A study on stability analysis of atrial repolarization variability using ARX model in sinus rhythm and atrial tachycardia ECGs. (December 2016) - Sivaraman, J.; Uma, G.; Langley, P.; Umapathy, M.; Venkatesan, S.; Palanikumar, G.
Highlights: Analysis of atrial repolarization dynamics in ECG using system identification techniques The ARX model is used for studying the PTaI and PPI dynamics. Analysis of atrial repolarization variability and heart rate variability in healthy and AT subjects Abstract: Background: The interaction between the PTa and PP interval dynamics from the surface ECG is seldom explained. Mathematical modeling of these intervals is of interest in finding the relationship between the heart rate and repolarization variability. Objective: The goal of this paper is to assess the bounded input bounded output (BIBO) stability in PTa interval (PTaI) dynamics using autoregressive exogenous (ARX) model and to investigate the reason for causing instability in the atrial repolarization process. Methods: Twenty-five male subjects in normal sinus rhythm (NSR) and ten male subjects experiencing atrial tachycardia (AT) were included in this study. Five minute long, modified limb lead (MLL) ECGs were recorded with an EDAN SE-1010 PC ECG system. The number of minute ECGs with unstable segments ( Nus ) and the frequency of premature activation (PA) (i.e. atrial activation) were counted for each ECG recording and compared between AT and NSR subjects. Results: The instability in PTaI dynamics was quantified by measuring the numbers of unstable segments in ECG data for each subject. The unstable segments in the PTaI dynamics were associated with the frequency of PA. The presence of PA is not the only factor causing the instability in PTaI dynamics in NSR subjects, and it is found that the cause of instability is mainly due to the heart rate variability (HRV). Conclusion: The ARX model showed better prediction of PTa interval dynamics in both groups. The frequency of PA is significantly higher in AT patients than NSR subjects. A more complex model is needed to better identify and characterize healthy heart dynamics.