Autoregressive modeling and power spectral estimate of R-R interval time series in arrhythmic patients.
The signal constituted by the successive R-R intervals in the ECG tracing carries important information about the control mechanisms of heart rate. The present paper describes advanced methods of parameter extraction from the R-R duration time series which use autoregressive (AR) modeling and power spectral estimates applied to patients in the MIT-BIH arrhythmia data base. The described methodologies enhance information which characterize the most common rhythm disturbances (A-V block, bigeminy/trigeminy, atrial and ventricular flutter, atrial fibrillation, etc.). Important applications of such methods are in the area of the pathophysiological comprehension of cardiac rhythm control mechanisms in the research side and the classification of abnormal rhythms as well in the clinical side. A few examples from the data base are illustrated which show interesting properties of signal processing and classification in respect to the more traditional methods.
D001145:Arrhythmias, Cardiac / Q000503:physiopathology*; D001281:Atrial Fibrillation / Q000503:physiopathology; D001282:Atrial Flutter / Q000503:physiopathology; D004562:Electrocardiography; D006327:Heart Block / Q000503:physiopathology; D006339:Heart Rate; D006352:Heart Ventricles; D006801:Humans; D008962:Models, Theoretical; D012044:Regression Analysis; D013223:Statistics as Topic