Recognition of individual heart rate patterns with cepstral vectors

Abstract. Heart rate patterns may contain diagnostic as well as forensic information. To test these possibilities, individual heart rate patterns were represented as heart-rate cepstral vectors (HRCVs) computed in 12 dimensions via linear predictive coding (LPC) of brief segments of heart rate. A library of codebook vectors was computed for 12 cardiac patients from a standard ECG database. Statistical classification of subjects was based on the minimal weighted distances between test and codebook vectors. Weights were based on the ratio of inter- to intrasubject variances of their cepstral coefficients. Results showed that: (1) HRCV coefficients adequately reproduced the HRV spectrum, and (2) HRCV distances could be used to identify individuals within the group with a reliability of 93%. Thus, heart rate variations are an individual characteristic that can be represented as a single 12-dimensional vector.

[1]  Aaron E. Rosenberg,et al.  On the use of instantaneous and transitional spectral information in speaker recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[3]  M.G. Bellanger,et al.  Digital processing of speech signals , 1980, Proceedings of the IEEE.

[4]  R. Cohen,et al.  An Efficient Algorithm for Spectral Analysis of Heart Rate Variability , 1986, IEEE Transactions on Biomedical Engineering.

[5]  J. Fleiss,et al.  Frequency Domain Measures of Heart Period Variability and Mortality After Myocardial Infarction , 1992, Circulation.

[6]  L Glass,et al.  Time series analysis of complex dynamics in physiology and medicine. , 1993, Medical progress through technology.

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

[8]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[9]  D. Adam,et al.  Assessment of autonomic function in humans by heart rate spectral analysis. , 1985, The American journal of physiology.

[10]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[11]  B.S. Atal,et al.  Automatic recognition of speakers from their voices , 1976, Proceedings of the IEEE.

[12]  William Craelius,et al.  Rhythm Analysis of Arterial Blood Pressure , 1986, IEEE Transactions on Biomedical Engineering.

[13]  T. Kuo,et al.  The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition. , 1995, IEEE transactions on bio-medical engineering.

[14]  JunichiroHayano,et al.  Respiratory Sinus Arrhythmia , 1996, Encyclopedia of Evolutionary Psychological Science.

[15]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[16]  W Craelius,et al.  Signal processing options for detecting conduction abnormalities in ischemic ventricles. , 1987, Journal of electrocardiology.

[17]  Ronald W. Schafer,et al.  Digital Processing of Speech Signals , 1978 .

[18]  R.M. Rangayyan,et al.  Analysis of knee vibration signals using linear prediction , 1992, IEEE Transactions on Biomedical Engineering.

[19]  D. O'Shaughnessy,et al.  Speaker recognition , 1986, IEEE ASSP Magazine.

[20]  A. Noll.  Cepstrum pitch determination. , 1967, The Journal of the Acoustical Society of America.

[21]  R. I. Kitney,et al.  The beat-by-beat investigation of cardiovascular function : measurement, analysis, and applications , 1987 .

[22]  S Cerutti,et al.  Heart rate variability as an index of sympathovagal interaction after acute myocardial infarction. , 1987, The American journal of cardiology.

[23]  Joseph P. Zbilut,et al.  Dimensional analysis of heart rate variability in heart transplant recipients , 1988 .