Semi-Supervised Analysis of the Electrocardiogram Using Deep Generative Models

Søren Møller Rasmussen, Malte E. K. Jensen, Christian S. Meyhoff, Eske K. Aasvang, Helge B. D. Sørensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Deep learning has gained increased impact on medical classification problems in recent years, with models being trained to high performance. However neural networks require large amounts of labeled data, which on medical data can be expensive and cumbersome to obtain. We propose a semi-supervised setup using an unsupervised variational autoencoder combined with a supervised classifier to distinguish between atrial fibrillation and non-atrial fibrillation using ECG records from the MIT-BIH Atrial Fibrillation Database. The proposed model was compared to a fully-supervised convolutional neural network at different proportions of labeled and unlabeled data (1%-50% labeled and the remaining unlabeled). The results demonstrate that the semi-supervised approach was superior to the fully-supervised, from using as little as 5% (5,594 samples) labeled data with an accuracy of 98.7%. The work provides proof of concept and demonstrates that the proposed semisupervised setup can train high accuracy models at low amounts of labeled data.
Original languageEnglish
Title of host publicationProceedings of 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
PublisherIEEE
Publication date2021
Pages1124-1127
ISBN (Print)978-1-7281-1180-3
DOIs
Publication statusPublished - 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Virtual event
Duration: 1 Nov 20215 Nov 2021

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
LocationVirtual event
Period01/11/202105/11/2021
SeriesAnnual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference
ISSN2694-0604

Keywords

  • Atrial Fibrillation
  • Electrocardiography
  • Humans
  • Neural Networks, Computer

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