# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Evaluation Paradigm  
The model generalization performance of DL models is a crucial perspective to be considered in the step of model evaluation. The generalization performance refers to the capability of classification models to infer categories of previously unseen and new data. For ECG classification, two evaluation paradigms have been investigated to evaluate the classification capability of DL models, i.e., the intra- and inter-patient paradigms, as depicted in Figure 5a. Specifically, in the inter-patient paradigm, the learning model trained on ECG signals from one group of patients is evaluated with different groups of patients which do not overlap with the training group. The intra-patient paradigm refers to the case that the DL mode could be trained and evaluated based on ECG signals from the same patients. Figure 4: Distribution of architectures used by selected studies in each year. (**a**) Trend of different DL models used by the selected studies; (**b**) Trend of optimization techniques. Among all the selected studies, 27 studies focus on the inter-patient paradigm, while a significant number of studies (319) consider the intra-patient diagnosis. In addition, a total of 11 studies consider both paradigms, while few studies do not describe their paradigm explicitly. As can be seen in Figure 5b, the proportion of the selected studies considering the inter-patient paradigm has been increasing in recent years as it is more desirable for clinical applications in practice. Detailed information about the selected studies, which consider the inter-patient paradigm, is presented in Table 3. It summarizes the specific ECG data used for training/validation and testing, deep learning algorithm, classification category, and classification performance. The MITDS1/DS2 method (82%, 31 of 38) is the most popular evaluation method for the inter-patient paradigm. Specifically, the ECG data in MITDB is divided into two groups, i.e., DS1 and DS2, where 22 records are included. The details about how to obtain the standard DS1 and DS2 are illustrated in [77]. Please note that the MITDS1/DS2 method is modified in some studies [63] for ECG analysis, where different recordings are included in DS1 and DS2. Additionally, some works consider leveraging ECG data from one database for training and testing the trained models with different ECG databases. For example, the model in [81] trains based on AFDB and then tests the model with MITDB. The number of classification categories in those selected works varies from 2 to 9 categories. Figure 5: Comparison of the intra- and inter-patient paradigms. (**a**) An illustration of the intra- and inter-patient paradigms; (**b**) Trend of the intra- and inter-patient paradigms; (**c**) Comparison of classification performance achieved in the inter- and intra-patient paradigms. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & **Train/Validate Data** & \multirow{2}{*}{**Test Data**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Class**} & \multicolumn{5}{c}{**Performance**} \\ \cline{5-10}  & & & & & **Acc (\%)** & & **F\({}_{1}\) (\%)** & **Sen (\%)** & **Ppv (\%)** & **Spe (\%)** \\ \hline
[82] & MITDB DS1 & MITDB DS2 &
\begin{tabular}{c} DenseNet- \\ BiLSTM \\ \end{tabular} & 5: N, S, V, F, Q & 92.37 & 63.49 & 68.29 & 60.35 & 94.51 \\ \hline
[83] & MITDB DS1 & MITDB DS2 & CNN & 5: N, S, V, F, Q & 88.34 & / & 90.9 & 48.25 & 88.51 \\ \hline
[84] & Fantasia + & Fantasia + &
\begin{tabular}{c} Fantasia + \\ INCART \\ \end{tabular} & CNN-LSTM & 2: N, CAD & 95.76 & 95.57 & 95.7 & / & 95.76 \\ \hline
[85] & MITDB DS1 & MITDB DS2 & O-WCNN & 5: N, S, V, F, Q & 99.43 & 92.05 & 91.06 & 93.5 & 99.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The inter-patient paradigm studies in the selected articles. As the motivations, tasks, datasets, and classification methods of all the reviewed studies are different, it is unfair and not straightforward to compare the classification performance across all the reviewed studies.