  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
found by DL-FUMI is an effective heartbeat prototype and achieves superior
performance over comparison algorithms.
