# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Multivariate Data Analysis on RR Time Series
The input of the neural network consisted in a generalized interval transition matrix, similar to that discussed above by [PERSON] and [PERSON] in [44]. The size of the hidden layer was set empirically to 12 (established by training networks while changing hidden layer size from 4 to 16 units). A 30-point moving average post-processing technique, with two thresholds, applied to ANN output, achieved AF classification positive predictive value of 92.34% AF and a sensitivity of 92.86% for the used AFDB dataset. [PERSON] and coworkers have proposed an effective solution to distinguish normal sinus rhythm and AF from the condition of sinus rhythm (SR) with atrial and ventricular ectopy for 10 min of RR segments [61]. First, a univariate analysis involving the coefficient of sample entropy (COSEn), local dynamics score (LDs) and detrended fluctuation analysis has been conducted using ANOVA tests (one-way Kruskal-Wallis) to compare the index values among the three groups (NSR, AF, SR with ectopy) and post hoc multiple comparisons were performed following Wilcoxon rank-sum test with Bonferroni correction. Results showed that taking one univariate metric is not sufficient to successfully achieve a separation of the three groups, for 24 h Holter recordings collected from 2722 consecutive patients (377,285 segments of 10 min RR interval) at the University of Virginia Heart Station. Afterwards, three multivariate analysis methods were compared: Logistic regression, K-nearest neighbors and Random forests. All models were validated using a 10-fold cross-validation procedure on the entire dataset. The random forests analysis, applied to the three nonlinear features with mean and standard deviation of RR segment, reached positive predictive values of 90, 98 and 97% for SR with ectopy, AF and NSR, respectively. The effectiveness of the classifier in a continuous context, mimicking real-time implementation has been also investigated. Changes in heart rhythm are faithfully detected. It is worth noting that this classifier was trained to consider atrial flutter to be the same as AF. [PERSON] and co-workers [62] have compared two approaches of genetic programming, namely, linear genetic programming (LGP) [63] and multi-expression programming (MEP) [64] and a weighted least squares (WLS) regression analysis to discriminate samples of AF arrhythmia and NSR episodes. Indeed, four statistical (Mean, STD, RMSSD and pNN50) and two geometrical (HRV triangular index and triangular interpolation of normal-to-normal interval histogram) features were extracted from 64-RR segment. Then, a feature selection technique based on an improved forward floating selection (IFFS) analysis was conducted. The IFFS algorithm was an improvement upon the sequential forward floating selection (SFFS) [65] algorithm. Thus, Mean, STD and RMSSD were selected as three relevant features. The application of the statistical methods on MIT-BIH Arrhythmia Database (70% for training and 30% for testing) yields an arrhythmia class as simple functions of the three features. Results have shown that LGP model has outperformed MED and WLS models in training and test phases. Therefore, sensitivity and specificity of the LGP model for the training data were 99.56% and 99.19%, respectively. These rates are, respectively, equal to 99.11% and 98.91% for the testing data. [PERSON] et al. [66] have exploited three features extracted from HRV in _Poincare_ plot as inputs to K-means clustering methods and Support Vector Machines with radial basis function kernel. These features are the number of clusters in _Poincare_ plot, mean stepping increment of inter-beat intervals, and dispersion of the points in the plot. First, a K-means clustering approach was followed to set a criterion about the number of clusters in _Poincare_ plot. Consequently, this helped mitigate possible inter-beat interval calculation errors that are not uncommon in AF data.