New Bayesian discriminator for detection of atrial tachyarrhythmias.
BACKGROUND
Accurate, rapid detection of atrial tachyarrhythmias has important implications in the use of implantable devices for treatment of cardiac arrhythmia. Currently available detection algorithms for atrial tachyarrhythmias, which use the single-index method, have limited sensitivity and specificity.


RESULTS
In this study, we evaluated the performance of a new Bayesian discriminator algorithm in the detection of atrial fibrillation (AF), atrial flutter (AFL), and sinus rhythm (SR). Bipolar recording of 364 rhythms (AF=156, AFL=88, SR=120) at the high right atrium were collected from 20 patients 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, AF, and AFL was 97%, 97%, and 94%, respectively; and the overall specificity for detection of SR, AF, and AFL was 98%, 98%, and 99%, respectively. The overall accuracy of detection of SR, AF, 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 AF with robust anti-noise performance.
Xu
Weichao
W
Department of Electrical and Electronic Engineering, Queen Mary Hospital, The University of Hong Kong.
Tse
Hung-Fat
HF
Chan
Francis H Y
FH
Fung
Peter Chin Wan
PC
Lee
Kathy Lai-Fun
KL
Lau
Chu-Pak
CP
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis / Q000503:physiopathology; D001282:Atrial Flutter / Q000175:diagnosis / Q000503:physiopathology; D001499:Bayes Theorem; D022062:Electrophysiologic Techniques, Cardiac; D005260:Female; D006325:Heart Atria / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013610:Tachycardia / Q000175:diagnosis* / Q000503:physiopathology; D013616:Tachycardia, Sinus / Q000175:diagnosis / Q000503:physiopathology