# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Multivariate Data Analysis on RR Time Series  
In RR time series analysis, it is important to quantify the randomness of the RR intervals. This is usually done by simple measures. Additionally, modeling helps in the attempt to construct a model capturing RR irregularity. AF arrhythmia's erratic nature, though, makes it difficult to exactly model RR irregularities present in AF. However, telling apart AF RR irregularities and that of other of other cardiac arrhythmias is supported by models. The research works on automatic AF detection using multivariate data analysis and which have been applied successfully to RR time series are discussed in the subsection. Figure 4 outlines different types of data analysis techniques. The abbreviations are listed in Table 1.  
Figure 4: Multivariate data analysis methods organized as statistical or biologically inspired techniques. To illustrate this idea, [PERSON] et al. have compared univariate and multivariate data analysis to build a reliable AF detector [53]. Ten RR features present in approaches that had demonstrated high performance were tested. The classification performances of the selected features were evaluated via two methods, i.e., using univariate analysis and Support Vector Machine (SVM) as a robust multivariate data analysis. The chosen variables were as follows: COSEn; SampEn; OriginCount (the count of the number of {RRi; RRi-1} values in the bin containing the origin); PACEvidence (reflects the evidence of compensatory pauses); IrregularityEvidence (measured the sparseness of RR histogram distribution); AFEvidence (AFEvidence = IrregularityEvidence OriginCount \(-\) 2 \(\times\) PACEvidence), as described by [PERSON] et al. [54]; and the median of absolute deviation (MAD) [55], which is a RR variance-based AF feature. Median heart rate, minimum RR interval and mean RR interval were also included within the set of chosen features. In addition, window lengths (WL) varying from 12 to 300 s and minimum number of AF beats (minNbeat) within an N-beat segment were carefully quantified as a requirement to classify a given segment as an AF event. Indeed, researchers followed a 5-fold cross validation to assess the optimal WL using the minNbeat values. Excellent results have been obtained by using the SVM, built by nine out of ten features, on the AFDB for training purposes (Se = 99.07% and Ac = 98.84%) and both the NSRDB and the MIT-BIH Arrhythmia Database for testing (Se = 96.35% and Sp = 98.91% for a relatively short window length of 65 beats). These findings were enhanced for window length of 140 beats and outperformed all the features analyzed separately. [PERSON] and [PERSON] have compared two cascade multivariate analysis methods to detect AF episodes of only 32 RR intervals [56]. The idea was to perform a dimensionality reduction of features and selection of discriminating ones by choosing between principal component analysis (PCA) and linear discriminant analysis (LDA) and then conduct a support vector machine (SVM) classification based on the resulting features. PCA and LDA have reduced the initial set of features from 9 (6 linear and 3 nonlinear features) to 4 principal components and 4 discriminant functions, respectively. To build a robust SVM classifier, radial basis function (RBF) as kernel function has been used and kernel width \(\sigma\) and regularization constant C were optimized (\(\sigma\) = 10 and C = 1). The overall sensitivity and specificity of the proposed method (LDA + SVM) have reached 99.07% and 100%, respectively, when tested on MIT-BIH Arrhythmia Database. These results outperformed those of PCA + SVM approach (99.28% of specificity) and were also better than the SVM classifier when it was directly used on the initial nine features (97.22% of sensitivity and 98.57% of specificity). The same strategy has been attempted in a work with [PERSON] et al. to discriminate six different types of arrhythmia classes including NSR and AF [57].