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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, METHODS, RESULTS. Meshes: Algorithms, Atrial Fibrillation, Diagnosis, Computer-Assisted, Diagnosis, Differential, Electrocardiography, Humans, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted.
user
Is optimization of novel spectral estimator for fractionated electrogram analysis helpful to discern atrial fibrillation type? Context: Paroxysmal versus persistent atrial fibrillation (AF) can be distinguished based on differences in the spectral parameters of fractionated atrial electrograms. Maximization of these differences would improve characterization of the arrhythmogenic substrate. A novel spectral estimator (NSE) has been shown previously to provide greater distinction in AF spectral parameters as compared with the Fourier transform estimator. Herein, it is described how the differences in NSE spectral parameters can be further improved. In 10 persistent and 9 paroxysmal AF patients undergoing electrophysiologic study, fractionated electrograms were acquired from the distal bipolar ablation electrode. A total of 204 electrograms were recorded from the pulmonary vein (PV) antra and from the anterior and posterior left atrial free wall. The following spectral parameters were measured: the dominant frequency (DF), which reflects local activation rate, the DF amplitude (DA), and the mean spectral profile (MP), which represents background electrical activity. To optimize differences in parameters between paroxysmal versus persistent AF patients, the NSE was varied by selectively removing subharmonics, using a threshold. The threshold was altered in steps to determine the optimal subharmonics removal. At the optimal threshold level, mean differences in persistent versus paroxysmal AF spectral parameters were: ΔDA=+0.371 mV, ΔDF=+0.737 Hz, and ΔMP=-0.096 mV. When subharmonics were not removed, the differences were substantially less: ΔDA=+0.301 mV, ΔDF=+0.699 Hz, and ΔMP=-0.063 mV.
assistant
NSE optimization produces greater spectral parameter difference between persistent versus paroxysmal AF data. Quantifying spectral parameter differences can be assistive in characterizing the arrhythmogenic substrate. Final Decision: yes