On the Effectiveness of Deep Representation Learning: The Atrial Fibrillation Case
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...
Paper Details
Title
On the Effectiveness of Deep Representation Learning: The Atrial Fibrillation Case
Published Date
Nov 1, 2019
Journal
Volume
52
Issue
11
Pages
18 - 29
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