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Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring From Ballistocardiograms.
- Source :
- IEEE Transactions on Biomedical Engineering; Nov2018, Vol. 65 Issue 11, p2634-2648, 15p
- Publication Year :
- 2018
-
Abstract
- A multiple instance dictionary learning approach, dictionary learning using functions of multiple instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a “heartbeat concept” that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept obtained by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189294
- Volume :
- 65
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 132478592
- Full Text :
- https://doi.org/10.1109/TBME.2018.2812602