# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Univariate Data Analysis on RR Time Series
ROC analysis on Shannon entropy feature allows setting a threshold for optimal discrimination (0.353). The best performance achieved corresponds to a sensitivity and specificity of 96.72% and 95.07%, respectively (Long-Term AF used as training set). AF and normal sinus rhythm samples are found in the test dataset (AFDB), reaching a sensitivity and specificity of 96.89% and 98.25%, respectively. However, the study stresses that sporadic AF episodes of relatively short duration (e.g., ten seconds) may pose potential limitations due to false negative detections. In another paper, [PERSON] and co-workers [40] have reapplied the same strategy with a different optimal discrimination threshold (0.639) to four clinical datasets. For the same training dataset, 96.14% and 95.73% were achieved for sensitivity and specificity, respectively. For AFDB (test dataset), they reached 97.37% and 98.44%, respectively. Therefore, by comparing results of the remaining datasets, the present threshold demonstrates a slightly better performance. [PERSON] et al. [41] have developed an algorithm that combines three features: root mean square of successive differences to measure the variability, the turning points ratio as a test for RR intervals randomness and Shannon entropy to characterize its complexity. Suppression of ectopic beats has been applied prior to the calculation of parameters. AF arrhythmia presence using this method is determined in accordance to threshold-based conditions. AF detection using a beat-by-beat approach is evaluated and accuracy is shown through a ROC curve indicating that the optimal episode length is 128-RR intervals with at least half of AF to ensure correct classification of the segment/episode as AF. For AFDB, sensitivity and specificity metrics achieved were 94.4% and 95.1%, respectively; and 90.2% and 91.2% for the MIT-BIH Arrhythmia Database, respectively. [PERSON] et al. [42] have studied the accuracy rate of a new algorithm for AF detection in cardiac telemonitoring with portable ECG devices. In order to do so, an AF indicator is calculated in overlapping 52-beat windows from the histogram of RR interval differences of 120-s ECG recordings. The indicator is calculated from the ratio of histogram width to height, the number of premature ventricular beats that it may include and the position of the histogram peak. The ROC analysis on only 8 recordings in the MIT-BIH Arrhythmia Database has permitted us to determine an optimal threshold value for AF detection. The reported method reached a sensitivity of 92.9% and a specificity of 90.9%. As limitations of the study, this algorithm cannot run if ECG recordings do not have enough beats because of the required minimum of 52 beats. To screen for AF presence, [PERSON] and [PERSON] have used a smoothed normalized variance of 10 s of RR intervals [43]. The feature normalization was inspired by [PERSON] [44]. This method comprised 5-step-process: RR calculation, RR normalization, variance computation and initial AF detections over each 10 s. Finally, a decision, based on a simple majority voting scheme over 600 beat windows, is formulated using the smoothed initial classifications. The algorithm, which is a morphology-independent, is suitable for ambulatory monitoring situations. ROC analysis results on the AFDB showed a sensitivity of 96% and a specificity of 89%. Different cardiac rhythm transitions, in particular between AF and sinus rhythms, have been carefully studied by [PERSON] et al. [45]. The proposed method involved 2-step process: (1) AF detection using 100 \(\Delta\)-RR intervals distribution difference curve (dRDDC) and (2) AF event classification (onset, cessation, and none). The first step includes dRDDC computing, detecting a peak in the dRDDC (local maximum value) for at least each 20 beats, and filtering noise peak (distinction between noise peak and event peak).