New Bayesian discriminator for detection of XXXX .

@entity1 @entity956 @entity955 @entity4710 @entity943
BACKGROUND: Accurate, rapid detection of @entity4710 has important implications in the use of implantable devices for treatment of @entity943 . Currently available detection algorithms for @entity4710 , which use the single-index method, have limited sensitivity and specificity. METHODS AND RESULTS: In this study, we evaluated the performance of a new Bayesian discriminator algorithm in the detection of @entity955 ( @entity955 ), @entity956 (SR). Bipolar recording of 364 rhythms ( @entity955 =156, AFL=88, SR=120) at the high right atrium were collected from 20 @entity1 who underwent electrophysiological procedures. After initial signal processing, a column vector of 5 features for each rhythm were established, based on the regularity, rate, energy distribution, percent time of quiet interval, and baseline reaching of the rectified autocorrelation coefficient functions. Rhythm identification was obtained by use of Bayes decision rule and assumption of Gaussian distribution. For the new Bayesian discriminator, the overall sensitivity for detection of SR, @entity955 , and AFL was 97%, 97%, and 94%, respectively; and the overall specificity for detection of SR, @entity955 , and AFL was 98%, 98%, and 99%, respectively. The overall accuracy of detection of SR, @entity955 , and AFL was 98%, 97% and 98%, respectively. Furthermore, sensitivity, specificity, and accuracy of this algorithm were not affected by a range of white Gaussian noises with different intensities. CONCLUSIONS: This new Bayesian discriminator algorithm, based on Bayes decision of multiple features of atrial electrograms, allows rapid on-line and accurate (98%) detection of @entity955 with robust anti-noise performance.
