# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Summary of RR-Based Automated AF Detection Research Studies

Table 2: _Cont._
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year [Ref]** & **Main Features** & **Techniques and Algorithms Used** & **Datasets** & **Best \%** & **Limitations and Remarks** \\ \hline \multirow{6}{*}{2004 [68]} & RR TemporalSTD, RMSSD, & ANN and Feed-Forward & \multirow{6}{*}{2004 [68]} & \multirow{2}{*}{32-RR intervals used} \\  & SDSD, & applied & & & \\  & & respectively to RR and & & & \\  & and spectral t-f features & time-frequency analysis & & & \\  & (FFBPNN) & obtained by Short-time FT & & & \\  & & and t-f distributions & & & \\ \hline \multirow{6}{*}{2007 [29]} & Trimmed \textless{}RB, sqrt(spread), & \multirow{6}{*}{Linear discriminant classifier} & MITDB, & Ac = 92 & Tested on 100-RR \\  & NormSqSum(50 ms) & & (LDC) & AFDB, & Ac = 94 & interbeat blocks, subject \\  & NormSqSum(50 ms) & & (LDC) & NSRDB, & Ac = 100 & to performer loss due \\ \hline \multirow{6}{*}{2008 [67]} & Spectral FFT features and RR autoregressive (AR), moving & FFT for spectral analysis and Multi-layer & \multirow{2}{*}{Kasturba Medical college (proprietary)} & \multirow{2}{*}{Kasturba Medical rate of AF = 66.67} & \multirow{2}{*}{ \begin{tabular}{c} Not AF-centered. \\ Ambitous study \\ covering different \\ arrhythmia cases, with \\ small local datasets \\ \end{tabular}  } \\  & Autoregressive moving & perceptron (MLP) & & & \\  & average (ARMA) & & & \\  & model features & & & \\ \hline \multirow{6}{*}{2008 [57]} & Combination of 15 RR HRV & Generalized Discriminant & \multirow{6}{*}{MTDB} & \multirow{6}{*}{Ac = 98.53 (AF)} & \multirow{6}{*}{ \begin{tabular}{c} Scope is broader than \\ AF only: \\ one-against-one vs. \\ one-against-all \\ \end{tabular}  } \\  & features obtained from & Analysis (CDA) and SVM & & & \\  & 32-RR sequences and & with radial basis for & & & \\  & & 6 class detection & & & \\  & & 6 class detection & & & \\ \hline \multirow{6}{*}{2008 [89]} & \multirow{6}{*}{4-period RR HRV features} & Logistic regression & AFDB & Success rate = 95.5 & \multirow{6}{*}{ \begin{tabular}{c} RR filtering and \\ day/evening \\ discrimination criteria applied \\ \end{tabular}  } \\  & Combination of 5 RR time, 1 frequency and & Principal component vs. & & & \\  & Linear discriminant (PCA vs. & & & \\  & 3 nonlinear features & LDA) applied before SVM & & & \\ \hline \multirow{6}{*}{2009 [66]} & _Poincare_/ plot features & K-means & ParAFDB & Se = 91.4 & \\  & & AFFermDB & Sp = 92.9 & \\ \hline \multirow{6}{*}{2012 [58]} & Combination of 14 RR HRV & \multirow{6}{*}{SVM} & \multirow{6}{*}{AFDB} & \multirow{6}{*}{ \begin{tabular}{c} Se = 96.30 \\ Sp = 93.10 \\ \end{tabular}  } \\  & features: 4 spectral, 6 & & & \\  & cross-spectral & & & \\  & and 4 nonlinear & & & \\ \hline \multirow{6}{*}{2012 [62]} & Combination of 5 RR temporal (Mean, STD) & Linear genetic program. vs. & & \\  & RRSD and (BN50) and & 2 & Multi-expression program. & & \\  & geometrical (Triangular & & \\  & index and interpolation) & & vs. weighted Least Squares & & \\  & & regression. Improved & & \\  & index and interpolation) & forward floating selection & & \\  & features & & & \\  & features & & & \\ \hline \multirow{6}{*}{2013 [53]} & Combination of 10 specific & \multirow{6}{*}{ \begin{tabular}{c} AK evidence, irregularity RR \\ sparsity, median of absolute \\ rate, \textless{}RR’s and mainRR \\ \end{tabular}  } \\  & Combination of 10 specific & \multirow{6}{*}{ \begin{tabular}{c} Advanced statistics, \\ Logistic Regression, \\ Knn and Random \\ forest (RF) \\ \end{tabular}  } & \multirow{6}{*}{ \begin{tabular}{c} Local 24 h Holter \\ (2722 samples) \\ \end{tabular}  } & \multirow{6}{*}{Pr = 97} & \multirow{6}{*}{\ \begin{tabular}{c} 10-min RR segments \\ used, 10-fold CV \\ approach and best \\ performance for RF \\ \end{tabular}