# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 5 Comparative Assessment of Existing R-Peak Detection Methods  
### Performance Relative to Robustness  
To further assess the performance of the proposed method, we now compare the proposed U-Net with ten existing detection methods in the literature, which are presented in Table 1 in Section 3.3. The evaluation uses the same datasets, including the original A&D FECG (AD_Origin) data and datasets with signal-to-noise ratio (SNR) levels of 0 dB and 10 dB (denoted as AD_0 and AD_10) and FECGSYN (SYN_Origin) data and datasets with SNR levels of 6 dB and 12 dB (SYN_6 and SYN_12). The same evaluation metrics are used: PPV, SEN, and F1-score, and a tolerance range of 50 ms is set. Figure 7a,b illustrate the performance of compared methods on the A&D FECG and FECGSYN databases, respectively. The results demonstrate the efficacy of the proposed method across datasets with varying SNR conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **PPV (\%)** & **SEN (\%)** & **F1-Score (\%)** \\ \hline SYN\_6 & 63.77 & 54.61 & 58.12 \\ SYN\_9 & 87.16 & 83.67 & 85.05 \\ SYN\_12 & 89.87 & 87.30 & 88.44 \\ SYN\_Origin & 99.96 & 99.93 & 99.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PPV, SEN, and F1-score on FECGSYN database with different SNR conditions. Five-fold cross-validation is applied for each database. For the A&D FECG database, under baseline conditions, the violin plot distribution of the U-Net(ours) across the three metrics is concentrated, with the median close to 1, indicating the highest overall performance. Neurokit2021, Rodrigues2021, Engzeemod2012, and Kalidas2017  
Figure 7: Violin plots comparing the performance of the proposed U-Net architecture with ten established methods on the (**a**) A&D FECG and (**b**) FECGSYN databases. The distributions of PPV, SEN, and F1-scores are shown across different SNR conditions. In all subplots, each black dot represents a individual test result, with median values indicated by a thick white bar and interquartile ranges (IQR) denoted by light-gray boxes. All metrics are scaled uniformly from 0 to 1 to enable direct comparison. are identified as comparable methods as their median values exceed 0.9, accompanied by narrow interquartile ranges (IQRs), indicating high detection accuracy. As the noise level increases, a general decline in performance is observed across all methods. When the SNR decreases to 0 dB, indicative of a high-noise environment, the distribution of the U-Net remains relatively compact, with the median close to 1, suggesting greater robustness to noise. However, the PPV, SEN, and F1-scores for half of the methods in the comparison experience significant drops, with the median falling below 0.5. Notably, only Neurokit2021 maintains a median exceeding 0.8 across the three metrics, although its performance exhibits considerable fluctuation, as evidenced by a wide IQR, indicating sensitivity to noise. In the synthetic database, the superior performance of the U-Net and Rodrigues2021 under baseline conditions is evident, characterized by a high median and a narrow IQR. As with the A&D FECG database, the performance of all methods declines with increasing noise levels; however, U-Net consistently maintains the highest performance. Specifically, on the database with an SNR of 6 dB, the median F1-score for U-Net decreases to approximately 0.6, which is notably twice as high as the median scores of the other compared methods, all of which fall below 0.3. This significant difference underscores the robustness of the U-Net in challenging noise conditions compared to existing methods. Under ideal conditions (10 dB and 12 dB SNR), U-Net's F1-score approaches the theoretical upper limit of 1.0, with the distribution of prediction results being more concentrated. In contrast, traditional methods exhibit limited performance and significant fluctuations in extreme values, highlighting the advantages of the U-Net in achieving consistent and reliable r-peak detections.