title: Heart beat characterization from ballistocardiogram signals using extended functions of multiple instances.
abstract: A multiple instance learning (MIL) method, extended Function of Multiple Instances (eFUMI), 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 eFUMI 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, eFUMI 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 eFUMI is more representative and a more discriminative prototype of the heartbeat signals than those found by comparison MIL methods in the literature.
authors.lastname: Changzhe Jiao
authors.forename: Princess
authors.initials: P
authors.lastname: Lyons
authors.forename: Alina
authors.initials: A
authors.lastname: Zare
authors.forename: Licet
authors.initials: L
authors.lastname: Rosales
authors.forename: Marjorie
authors.initials: M
authors.lastname: Skubic
mesh_terms: D001450:Ballistocardiography; D006339:Heart Rate; D006801:Humans; D000069550:Machine Learning; D014159:Transducers; D035501:Uncertainty