Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes

Abstract

Two expert cardiologists assessed the realism of simulated signals relative to real ECG signals, both in sinus rhythm and AF. The cardiologists identified the correct rhythm in all cases, and considered two-thirds of the simulated signals as realistic. The proposed model was also investigated by evaluating the performance of two AF detectors which explored either rhythm only or both rhythm and morphology. The results show that detection performance is strongly dependent on AF episode duration, and, consequently, demonstrate that the model can play a significant role in the investigation of detector properties.

ECG-Derived Respiratory Rate in Atrial Fibrillation

“…The f-wave variance σ 2 f is estimated from the observed signal by computing the sample variance of the concatenated TQ intervals of all beats, following bandpass filtering in the band [0. 5,30] Hz to preserve f-wave related frequency components and reduce the influence of noise [27]. The QRS variance σ 2 QRS (n) is estimated bŷ…”

“…To shed light on performance, a model for simulating 12-lead ECG signals in AF is employed [30]. Briefly, the signals are generated by simulating QRST complexes whose morphological variability accounts for respiratory influence (angular variation around each lead), real, extracted f-waves, and various types of noise, see Appendix for further details.…”

“…The multi-lead ECG model in [32] is used for simulating QRST complexes with different morphologies, see [30] for further details. A synthesized orthogonal three-lead VCG signal u QRS (n) (3x1) is constructed by concatenating a single QRST complex for each lead (X, Y, Z) until the desired length is attained.…”

“…3) Noise: A noise component u n (n) (12 × 1) is composed of two types of noise frequently encountered in ambulatory recordings, muscle noise and electrode movement artifacts extracted from the MIT BIH Noise Stress Test Database. Noise with 20 µV RMS is included to better mimic real ECGs [30]. 4) Respiratory Influence: In [34], see also [35], it was proposed that the angular variation around each axis (lead l) is proportional to the amount of air in the lungs during the p:th respiratory cycle.…”

Support system for classification of beat-to-beat arrhythmia based on variability and morphology of electrocardiogram

“…Conventionally, the R-R interval is used to data extraction from the ECG signal in order to diagnose different types of arrhythmia [3][4][5][6][7][8]. However, the analysis of the R-R intervals is not able to measure changes on other ECG waves, such as the distortions on P wave for atrial fibrillation (AF) [9][10][11][12]. Thereby, some studies segment the ECG signal [13][14][15][16][17].…”