# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 4 Discussion
### ECG Database  
The quality of data plays a vital role in achieving high classification performance [37]. DL techniques highly depend on the training data from which they learn the relationship between data characteristics and corresponding categories. As ECG signals are considered private medical information, they are, in general, difficult to collect from a broad range of patients having different genders and ages. In addition, the measurement conditions should be kept unified for every patient, and the annotation for ECG signal samples should be precise, which all require standard facilities and significant annotation effort from cardiologists [5]. Hence, at the current stage, the publicly available ECG databases are the main data resources for DL-based ECG arrhythmia classification and support the progress of the research. However, the symptoms of arrhythmia in ECG signals vary from person to person. Exploiting diverse ECG databases to help DL models experience a greater amount of data samples could significantly improve their inference performance in practical clinical applications.  
According to this review, among the multiple ECG databases, the MITDB is the most popular database for DL-based arrhythmia classification, while it was collected about 40 years ago [9]. The MITDB actually acts as the data baseline to help compare newly designed DL methods to well-established models. Nowadays, as the model complexity of DL models increases to pursue higher classification performance, it inevitably consumes more ECG data for training. Hence, there have been growing works that consider combining datasets from multiple different public ECG databases [16; 123]. Another example is that, in PhysioNet Computing in Cardiology Challenge 2020, seven public databases are provided to participants. However, the differences in the number of leads, signal duration, measurement condition, and patient demographic distribution of those different ECG databases should not be simply ignored.  
Another issue with the existing arrhythmia-related ECG database is that the data categories are significantly imbalanced. The amount of ECG data in the normal category is often dominant in those databases [10]. Although various methods, such as data augmentation [124] and focal loss [92], are used to address the issue of imbalanced datasets, collecting more data in abnormal categories is the ideal way to entirely resolve the issue. However, collecting the specific data requires patients who exactly have the diseases to be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Code Platform** & **Database** &
\begin{tabular}{c} **Evaluation** \\ **Paradigm** \\ \end{tabular} & **Class Types** & **Model** \\ \hline
[101] & GitHub & MITDB & Intra-patient & 5 & CWT+CNN \\ \hline
[111] & GitHub &
\begin{tabular}{c} Telehealth Network \\ of Mins Gerais \\ (TNMG) \\ \end{tabular} & Intra-patient & 6 & DNN \\ \hline
[119] & GitHub & MITDB; CPSC2018 & Intra-patient & 17 & CNN+ BiLSTM \\ \hline
[120] & Website & CPSC2018 & Intra-patient & 9 & CNN \\ \hline
[121] & Website &
\begin{tabular}{c} 2017 PhysioNet \\ Challenge data \\ \end{tabular} & Intra-patient & 12 & DNN \\ \hline
[68] & GitHub & MITDB & Intra-patient & 5 & Hybrid CNN \\ \hline
[122] & Website &
\begin{tabular}{c} 2017 PhysioNet \\ Challenge data \\ \end{tabular} & Intra-patient & 4 & CNN \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._classified, which often is difficult in reality. Hence, the imbalance of the ECG dataset is one of the challenges that researchers should expect to confront in the long term.