Background Areas with high frequency activity within the atrium are thought to be drivers of the rhythm in patients with atrial fibrillation (AF) and ablation of these areas seems to be an effective therapy in eliminating DF gradient and restoring sinus rhythm. of atrial electrograms (AEGs) in ABT 492 meglumine manufacture persistent atrial fibrillation (persAF). For each patient, 2048 simultaneous AEGs were recorded for 20.478?s-long segments in the left ABT 492 meglumine manufacture atrium (LA) and exported for analysis, with their anatomical locations together. After the DFs were identified using AR-based spectral estimation, they were colour coded to produce sequential 3D DF maps. These maps were systematically compared with maps found using the Fourier-based approach. Results 3D DF maps can be obtained using AR-based spectral estimation after AEGs downsampling (DS) and the resulting maps are very similar to those obtained using FFT-based spectral estimation (mean 90.23?%). There were no significant differences between AR techniques (p?=?0.62). The processing time for AR-based approach was considerably shorter (from 5.44 to 5.05?s) when lower sampling frequencies and model order values were used. Higher levels of DS presented higher rates of DF agreement (sampling frequency of 37.5?Hz). Conclusion We have demonstrated the feasibility of using AR spectral estimation methods for producing 3D DF maps and characterised their differences to the maps produced using the FFT technique, offering an alternative approach for 3D DF computation in human persAF studies. with coefficients [as given by Eq.?(2) [20C22], is the variance of the driving white noise is the sampling period. {To estimate the AR coefficients {is the number of samples [14,|To estimate the AR coefficients is the true LAMC1 number of samples [14, 20, 22]. To estimate the coefficients and variance, the method first requires the estimation of the first model order AR process parameters (Eq.?7). This is then followed by a recursive implementation for obtaining successively higher model orders from [20]. -?lag products) for each window location and the variance are calculated using the following equations [20C22]: coefficient is estimated after the =?1,?2,? ,? is the optimum model order, is the white noise variance and is the number of samples of the data used. [24] package in [25]. Mixed model ANOVA was used to study the effect of downsampling factor and DF estimation for the AR spectral techniques. P-values less than 0.05 were considered statically significant. Results Eight male patients with symptomatic drug-refractory persAF were included in this study (mean age of 47??4?years). Patients presented a history of persAF episodes of 34??9?months with a moderated dilated LA (48??2?mm) and left ventricle ejection fraction above 55?% (5 out of 8). Patient characteristics are summarized in Table?1 and represent largely what we might expect for a persAF population undergoing catheter ablation. Figure?2 shows an AEG originally sampled at 1200?Hz with a total of 8192 samples (upper trace). A re-sampled signal with downsampling of 32 times (new Fs?=?37.5?Hz) is shown on the second trace. Spectral analysis performed using FFT (for the original signal) and AR Yule-Walker (for the downsampled signal) illustrates that the DF of the signal can still be estimated after downsampling using the AR approach. Zero padding of 4 times resulted in a total of 32,768 samples produced a frequency step of 0.0366?Hz for the FFT approach. The PSD using AR Yule-Walker model was applied for two different AR model orders (50 and 18) and since the AR spectrum is continuous, the number of spectral samples was chosen so that frequency intervals were the same as applied by using the FFT approach using the original sampling frequency (Fs?=?1200?Hz). Selection of model order Model orders were estimated for different Fs and the results are illustrated in Fig.?3. Figure?3a shows the cumulative histogram of the AEGs (in ?%) against model order for one patient whose original signals were ABT 492 meglumine manufacture downsampled to 75?Hz. The model order value chosen was 24. Figure?3b shows the average behaviour of the estimated best AR model order for all patients for different downsampling strategies. The model order values for each Fs are.