# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Multivariate Data Analysis on RR Time Series
Here, generalized discriminant analysis (GDA) feature reduction was used to yield 5 discriminant functions instead of 15 extracted from the input HRV signal (32 RR intervals) of MIT-BIH Arrhythmia Database. The results of the proposed method are shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Statistical Methods of Multivariate Analysis** & **Biologically Inspired Methods of Multivariate Analysis** \\ \hline MLR: Multiple linear regression & ANN: Artificial neural networks \\ PLS: Partial least square & MLP: Multi-layer perceptron \\ PCA: Principal component analysis & PNN: Probabilistic neural network \\ CA: Cluster analysis & LVQ: Learning vector quantization \\ LDA: Linear discriminant analysis & RBF: Radial basis function \\ KNN: K-nearest neighbors & SOM: Self-organizing map \\ SVM: Support vector machine & FIS: Fuzzy inference system \\ RVM: Relevance vector machine & FCM: Fuzzy c-means \\  & FNN: Fuzzy neural network \\  & ART: Adaptive resonance theory \\  & NFS: Neuro fuzzy system \\  & GA: Genetic algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multivariate analysis methods and abbreviations. Arrhythmia Database. SVM-based radial basis function was then applied to both original and reduced features using the optimum values of \(\sigma\) (=0.08) and C (=30). The authors have compared the performances of two types of SVM decomposition method: the one-against-one and the one-against-all. This latter has outperformed the first one and therefore can discriminate the NSR with an overall accuracy of 98.94%, the AF with 98.53%, the premature ventricular contraction with 98.96%, the sick sinus syndrome with 98.51%, the ventricular fibrillation with 100%, and the 2\({}^{\circ}\) heart block with 100%. In another study [58], they have extracted 14 features from HRV signal. These features consist of 4 spectrum features (spectral power in the low frequency (LF) and high frequency (HF) bands and their frequency peaks), 6 cross-spectrum features, and 4 nonlinear features including SD1, SD2, SD1/SD2, and sample entropy. ANOVA (analysis of variance) test of these features was applied to determine whether there were statistical differences in the features extracted from paroxysmal AF (PAF) and non-PAF episodes. For the AFDB, all the features exhibit statistically significant differences in two groups except frequency peaks in LF and HF bands, which were then eliminated from SVM classifier building. By using RBF Kernel function, the optimum values of SVM parameters are achieved as 3.6 and 10 for \(\sigma\) and C, respectively. The obtained sensitivity and specificity were 96.30% and 93.10%, respectively. [PERSON] et al. presented a study in which HRV features of AF arrhythmia episodes, researchers hypothesize, could be different depending on the time of the day [59]. Indeed, they started by studying HRV patterns of AF episodes during entire day, daytime and evening time. From AFDB, four data acquisition sessions were chosen: morning session (6 a.m. to 12 noon, time period (1)); afternoon session (from noon to 6 p.m., time period (2)); evening session (from 6 p.m. to 12 p.m., time period (3)) and late night session (from 12 a.m. to 6 a.m., time period (4)). Only RR intervals between 0.75 and 1.5 times the accumulated RR average are kept, to avoid significantly short or long RR intervals due to issues of noisy signals or missing R peaks, respectively. Three classifiers based on Logistic regression analyses were generated for the studied session's day. The second classifier dedicated to the daytime detection of AF episodes performed more accurately with 99.3% success rate compared to the first (whole of the day) and third (evening time) classifiers (95.2% and 93.8%, respectively). [PERSON] et al. pioneered the use of pattern recognition methods for AF diagnosis [60], among other researchers. In the study, a backpropagation artificial neural network to detect AF in the presence of other cardiac arrhythmias was designed and evaluated.