Atrial flutter and atrial tachycardia detection using Bayesian approach with high resolution time-frequency spectrum from ECG recordings

Contemporary methods of atrial flutter (AFL), atrial tachycardia (AT), and atrial fibrillation (AF) monitoring, although superior to the standard 12-lead ECG and symptom-based monitoring, are unable to accurately discriminate between AF, AFL and AT. Thus, there is a need to develop accurate, automated, and comprehensive atrial arrhythmia detection algorithms using standard ECG recorders. To this end, we have developed a sensitive and real-time realizable algorithm for accurate AFL and AT detection using any standard electrocardiographic recording. Our novel method for automatic detection of atrial flutter and atrial tachycardia uses a Bayesian approach followed by a high resolution time-frequency spectrum. We find the TQ interval of the electrocardiogram (ECG) corresponding to atrial activity by using a particle filter (PF), and analyze the atrial activity with a high resolution time-frequency spectral method: variable frequency complex demodulation (VFCDM). The rationale for using a high-resolution time-frequency algorithm is that our approach tracks the time-varying fundamental frequency of atrial activity, where AT is within 2.0-4.0 Hz, AFL is within 4.0-5.3 Hz and NSR is found at frequencies less than 2.0 Hz. For classifications of AFL (n = 22), AT (n = 10) and normal sinus rhythms (NSR) (n = 29), we found that our approach resulted in accuracies of 0.89, 0.87 and 0.91, respectively; the overall accuracy was 0.88.