Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances

TL;DR

This paper introduces an extended multiple instance learning method, eFUMI, to learn personalized heartbeat signatures from ballistocardiogram signals, improving heartbeat detection and heart rate estimation accuracy.

Contribution

The paper presents a novel application of eFUMI to BCG signals, effectively handling uncertainties and misalignments to learn personalized heartbeat concepts.

Findings

eFUMI outperforms existing MIL methods in heartbeat characterization

Learned heartbeat concepts are more representative and discriminative

Improved accuracy in heartbeat detection and heart rate estimation

Abstract

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.