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On the Effectiveness of Deep Representation Learning: the Atrial Fibrillation Case
- Source :
- Computer (Long Beach Calif)
- Publication Year :
- 2019
-
Abstract
- The automatic and unsupervised analysis of biomedical time series is of primary importance for diagnostic and preventive medicine, enabling fast and reliable data processing to reveal clinical insights without the need for human intervention. Representation learning (RL) methods perform an automatic extraction of meaningful features that can be used, e.g., for a subsequent classification of the measured data. The goal of this study is to explore and quantify the benefits of RL techniques of varying degrees of complexity, focusing on modern deep learning (DL) architectures. We focus on the automatic classification of atrial fibrillation (AF) events from noisy single-lead electrocardiographic signals (ECG) obtained from wireless sensors. This is an important task as it allows the detection of sub-clinical AF which is hard to diagnose with a short in-clinic 12-lead ECG. The effectiveness of the considered architectures is quantified and discussed in terms of classification performance, memory/data efficiency and computational complexity.
- Subjects :
- Atrial Fibrillation, ECG analysis, deep learning
General Computer Science
business.industry
Computer science
Feature extraction
deep learning
030204 cardiovascular system & hematology
Machine learning
computer.software_genre
Article
03 medical and health sciences
Kernel (linear algebra)
0302 clinical medicine
Atrial Fibrillation
ECG analysis
Unsupervised learning
030212 general & internal medicine
Artificial intelligence
business
computer
Feature learning
Subjects
Details
- ISSN :
- 00189162
- Volume :
- 52
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- Computer
- Accession number :
- edsair.doi.dedup.....5a1914b0e8dd860ff4f614fabd9465d4