnam, Kamalan

AU - Nandi, Manasi

PY - 2019/9

Y1 - 2019/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85081128393&partnerID=8YFLogxK

U2 - 10.23919/CinC49843.2019.9005823

DO - 10.23919/CinC49843.2019.9005823

M3 - Conference paper

AN - SCOPUS:85081128393

T3 - Computing in Cardiology

BT - 2019 Computing in Cardiology, CinC 2019

PB - IEEE Computer Society

T2 - 2019 Computing in Cardiology, CinC 2019

Y2 - 8 September 2019 through 11 September 2019

ER -