# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Univariate Data Analysis on RR Time Series
ROC curve analysis allowed to fix an optimal threshold for AF detection, which yields good sensitivity and specificity for episodes of 32 RR time series (94.4% and 92.6%, respectively), 64 (95.8% and 94.3%), and 128 (95.9% and 95.4%). [PERSON] et al. [28] have investigated two RR irregularity characteristics for AF detection in ECG: Normalized absolute deviation of RR intervals and normalized absolute difference between successive RR intervals. Some constraints for RR intervals have been added before the calculation of these parameters. ROC results showed 87.33% and 89.33% as overall of sensitivities of three AF datasets (AFDB, MIT-BIH Arrhythmia Database and Drager Atrial Fibrillation Database). In another paper, [PERSON] and [PERSON] [29] have compared two probability density functions (Gaussian and Laplace) to model the histograms of RR intervals. They have used Neyman-Pearson (NP) detection approach to find out suitable criteria for the arrhythmia detection. Indeed, Gaussian probability density function assumption leads to a simple NP detection criterion reduced to variance test and absolute deviation [30]. Analysis has been done on windows of 30 heartbeats. Since both detection criteria are sensitive to noise measurement, they added a constraint on the absolute value of the RR time series differences and excluded those greater than a fixed threshold. RR intervals bounded by Ventricular Beats have also been excluded. These approaches did not outperform their previous study as it reached 88% and 89.33%as overall of sensitivities of the same AF datasets for Gaussian and Laplace probability density functions, respectively. [PERSON] et al. [31] conceived a low complexity structure to detect AF arrhythmia. This method involved different blocks including preprocessing (3-point median filter), bigeminal suppression (measurement of inter-beats irregularity), data fusion, decision function, and finally detection of an appropriate threshold (specific value for which sensitivity and specificity are identical). High specificity and sensitivity (98.3%/and 97.1%, respectively) were achieved for AFDB and NSRDB. However, a lot of false alarms were produced when atrial flutter or ventricular flutter is encountered. It is worth noting that this algorithm required a window of only 8 RR intervals, which is appealing for short AF episodes detection. Rather than RR intervals, [PERSON] et al. [32] have reconstructed a phase space of RR intervals following the application of [PERSON] embedding theorem [33]. The distribution of probability density function (PDF) of the distance between two points in the attractor is then analyzed. The authors argue that the PDF possibly contains relevant information about the phase point spatial distribution in the reconstructed attractor. A new characteristic variable \(k_{n}\), defined as the sum of \(n\) points slope in filtered PDF curve, is established in order to detect AF arrhythmia. The use of the information on filtered PDF curve slopes contributed to higher precisions achieved in AF detection. Additionally, how the number of RR intervals affect AF detection precision has been studied. Experiments on the AFDB dataset following this technique proved correct AF identification with a performance of 97.80% sensitivity and 99% specificity for 40-RR intervals. However, the reduction of analyzed RR intervals undermines the detection precision. Three linear and nonlinear features have been proposed by [PERSON] et al. [34] to characterize RR dynamics for the detection of AF episodes of AFDB and local datasets. On one hand, two parameters were extracted through the identification of an autoregressive model of RR time series to assess the predictivity and to enhance the presence or absence of normal sinus and AF rhythms in the tachogram. On the other hand, the minimum of the corrected conditional entropy is calculated to verify the regularity of the RR sequences.