TY - JOUR
T1 - Compressed sensing technology-based spectral estimation of heart rate variability using the integral pulse frequency modulation model
AU - Chen, Szi Wen
AU - Chao, Shih Chieh
PY - 2014/5
Y1 - 2014/5
N2 - In this paper, a compressed sensing (CS)-based spectral estimation of heart rate variability (HRV) using the integral pulse frequency modulation (IPFM) model is introduced. Previous research in the literature indicated that the IPFM model is widely accepted as a functional description of the cardiac pacemaker, and thus, very useful in modeling the mechanism by which the autonomic nervous system modulates the heart rate (HR). On the other hand, recently CS becomes an emerging technology that has attracted great attention since it is capable of acquiring and reconstructing signals that are considered sparse or compressible, even when the number of measurements is small. Using the IPFM model, we here present a CS-based algorithm for deriving the amplitude spectrum of the modulating signal for HRV assessments. In fact, the application of the CS method into HRV spectral estimation is unprecedented. Numerical results produced by a real RR database of PhysioNet demonstrated that the proposed approach can robustly provide high-fidelity HRV spectral estimates, even under the situation of a degree of incompleteness in the RR data caused by ectopic or missing beats.
AB - In this paper, a compressed sensing (CS)-based spectral estimation of heart rate variability (HRV) using the integral pulse frequency modulation (IPFM) model is introduced. Previous research in the literature indicated that the IPFM model is widely accepted as a functional description of the cardiac pacemaker, and thus, very useful in modeling the mechanism by which the autonomic nervous system modulates the heart rate (HR). On the other hand, recently CS becomes an emerging technology that has attracted great attention since it is capable of acquiring and reconstructing signals that are considered sparse or compressible, even when the number of measurements is small. Using the IPFM model, we here present a CS-based algorithm for deriving the amplitude spectrum of the modulating signal for HRV assessments. In fact, the application of the CS method into HRV spectral estimation is unprecedented. Numerical results produced by a real RR database of PhysioNet demonstrated that the proposed approach can robustly provide high-fidelity HRV spectral estimates, even under the situation of a degree of incompleteness in the RR data caused by ectopic or missing beats.
KW - Compressed sensing (CS)
KW - heart rate variability (HRV)
KW - information theory
KW - sparse signals
KW - spectral estimation
UR - http://www.scopus.com/inward/record.url?scp=84900839045&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2013.2282307
DO - 10.1109/JBHI.2013.2282307
M3 - 文章
C2 - 24058040
AN - SCOPUS:84900839045
SN - 2168-2194
VL - 18
SP - 1081
EP - 1090
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 3
M1 - 6601707
ER -