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An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.
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