التفاصيل البيبلوغرافية
| العنوان: |
Semi-Supervised Analysis of the Electrocardiogram Using Deep Generative Models. |
| المؤلفون: |
Rasmussen SM, Jensen MEK, Meyhoff CS, Aasvang EK, Slrensen HBD |
| المصدر: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 1124-1127. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: [IEEE] Country of Publication: United States NLM ID: 101763872 Publication Model: Print Cited Medium: Internet ISSN: 2694-0604 (Electronic) Linking ISSN: 23757477 NLM ISO Abbreviation: Annu Int Conf IEEE Eng Med Biol Soc Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: [Piscataway, NJ] : [IEEE], [2007]- |
| مواضيع طبية MeSH: |
Atrial Fibrillation*/diagnosis , Neural Networks, Computer*, Electrocardiography ; Humans |
| مستخلص: |
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. |
| تواريخ الأحداث: |
Date Created: 20211211 Date Completed: 20211228 Latest Revision: 20211228 |
| رمز التحديث: |
20231215 |
| DOI: |
10.1109/EMBC46164.2021.9629915 |
| PMID: |
34891485 |
| قاعدة البيانات: |
MEDLINE |
Full Text Finder