title: 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.
authors.affiliation: Department of Biomedical Engineering, Rutgers University Piscataway, NJ 08854, USA.
authors.forename: D J
authors.initials: DJ
authors.lastname: Curcie
authors.forename: W
authors.initials: W
authors.lastname: Craelius
mesh_terms: D006331:Heart Diseases / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted