# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Database
Furthermore, the sampling rates of ECG signals range from 128 Hz to 1000 Hz, and about half of ECG databases (8/17) have a sample rate of 250 Hz, as can be seen in Table 2. Based on the signal duration, the ECG signals can be categorized into long- and short-term measurements, from 10 s to 2 h. Most databases provide gender and age information, and the numbers of females and males tested are generally balanced. Some datasets, such as MITDB, contain both normal and abnormal ECG signals, while most ECG signals in datasets, such as the INCART database, are from patients having ventricular ectopic beats. Compared to the widely-used MIT-BIH series of databases collected in the USA, such as MITDB, NSRDB AFDB, recent ECG databases such as PTB/PTB-XL and Chapman collected in Germany and China, respectively, emerged in the research community, which is considered by increasing studies considered for DL-based ECG arrhythmia classification. Figure 2 shows the trend of major databases used by selected works each year from 2017 to 2022. One can observe that every year, MITDB is still the dominant database in the research community. The proportion of studies that consider PTB/PTBXL has been increasing in recent years. The diversity of databases is improved as the number of databases used by more than ten studies increases from 6 in 2017 to 9 in 2022. Being the most popular ECG database, MITDB contains 2-lead ECG signals with a sampling rate of 360 Hz and a duration of 30 min. The ECG signals are collected from 47 patients. Recordings 201 and 202 are collected from the same patient, resulting in 48 recordings in total. The age of patients ranges from 23 to 89 years. The duration of each recording is about 30 min. However, the ECG dataset from MITDB is an _imbalanced dataset_ where most ECG recordings are normal while abnormal recordings are much less than the normal ones. As the abnormal signals are more difficult to collect, most ECG databases encounter the issue of data imbalance, which could potentially introduce learning bias in the DL-based classification frameworks [14]. Besides ECG datasets obtained specifically for arrhythmia classification, to improve the robustness of the DL models, noisy but normal ECG records can be added to the training dataset. For example, the MIT-BIH Noise Stress Test Database, which is collected from physically active volunteers to mimic ambulatory ECG, acts as a category of noisy ECG signals [15; 16; 17]. In this way, the real situation in clinical practice can be emulated. Figure 2: Trend of different ECG databases used by selected studies. Among the selected works, 165 (45%) of studies consider more than one ECG dataset by combining multiple different ECG databases. For example, [18] exploits five public ECG datasets, i.e., AFDB, MITDB, NSRDB, the 2017 PhysioNet/CinC Challenge Database, and the first China Physiological Signal Challenge 2018 Database (CPSC2018), where AFDB is used for training and evaluation while other four datasets are used to test the generalization performance of the proposed DL model. This mechanism of training and testing DL models with ECG signals from two non-overlapping groups of patients, respectively, is a typical case of _inter-patient diagnosis_. However, as those datasets have different attributes such as categories and numbers of channels, a smaller number of classification categories, such as categories of Atrial fibrillation (AF)/non-AF and categories of AF, Normal, Premature Atrial Contractions (PAC), Premature Ventricular Contractions (PVC), Ventricular fibrillation (VF), and Noise are often considered [19; 20]. In [21], ECG signals from MITDB, MIT-BIH AFDB, CUDB, and MIT-BIH VFDB are fused to form one dataset where the training and testing datasets are obtained by randomly selecting ECG data from the combined dataset. Hence, the _intra-patient diagnosis_ is performed where the DL model has the possibility to train and test based on ECG information from the same patient.