# 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 the case of clustering exhibiting either a single cluster or too many of them, an SVM is called to discriminate AF from non-AF using only two feature measures: the stepping increment of inter-beat intervals (average), and diagonal-based dispersion of the points. The SVM performance was evaluated using a 4-fold cross-validation method with leave-one-out on the Paroxysmal Atrial Fibrillation Prediction Challenge Database and Atrial Fibrillation Termination Challenge Database samples. The classification metrics achieved correspond to average values of 91.4% sensitivity and 92.9% specificity. [PERSON] et al. [67] have attempted to classify NSR and 8 different arrhythmias including AF, collected from Kasturba Medical College Hospital, Manipal (India), by using the multilayer perceptron artificial neural networks. The spectral parameters of the RR interval variability have been extracted using fast Fourier transform algorithm and three modelling techniques, namely, autoregressive (AR) model, moving average (MA) model and autoregressive moving average (ARMA). The idea was to evaluate the frequency domain-based approaches that would yield better resolution and could be used for diagnosis in clinical practice. Results showed that the ARMA model gave better accuracy compared to the other modelling approaches, which attained 83.33% as a correct classification of the studied diseases. While this method may work very well for NSR (100% as correct classification), it seems that it often failed to detect AF (66.67% as correct classification). [PERSON] and [PERSON] have used ANN to compare time and time-frequency (t-f) analysis to detect several arrhythmias including AF in only 32-RR intervals [68]. Standard deviation, root mean square of successive differences, standard deviation of successive differences, the percentage of intervals presenting time duration difference between adjacent normal-to-normal RR intervals greater than 5 ms, 10 ms and 50 ms, have been extracted from the tachogram. All possible combinations (63 cases) among these features were tested by a feed-forward back-propagation neural network (FFBPNN) to create the pattern set for the classification stage. Input features to FFBPNN for the t-f analysis was based on the use of short time Fourier transform (STFT) and 18-different t-f distributions (TFDs). The outputs from all neural networks are fed into a 3-step decision rule (average, vote and decision vote) to classify each RR-segment as normal or arrhythmic. These approaches were evaluated using the MIT-BIH Arrhythmia Database. The obtained findings for both sensitivity and specificity reached 87.5 and 89.5%, respectively, for time domain analysis and 90 and 93%, respectively, for t-f domain analysis. This methodology lumped together several arrhythmias (AF, ventricular fibrillation, ventricular tachycardia, supraventricular tachycardia, etc. ), which assumes that all arrhythmias may pretend to be AF. Supervised linear discriminant classifier (LDC) has been used by [PERSON] et al. to estimate the likelihood of a segment of 100 RR intervals that contain PAF and NSR [69]. Trimmed mean of RR intervals, square root of spread in inter-heartbeat interval and its difference, and normalized squared sum of inter-heartbeat differences values less and greater than a fixed value (50 ms) have served as inputs to LDC. Accuracies of 92%, 94%, 100% and 100% have been reached on MIT-BIH Arrhythmia Database, AFDB, NSRDB and NSR2 DB databases, respectively. However, the authors noted that other rhythms (whether arrhythmias or arising during periods that would be labeled as \"normal\"), ventricular premature contractions and false detection of QRS complexes, that give rise to irregular RR interval times, may also trigger the classifier. Recently, deep learning techniques, as a new generation of machine learning pipelines, began attracting interest to AF screening. In this sense, [PERSON] et al. [70] have applied a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) on 100-RR interval.