# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Database  
DL models require a large amount of ECG signals as the training data to learn the relation between ECG characteristics and the corresponding types of arrhythmias. However, ECG data are considered highly private and sensitive health information, which in general, is difficult to collect from a large group of patients and form a comprehensive database. For the ease of access and the sake of fairly comparing developed DL methods in existing works, the majority of selected studies (89%, 326 out of 368) have established and evaluated their DL models based on ECG datasets from open-source or publicly available databases such as MIT-BIH Arrhythmia Database (MITDB) [9] and MIT-BIH Atrial Fibrillation Database (AFDB) [10]. Table 2 presents the ECG datasets used in the selected works, including their publication information, signal information, demographic information, and the number of selected works that use them for arrhythmia classification. As can be seen in Table 2, MITDB is the most popular, as about 61% (223 out of 368) of works use it for arrhythmia classification. Other popular databases used by more than ten selected works are AFDB, PTB [11], PTB-XL [12], NSRDB, and INCART databases. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Database** & **Publicly** & **Release** & **No.** & **Sampling** & **Duration** & **Subjects** & **Collection** & **No. of** & **Demographic** & **Papers** \\  & **Available** & **Year** & **Channels** & **Rate (Hz)** & **Duration** & **Subjects** & **Place** & **Records** & **Information** & **Papers** \\ \hline MIT-BIH & & & & & & & & & & Male: 25, & \\ Arrhythmia & & & & & & & & & female: 22 & (23–89 years) & \\ Database & & & & & & & & & old) & \\ \hline MIT-BIH Atrial & & & & & & & & & & Subjects are & \\ Fibrillation & & & & & & & & & suffering from & \\ Database & & & & & & & & & & & affiliation from \\ (AFDB) & & & & & & & & & & & \\ \hline PTB Diagnostic & & & & & & & & & & Male: 377, & \\ ECG Database & & & & & & & & & female: 139 & \\  & & & & & & & & & & & \\ (MITDB) & & & & & & & & & & & \\ \hline MIT-BIH & & & & & & & & & & & \\ Normal Simus & & & & & & & & & & \\ Rhythm & & & & & & & & & & (20–50 years old), & \\ (NSRDB) & & & & & & & & & & & \\  & & & & & & & & & & & \\ \hline St Petersburg & & & & & & & & & & Male: 17, & \\ INCART & & & & & & & & & & female: 15 & \\
12-lead & Yes & 2008 & 12 & 257 & 30 min & 32 & Russia & 75 & & (18–80 years old). None of & \\ Arrhythmia & & & & & & & & & & & \\ (INCART) & & & & & & & & & & & \\ \hline BIDMC & & & & & & & & & & & \\ Congestive & & & & & & & & & & (54–63 years old); subjects are & \\ Heart Failure & & & & & & & & & & & with severe \\ (BIDMC) & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Popular ECG databases used by the selected studies. In addition, most datasets contain 12-lead ECG signals where ten electrodes are placed in different locations of the human body, such as V1 for the fourth intercostal space on the right sternum and RA (right arm) for anywhere between the right shoulder and right  
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elbow [13]. It results in 12-channel ECG signals where signals of aVR (augmented vector right), aVL (augmented vector left), and aVF (augmented vector foot) channels are obtained based on combinations of ECG signal measurements from other electrodes. The multi-channel ECG signals could better capture additional heart status information based on a greater number of simultaneous measurements.