# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## 3 Experiments  
### Datasets  
Our experimental analysis utilized a combined training dataset comprising over 88,000 annotated twelve-lead ECG recordings originating from six publicly available \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Optimizers & **Adam**, AdamW \\ \hline Learning rates & 0.1, **0.01**, 0.001, 0.0001 \\ \hline Number of epochs & 10, **30**, 50, 100 \\ \hline Early stop & **5**, 10, 20 \\ \hline Loss Function & Binary Cross Entropy with Logistic Loss \\ \hline Positive weight & **yes**, no \\ \hline Batch size & **500**, 1000 \\ \hline K-Folds & 5 \\ \hline Pruning strategies & No, Random, L1 \\ \hline Dropouts & No, 0.1, 0.2, 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network hyper-parameters. Bolded values represent parameters for which the best results were obtained. sources [39; 40; 41; 42; 43; 44], as shown in Table 3. In the table, analysis of datasets published in parts (CPSC and CPSC-Extra, PTB and PTB-XL) or representing the same demography with signals of the same characteristics (Chapman-Shaoxing, Ningbo) is combined and represented in a single row. Additional demographic information can be found in Appendix A.1. These recordings exhibit considerable variability in duration, ranging from 5 s to 30 min, and in sampling frequencies, including 257 Hz, 500 Hz, and 1000 Hz. Notably, the majority of the samples are 10 s and recorded at 500 Hz. However, it is crucial to note that the St. Petersburg INCART dataset [40] consists of 75 annotated recordings extracted from 32 Holter records, where each record lasts 30 min and is recorded with a 257 Hz frequency. As such, signals with a sampling frequency lower than 500 Hz were interpolated, and signals with a sampling frequency of 1000 Hz were downsampled to match the target of 500 Hz. Each of the six datasets encompasses 26 scored classes, comprising normal rhythm and 25 cardiac abnormalities, with per-dataset disposition presented in Table 4, and up to 104 unscored classes. The detailed distribution of recordings containing labels outside of the scope of the used challenge metric is available in Appendix A.2. It is crucial to notice that while a majority of the ECGs in the datasets include one (44.9%), two (24.7%), or three (14.7%) labels, the total number of labels can vary from 1 to 12 [45]. The WFDB format was used to deliver all data, where each ECG signal data recording was represented by a binary MATLAB v4 file and an accompanying WFDB header text file that contained additional recording characteristics and patient attributes, including diagnosis. An exemplar header file can be found in Appendix B. The heterogeneity in label quality and the presence of inaccuracies within these datasets contribute to the complexity of the problem. The datasets used have signals from 12 ECG leads, and the analysis of such signals is used in the work because it provides the best results. However, the datasets have different numbers of scored and unscored classes present (as shown in Tables 4 and A2), and each presents different quality of signal, both in the form of noise and incorrect signals (where some leads are zeroed or lost in part of the signal). Known data quality issues include a lack of a lead signal--when all values of the given lead recording are zeros, disturbance in contact between the surface electrode and the patient's body (i.e., during readjustments) represented as transient spikes or oscillations or rapid bursts of high-frequency noise [46; 47], and malformed binary files. To protect the core networks from incorrect signals and allow them to work with both full 12-lead ECG signals and those subject to external interference, at the stage of pre-processing within the domain knowledge vector, marking of erroneous QRS readings and distances between peaks is used.