An effective heart rate variability processor design based on time-frequency analysis algorithm using windowed Lomb periodogram

In this paper, a system for time-frequency analysis of heart rate variability (HRV) using a fast windowed Lomb periodogram is proposed. Time-frequency analysis of HRV is achieved through a de-normalized fast Lomb periodogram with a sliding window configuration. The Lomb time-frequency distribution (TFD) is suited for spectral analysis of unevenly spaced data and has been applied to the analysis of HRV. The system has been implemented in hardware as an HRV processor and verified on FPGA. Simulations show that the proposed Lomb TFD is able to achieve better frequency resolution than short-time Fourier transform of the same hardware size. The proposed system is suitable for portable monitoring devices and as a biomedical signal processor on an system-on-chip (SOC) design.

[1]  Jennie Malboeuf Algorithm , 1994, Neurology.

[2]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[3]  R. Roine,et al.  Arrhythmias and heart rate variability during and after therapeutic hypothermia for cardiac arrest* , 2009, Critical care medicine.

[4]  Lionel Tarassenko,et al.  Quantifying errors in spectral estimates of HRV due to beat replacement and resampling , 2005, IEEE Transactions on Biomedical Engineering.

[5]  E. Sforza,et al.  Predicting sleep apnoea syndrome from heart period: a time-frequency wavelet analysis , 2003, European Respiratory Journal.

[6]  Shing-Chow Chan,et al.  Robust adaptive Lomb periodogram for time-frequency analysis of signals with sinusoidal and transient components , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[7]  Munna Khan,et al.  A Review of Measurement and Analysis of Heart Rate Variability , 2009, 2009 International Conference on Computer and Automation Engineering.

[8]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[9]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[10]  W. Press,et al.  Fast algorithm for spectral analysis of unevenly sampled data , 1989 .

[11]  Patrick E. McSharry,et al.  Method for generating an artificial RR tachogram of a typical healthy human over 24-hours , 2002, Computers in Cardiology.

[12]  K. Vinod,et al.  Sampling frequency of the RR interval time series for spectral analysis of heart rate variability , 2004, Journal of medical engineering & technology.

[13]  George B. Moody,et al.  Spectral analysis of heart rate without resampling , 1993, Proceedings of Computers in Cardiology Conference.

[14]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[15]  Homer Nazeran,et al.  EEG and HRV signal features for automatic sleep staging and apnea detection , 2010, 2010 20th International Conference on Electronics Communications and Computers (CONIELECOMP).

[16]  Mika P. Tarvainen,et al.  Software for advanced HRV analysis , 2004, Comput. Methods Programs Biomed..

[17]  A. G. Ananth,et al.  A Review of Heart Rate Variability and It ‟ s Association with Diseases , 2012 .

[18]  P.P. Domitrovich A graphical user interface for the study of heart rate variability , 2007, 2007 Computers in Cardiology.

[19]  M. Aboy,et al.  Lomb-Wech periodogram for non-uniform sampling , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  N. Lomb Least-squares frequency analysis of unequally spaced data , 1976 .

[21]  Ryosuke Tsuruta,et al.  Real-time monitoring of heart rate variability in critically ill patients. , 2010, Journal of critical care.