# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Univariate Data Analysis on RR Time Series
The second step involves one to four successive steps to classify event type: (1) analysis of histogram; (2) analysis of standard deviation; (3) numbering aberrant rhythms recognition; and (4) Kolmogorov-Smirnov (K-S) test. The algorithm performance increases step by step to finally reach 96.1% and 98.1% of sensitivity and specificity, respectively, for the AFDB dataset. The authors assumed that the accuracy decreases with the number of RR intervals, stating a 20-heartbeat-long limitation for AF episode detection. [PERSON] and [PERSON] [44] are among the first researchers having proposed an AF automatic detector. They have compared a basic Markov model to a basic Markov model with filtering, with interpolation, and with both. The idea was to yield a Markov model in which the probabilities for transitions between short, regular and long RR intervals of a test record are compared with the transition probabilities measured during AF. Results showed that a Markov model combined with filtering and interpolation outperformed the other approaches and reached 96.09% and 86.79% of AF sensitivity and positive predictivity for the MIT-BIH Arrhythmia Database. [PERSON] et al. [46] studied Markov models as well, comparing various algorithms of AF detection to determine the best suited for clinical environment uses (real use case scenario). The investigated algorithms followed a Hidden Markov Model (HMM), measures of variance, linear predictive coding, and measurement of approximate entropy. Ectopic beat removal was performed before analysis, not having an impact on the detector assessment. The HMM algorithm turned out to be the one which performed best for this application, based on the study results from the training and test datasets. Error rate estimates amounted to 7% for training dataset (MIT-BIH Arrhythmia Database) and 5.97% in the case of the test dataset (AFDB). A novel smart phone application has been conducted on moderately sized prospective cohort study regarding 76 patients with AF undergoing cardiovers [47]. The real-time pulse analysis was performed using two statistical techniques: normalized root mean square of successive 64-RR difference (RMSD/mean) and Shannon entropy (ShE). Decision is given by a simple logical condition on the both features after fixing thresholds on the basis of the AFDB and NSRDB datasets. Findings revealed that the algorithm combining RMSD/mean and ShE was 100% and 96.05% accurate for identifying pulse recordings obtained from AF patients and NSR, respectively. The authors assumed that exposure to very high temperatures or bright ambient light, or patients with a high burden of premature beats and/or atrial tachy-arrhythmias might change the performance of the app. The same authors have introduced time-varying coherence functions (TVCF) and Shannon Entropy (ShE) for the same goal [48]. The TVCF is calculated by the multiplication of two time-varying transfer functions (TVTFs), which are obtained from two adjacent 128-RR segments. Combination of the two features has been done by logical AND condition. This approach reached a specificity of 97.54%, a sensitivity of 97.41% (accuracy of 97.49%)for the AFDB, and a specificity of 100% for the NSRDB dataset. In another study [49], they have investigated the ability of three statistical techniques, namely, the root mean square of successive differences (RMSSD), the Shannon entropy (ShE) and the sample entropy (SampE) to diagnose paroxysmal AF. SampE use led to the best performance achieved. The largest area under the ROC curve, using both the AFDB and NSRDB datasets, yields optimal thresholds as the calculated sensitivity and specificity show: 97.26% and 95.91%, respectively. [PERSON] et al. has proposed a cascade of two sequential complementary analyses of RR intervals for automatic AF arrhythmia detection [50]. The first one consisted of discrete wavelet transform (DWT) to identify periods of high heart rate variability (HRV) coefficients.