**6. Conclusions**

16 Atrial Fibrillation

AF.

Although previous algorithms reached a high classification ability in long heart rate records, their performance was notably reduced for sort data sets. Similar behavior was appreciated for MSE measures [23]. Thus, in long RR time series, when matches abound, entropy metrics can distinguish AF well from SR [23]. However, there is a challenge, though, in assuring a sufficient number of matches when the data sets are short [82]. Thereby, Lake and Moorman [82] optimized the SampEn, developing general methods for the rational selection of the template length *m* and the tolerance matching *r*. The major innovation was to allow *r* to vary so that sufficient matches are found for confident entropy estimation, with conversion of the final probability to a density by dividing by the matching region volume, 2*rm*. The optimized SampEn estimate and the mean heart beat interval each contributed to accurate detection of AF in as few as 12 heartbeats. The final algorithm, called the coefficient of SampEn (COSEn), provided high degrees of accuracy in distinguishing AF from SR in 12-beat calculations performed hourly. The most common errors were atrial or ventricular ectopy, which increased entropy despite SR, and atrial flutter, which can have low or high entropy

Finally, Segerson et al [83] showed that measures of short-term HRV during SR correlate with measures of cycle length entropy during paroxysms of AF. More precisely, two measures of short-term HRV in SR, such as the root mean square of the differences between consecutive normal intervals (RMSSD) and the inter-beat correlation coefficient (ICC), correlated with well-established measurements of entropy during AF, such as ShEn and ApEn. Recognizing that RMSSD and ICC are known measures of parasympathetic function in SR, authors' claimed that their results suggest a role for vagal regulation of cycle length entropy during

During AF, the fibrillatory impulses continuously bombard and penetrate the AV node to varying degrees (concealed conduction), creating appreciable variability on the AV nodal refractoriness [84]. Since the AV node is the structure responsible for the conduction of atrial impulses to the ventricles, the strategy of rate control during AF deals with efforts to utilize and adjust the propagation properties of the node [84]. Characteristics of AV conduction have been widely investigated during the last years by using different techniques and, especially, PP analysis. In this graph, it is possible to identify the lower envelope, which have been used to characterize the functional refractory period and the rate dependence of AV node conduction [85, 86]. In addition, the degree of scatter of the PP, calculated as the root mean square difference of each RR-interval and the lower envelope, has been presented

By applying PP analysis to 24-h Holter recordings of 48 patients with chronic AF, it was suggested that both AV node refractoriness and the degree of concealed AV conduction during AF may show a circadian rhythm, but also that circadian rhythms may be attenuated in patients with heart failure [86]. These findings point to the possibility of obtaining information concerning altered autonomic control of the RR intervals in patients with AF

On the other hand, Oka et al [87] showed that for some PPs computed from 24-h recordings exhibited two separate sectors of RR intervals. When this occurred, the RR-interval histogram disclosed a bimodal distribution in approximately 40% of patients. It should be noted,

states depending on dynamics of atrioventricular conduction.

**5.4. HRV analysis to characterize the AV node**

as a measure of concealed conduction in the AV node [86].

(and heart failure or other disease) with this simple technique.

Different pathophysiologic processes control heart's behavior during AF in opposite directions, making difficult the understanding of the mechanisms provoking onset, maintenance and termination of this arrhythmia. Nonetheless, the state of the art summarized in the present work suggests that the use of modern methods of nonlinear analysis can facilitate the understanding of cardiovascular function during AF, in a complementary way to the traditional linear techniques. Thus, nonlinear indices have provided robust estimates of AF organization able to reveal information about several aspects of the arrhythmia. In this respect, clinically relevant information related to the arrhythmia state and its progression after pharmacological and electrical cardioversion has been shown by different researches. In addition, nonlinear analysis has shown to play an important role in the analysis of the ventricular response provoked by the arrhythmia, thus being able to reflect cardiovascular autonomic regulation changes before, during and after AF onset.
