Ecg Arrhythmia Classification Using Transfer Learning From 2- Dimensional Deep Cnn Features

Keywords

DenseNet; Electrocardiogram; Feature extraction; Machine learning; Neural network; Transfer learning

Abstract

Due to the recent advances in the area of deep learning, it has been demonstrated that a deep neural network, trained on a huge amount of data, can recognize cardiac arrhythmias better than cardiologists. Moreover, traditionally feature extraction was considered an integral part of ECG pattern recognition; however, recent findings have shown that deep neural networks can carry out the task of feature extraction directly from the data itself. In order to use deep neural networks for their accuracy and feature extraction, high volume of training data is required, which in the case of independent studies is not pragmatic. To arise to this challenge, in this work, the identification and classification of four ECG patterns are studied from a transfer learning perspective, transferring knowledge learned from the image classification domain to the ECG signal classification domain. It is demonstrated that feature maps learned in a deep neural network trained on great amounts of generic input images can be used as general descriptors for the ECG signal spectrograms and result in features that enable classification of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in classifying near 7000 instances by ten-fold cross validation.

Publication Date

12-20-2018

Publication Title

2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/BIOCAS.2018.8584808

Socpus ID

85060859670 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85060859670

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