Scanned ECG Arrhythmia Classification Using a Pre-trained Convolutional Neural Network as a Feature Extractor

Coenen, Frans ORCID: 0000-0003-1026-6649, Aldosari, Hanadi, Lip, Gregory ORCID: 0000-0002-7566-1626 and Zheng, Yalin ORCID: 0000-0002-7873-0922 (2022) Scanned ECG Arrhythmia Classification Using a Pre-trained Convolutional Neural Network as a Feature Extractor. .

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Official URL: http://dx.doi.org/10.1007/978-3-031-21441-7_5

Abstract

The classification of cardiovascular diseases using ECG data is considered. It is argued that to obtain a satisfactory classification features should be extracted from ECG images in their entirety, instead of translating the image into a 1D time series and only considering a small number of features as is the current common practise. The presented approach used a pre-trained Convolutional Neural Network (CNN) as a features extractor, followed by the application of T-distributed Stochastic Neighbour Embedding (T-SNE) to find the best discriminant features to perform ECG classification. The motivation using a pre-trained CNN model is that available ECG data sets tend to be limited in size; typically insufficient for training a bespoke deep learning model for feature extraction. Using a pre-trained CNN this challenge can be addressed. The features were extracted from the fully connected layers immediately preceding the softmax layer. The use of several pre-trained CNNs is reported on: VGG16, InceptionV3, and ResNet50. The operation of the proposed approach was also compared with recent relevant published approaches. A best AUC value of 0.960 was produced using the proposed approach; while the best alternative approach, out of those considered, produced an AUC of 0.932.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	ECG Classification, Convolutional neural networks, SVM Classifier, KNN Classifier
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	15 Mar 2023 10:20
Last Modified:	20 Nov 2023 15:22
DOI:	10.1007/978-3-031-21441-7_5
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169059