  A multiple instance learning (MIL) method, extended Function of Multiple
Instances ($e$FUMI), is applied to ballistocardiogram (BCG) signals produced by
a hydraulic bed sensor. The goal of this approach is to learn a personalized
heartbeat "concept" for an individual. This heartbeat concept is a prototype
(or "signature") that characterizes the heartbeat pattern for an individual in
ballistocardiogram data. The $e$FUMI method models the problem of learning a
heartbeat concept from a BCG signal as a MIL problem. This approach elegantly
addresses the uncertainty inherent in a BCG signal e. g., misalignment between
training data and ground truth, mis-collection of heartbeat by some
transducers, etc. Given a BCG training signal coupled with a ground truth
signal (e.g., a pulse finger sensor), training "bags" labeled with only binary
labels denoting if a training bag contains a heartbeat signal or not can be
generated. Then, using these bags, $e$FUMI learns a personalized concept of
heartbeat for a subject as well as several non-heartbeat background concepts.
After learning the heartbeat concept, heartbeat detection and heart rate
estimation can be applied to test data. Experimental results show that the
estimated heartbeat concept found by $e$FUMI is more representative and a more
discriminative prototype of the heartbeat signals than those found by
comparison MIL methods in the literature.
