# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Evaluation Paradigm
However, we still intuitively compare the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Train/Validate**} & \multirow{2}{*}{**Test Data**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**Performance**} \\ \cline{5-10}  & & & & & **Acc (\%)** & \(F_{1}\) (\%) & \multicolumn{1}{c}{**Sen (\%)**} & \multicolumn{1}{c}{**Ppv (\%)**} & \multicolumn{1}{c}{**Spe (\%)**} \\ \hline
[81] & AFDB & MITDB & CNN+RNN & 2-AF;NoAF & 89.3 & / & 99.82 & 51.71 & 87.94 \\  & AFDB & NSRDB & & / & / & / & / & 95.01 \\ \hline
[86] & MITDB DS1 & MITDB DS2 & CNN+BLSTM & 5: N, S, V, E, Q & 96.77 & 77.84 & 74.89 & 81.24 & 95.16 \\ \hline
[28] & PTB-XL (by & PTB-XL (by & \multirow{2}{*}{CNN-FWS} & \multirow{2}{*}{2: N; abnormal} & 90.05 & 90.2 & 88.9 & 91.5 & / \\  & subject) & & CNN-FWS & 2: N; abnormal & 90.05 & 90.2 & 88.9 & 91.5 & / \\ \hline
[87] & PTB & PTB & MLA-CNN- & \multirow{2}{*}{6} & \multirow{2}{*}{62.94} & \multirow{2}{*}{/} & \multirow{2}{*}{63.97} & \multirow{2}{*}{63} & \multirow{2}{*}{/} \\  & & BiGRU & & & & & & & \\ \hline
[88] & MITDB DS1 & MITDB DS2 & CNN & 5: N, S, V, F, Q & 96.36 & / & 70.6 & 48.1 & 96.16 \\ \hline
[89] & Tongji & \multirow{2}{*}{ \begin{tabular}{c} Hospital, \\ China \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} CPSC 2018 \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} DCNN \\ \end{tabular}  } & \multirow{2}{*}{6} & \multirow{2}{*}{/} & \multirow{2}{*}{84.2} & \multirow{2}{*}{80} & \multirow{2}{*}{/} & \multirow{2}{*}{98} \\  & & CPSC 2018 & & & & & & & \\  & & China & & & & & & & \\ \hline
[90] & MITDB DS1 & MITDB DS2 & CNN & 5: N, S, V, E, Q & 90.22 & / & 35.64 & 27.71 & 87.87 \\ \hline
[91] & MITDB DS1 & MITDB DS2 & \multirow{2}{*}{\ \begin{tabular}{c} DCNN + \\ SUP \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} 95.1 \\ \end{tabular}  } & \multirow{2}{*}{84} & \multirow{2}{*}{87.2} & \multirow{2}{*}{82.4} & \multirow{2}{*}{/} \\  & & MITDB DS1 & & & & & & & \\  & & MITDB DS1 & NCART & & 91.6 & / & 88 & 65.3 & / \\ \hline
[91] & MITDB DS1 & MITDB DS2 & CNN-LSTM & 5: N, S, V, F, Q & 95.81 & 71.06 & 69.20 & 74.94 & 94.56 \\ \hline
[92] & MITDB DS1 & MITDB DS2 & DRCNN & 5: N, S, V, F, Q & 88.99 & / & 52.10 & 56.82 & 94.75 \\ \hline
[93] & MITDB DS1 & MITDB DS2 & OptRPC & 5: N, S, V, F, Q & 98.48 & / & 98.45 & 98.43 & 98.06 \\ \hline
[94] & UVA Holter & MITDB & CNN+RNN & \begin{tabular}{c} 4-NSR; AF; \\ Other, noise \\ \end{tabular}  & / & 75.5 & / & / & / \\ \hline
[95] & MITDB DS1 & MITDB DS2 & CNN & 2: PVC; no & \multirow{2}{*}{99.0} & \multirow{2}{*}{99.20} & \multirow{2}{*}{98.60} & \multirow{2}{*}{/} \\  & & PVC & & & & & & \\ \hline
[29] & MITDB DS1 & MITDB DS2 & SE-ResNet & 5: N, S, V, F, Q & 99.61 & / & 93.78 & / & / \\ \hline
[96] & MITDB DS1 & MITDB DS2 & \multirow{2}{*}{ \begin{tabular}{c} SUTDB \\ SUDB \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} 98.76 \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} 98.76 \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} 98.60 \\ \end{tabular}  } & \multirow{2}{*}{/} & \multirow{2}{*}{/} & \multirow{2}{*}{/} & \multirow{2}{*}{/} \\  & MITDB & NCARTDB & & & & & & \\ \hline
[97] & MITDB DS1 & MITDB DS2 & RBM & 5: N, S, V, F, Q & 98.61 & / & 87.31 & / & 98.76 \\ \hline
[98] & MITDB DS1 & MITDB DS2 & Faster & \multirow{2}{*}{ \begin{tabular}{c} N, S, V, F, Q \\ R-CNN \\ \end{tabular}  } & \multirow{2}{*}{95.68} & \multirow{2}{*}{/} & \multirow{2}{*}{72.8} & \multirow{2}{*}{90} & \multirow{2}{*}{/} \\  & & R-CNN & & & & & & \\ \hline
[99] & MITDB DS1 & MITDB DS2 & CNN & 5: N, S, V, F, Q & 94.70 & 88.9 & 89 & 93.7 & / \\ \hline
[100] & MITDB DS1 & MITDB DS2 & FNN+CNN & 5: N, S, V, F, Q & 94.2 & / & 58.2 & 53.6 & / \\ \hline
[101] & MITDB DS1 & MITDB DS2 & CWT+CNN & 5: N, S, V, F, Q & 98.74 & 68.76 & 67.47 & 70.75 & / \\ \hline
[102] & MITDB DS1 & MITDB DS2 & \begin{tabular}{c} WaveNet- \\ LSTM \\ \end{tabular}