Digital Spectral Analysis With Applications Marple Pdf Download
all the patients that were included in this study had chronic af. we have tested the ability of ar model-based spectral analysis to track the df in patients with paroxysmal af (persaf) and patients with nonpersaf.
autoregressive (ar) model-based spectral estimation techniques, with emphasis on selection of appropriate sampling rate and ar model order, were implemented to generate high-density 3d df maps of atrial electrograms (aegs) in persistent atrial fibrillation (persaf). for each patient, 2048 simultaneous aegs were recorded for 20.478s-long segments in the left atrium (la) and exported for analysis, together with their anatomical locations. 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.
we fitted a linear mixed effects model to the data, which included as random effects: (1) patient, (2) the interaction patient and down sampling (ds) in the time domain of the aegs with different sampling frequencies (down sampled signal) and (3) the interaction between patient, down sampled signal, and df estimation using each of the ar spectral techniques (yule-walker, covariance, modified covariance and burg methods). as fixed effects we included: (1) df estimation using each of the ar spectral techniques, (2) ds of the aegs, and (3) the interaction between ar spectral estimation techniques and ds of the aegs. these analyses tested whether any differences between the df using the ar spectral estimation techniques varied by sampling frequency, while properly allowing for the nested structure of the data (i.e. where the df calculated by each ar spectral technique was measured at each sampling frequency in each patient). the results were compared with those obtained using the fft-based approach by the percentage of df agreement. the analysis was performed using the nlme [ 24 ] package in r [ 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.
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