Semi-Supervised Analysis of the Electrocardiogram Using Deep Generative Models
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Final published version, 2.13 MB, PDF document
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.
|Title of host publication||2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021|
|Publication status||Published - 2021|
|Event||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico|
Duration: 1 Nov 2021 → 5 Nov 2021
|Conference||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021|
|Periode||01/11/2021 → 05/11/2021|
|Sponsor||Elsevier, The Institution of Engineering and Technology (IET)|
|Series||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
© 2021 IEEE.