# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Univariate Data Analysis on RR Time Series
An optimum set of threshold was selected by ROC analysis and results showed that the three parameters share the same sensitivity (93.3%), but they differed in terms of positive predictivity. The variable that characterizes the tachogram predictivity showed the best performance (94.4%). [PERSON] and [PERSON] [35] have studied the similarity between the successive difference of RR intervals (\(\Delta\)RR) and the standard density histograms of 100-RR. First, a density histogram of RR and \(\Delta\)RR was computed. The two histograms were then used as a template for AF detection. Benchmark standard histograms and a test record differences followed a quantitative evaluation by Kolmogorov-Smirnov (KS) test using \(p\)-value statistical significance. Test record comparisons (AFDB and the MIT-BIH Arrhythmia Database) led to the demonstration that the density histograms of \(\Delta\)RR work more accurately for most subjects (the average sensitivity and specificity is 93.2% and 96.7%, respectively) than RR intervals density histograms. Nevertheless, for short AF episodes, the specificity decreased. In another paper [36], the same authors have compared coefficient of variation of RR and \(\Delta\)RR intervals density histograms. The Kolmogorov-Smirnov test using RR intervals did not improve the coefficient of variation test results, while the Kolmogorov-Smirnov test using the \(\Delta\)RR intervals improved both the sensitivity and specificity (94.4% and 97.2%, respectively). Other uses of RR distribution plots and \(\Delta\)RR histograms have been performed by [PERSON] et al. for very long terms (weeks) AF monitoring [37]. Geometric features, such as width of the histogram base, its height and some characteristics from unimodal or multimodal distribution, have been studied to characterize theses distributions. Main distribution width of \(\Delta\)-RR histograms has been selected as the best discrimination parameter. Sensitivity and positive predictivity reached 92% and 78%, respectively, when tested on the AFDB dataset. Only episodes longer than two minutes RR intervals segments were included in the analysis. As a clinical output linked to shorter AF episodes, authors argue that their relevance is lower in long-term monitoring use cases. [PERSON] et al. [38] presented a scatter plot approach to distinguish AF arrhythmia from normal sinus rhythm. One-minute RR interval signals were used to represent the plots. From the plots, four geometrical features were used, namely, Vector Angular Index (VAI), Vector Length Index (VLI), Dispersion of points along the diagonal line (SD2) and perpendicular to the diagonal line (SD1). Each index was tested using Mann-Whitney test to see if there was significant difference between the two groups. Indeed, Box plots demonstrated clear difference of each index between AF and regular ECG. The optimal threshold ranges for the classification of the two groups have been found by ROC curve analysis. The best parameters to achieve high performance are SD1 and SD2 which reached a sensitivity of 98.3% and a specificity of 100%. These results have been obtained using a small dataset as it contained only 120 recordings (60 from AF Termination Challenge Database and 60 ECG recordings from healthy people) of 1 min duration, which cannot be directly integrated into an applicable tool as a generalization study involving a big dataset would be required. The method introduced by [PERSON] et al. [39], comprised a three-pass procedure. The first pass, where a sequence of RR interval is pre-processed with median and low-pass integer filters, aims to generate low/high scale reference sequences. The second pass is aimed at obtaining a dynamic symbolic sequence of the first pass data. Symbolic dynamics transform the information as a variation of successive RR to a series of fewer symbols, where each symbol is representing an instantaneous state. Finally, a third pass consists in calculating Shannon entropy for the symbolic sequences to discriminate AF presence in the corresponding heartbeat.