Circadian variation in dominant atrial fibrillation frequency in persistent atrial fibrillation

Abstract

Circadian variation in atrial fibrillation (AF) frequency is explored in this paper by employing recent advances in signal processing. Once the AF frequency has been estimated and tracked by a hidden Markov model approach, the resulting trend is analyzed for the purpose of detecting and characterizing the presence of circadian variation. With cosinor analysis, the results show that the short-term variations in the AF frequency exceed the variation that may be attributed to circadian. Using the autocorrelation method, circadian variation was found in 13 of 18 ambulatory ECG recordings (Holter) acquired from patients with long-standing persistent AF. Using the ensemble correlation method, the highest AF frequency usually occurred during the afternoon, whereas the lowest usually occurred during late night. It is concluded that circadian variation is present in most patients with long-standing persistent AF though the short-term variation in the AF frequency is considerable and should be taken into account.

Signal Quality Assessment of F-waves in Atrial Fibrillation

“…Since f-waves may exhibit considerable variation in AF frequency [7], variation in ω 0 is allowed by dividing x into K overlapping subsegments and determining a local frequency estimateω 0,k , which may deviate a maximum of ∆ω 0 fromω 0 , for each subsegment, using the same maximum likelihood approach as before.…”

Applications of Signal Analysis to Atrial Fibrillation

“…The DAF is known to be influenced by autonomic modulation and its variations over time have been studied in terms of the effects of parasympathetic and sympathetic stimulation as well as with respect to circadian rhythm. It has been shown that AF frequency decreases during the night and increases in the morning [40].…”

Generation of realistic atrial to atrial interval series during atrial fibrillation

“…(3) Statistical parameters derived from AA series during AF have been assumed to be time invariant. However, many authors have reported temporal variations in AA interval series which can be gradual or sudden [5,27,32]. These variations could be emulated by concatenating generated AA interval series with different statistical parameters.…”