# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods  
In this paper, a selection of works that attempt to solve the problem of detecting AF are presented. Most of them use a two-step procedure after ECG data pre-processing as shown in Figure 1: first, feature extraction from RR time series (tachogram) or P-wave or their combination is conducted and then followed by a classification building process based on the extracted parameters. The performance of the major established classification methods was evaluated on Physionet signals; a publicly accessible online database containing physiologic signals. Seven datasets are commonly used: MIT-BIH Arrhythmia Database (abbreviated MITDB or often MIT-BIH AR as well), MIT-BIH Atrial Fibrillation Database (AFDB), Long-Term AF database, Paroxysmal Atrial Fibrillation Prediction Challenge Database (ParAFDB), AF Termination Challenge Database (AFTermDB), MIT-BIH Normal Sinus Rhythm Database (NSRDB), and Normal Sinus Rhythm RR Interval Database (NSR2 DB). Figure 1: ECG classification flow chart analysis. As said previously, cardiology experts inspect ECG recordings in an experienced-based manner to identify any type of heart rhythm. In an automated AF detection algorithm, this step is equated with extraction of interesting and discriminant variables from the ECG signals. These parameters help to characterize the P-wave (presence, morphology) and the RR intervals' variability (regularity, irregularity, pattern). After reviewing recent papers, we have noticed that the published works dealing with AF detection may be categorized in six methodologies as summarized in Figure 2.  
This review highlights the advances in AF detection that followed RR time series analysis. Since P-waves are associated with significantly higher computational resources than are the RR-based classifiers, their integration in a low power demanding electronic device is not a realistic option. Furthermore, the detection of P-wave fiducial points is a very difficult task because of its low amplitude, which makes it prone to corruption by noise. This being so, some authors have demonstrated that opting for complex features by using morphologic analysis does not necessarily outperform that the results achieved by an RR-based AF classifier [18; 19]. For these reasons, the analysis of RR irregularities is most studied by researchers rather than the absence of P-waves or the combination of both characteristics. The AF detectors are evaluated by the following metrics:  
Accuracy: Ac = (TP + TN)/N, Sensitivity: Se = TP/(TP + FN), Specificity: Sp = TN/ (TN + FP), Precision, also known as Positive Predictive Value: Pr = PPV = TP/(TP + FP), and, less often, Negative Predictive Value: NPV = TN/(TN + FN); where TP is the number of True Positives or correctly detected AF episodes, TN is the number of True Negatives or correctly detected non-AF episodes, FP is the number of False Positives, i.e., normal rhythms misclassified as AF, and FN is the number of False Negatives or missed AF cases classified as normal. The total number of observations is denoted by N. Sensitivity-Specificity and Sensitivity-Precision metrics dyads are usually favored in comparison to Ac alone or more ambiguous success rates, as better describing the classification tradeoffs between the ability to capture existing cases and the certainty of the classification results obtained. In terms of features, research on heart rate variability (HRV) traits has been paving the way for the work on arrhythmia classification based on RR metrics (demoted NN when dealing with normal heartbeats). The QRS complex, i.e., the most prominent characteristic among the PQRST electrocardiogram wave points (see Figure 3), is understandably at the basis of ECG assessment and has been extensively investigated. QRS detection algorithms and R-R interbeat computation are standard practice in ECG processing nowadays, with RR time series (tachogram) and subsequent HRV features currently being implemented in online paradigms [20] and seen in wearable health tracking domains.