Abstract:

A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DLFUMI), 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.

Links:

Citation:

C. Jiao, B. Su, P. Lyons, A. Zare, K. C. Ho and M. Skubic, "Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms,"IEEE Trans. Biomed. Eng., To Appear.

@Article{Jiao2017Multiple,
Title = {Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms},
Author = {Jiao, Changzhe and Su, Bo-Yu and Lyons, Princess and Zare, Alina and Ho, K. C. and Skubic, Marjorie},
Journal = {IEEE Trans. Biomed. Eng.},
Year = {2018},
volume = {65},
number = {11},
pages = {2634-2648},
month = {Nov.},
doi = {10.1109/TBME.2018.2812602},
}