## 4 Discussion  
This section discusses the experimental results of the proposed CNN and BiLSTM hybrid model in completely independent subjects and discusses the limitations of this study. Table 2 shows the results of this and other studies in terms of the completely subject-independent model.  
Currently, we compare with existing studies that we found. The deep learning model [17] uses the beat and data augmentation to reconstruct ECG signals. The correlation coefficients of the deep learning model [17] without data augmentation and with data augmentation are 0.846 and 0.908, respectively. The correlation coefficients, RMSE, and PRD of the CNN-BiLSTM model proposed in this study are 0.963, 0.119, and 7.885, respectively. Through [17], it was found that the use of data augmentation improved the performance of the model. This study did not use data augmentation, and the model performance was better than the model with data augmentation in [17]. Compared with the deep learning model, the performance of the model proposed in this study is improved to a certain extent.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Align \(E\) with \(E_{r}\)** & \(r\) & **RMSE** & **PRD** \\ \hline CNN & No & 0.953 \(\pm\) 0.046 & 0.237 \(\pm\) 0.077 & 12.884 \(\pm\) 3.974 \\ Yes & 0.954 \(\pm\) 0.044 & 0.237 \(\pm\) 0.077 & 12.884 \(\pm\) 3.974 \\ \hline CNN-BiLSTM & No & 0.963 \(\pm\) 0.067 & 0.119 \(\pm\) 0.085 & 7.885 \(\pm\) 5.637 \\ Yes & 0.965 \(\pm\) 0.063 & 0.119 \(\pm\) 0.085 & 7.867 \(\pm\) 5.638 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the CNN-BiLSTM and CNN model’s performance. Note: NR stands for not reported. \(r\), RMSE, and PRD represent Pearson’s correlation coefficient, root mean square error, and percentage root mean square difference, respectively. \(E\) and \(E_{r}\) represent the ECG signal and the reconstructed ECG signal, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Data** & **Segment Length** & **Data Augmentation** & \(r\) & **RMSE** & **PRD** \\ \hline Deep learning [17] & MIMIC II [24] & Beat & No & 0.846 & NR & NR \\  & & Yes & 0.908 & NR & NR \\ \hline CNN-BLSTM (this study) & MIMIC II [24] & 8 s & No & 0.963 & 0.119 & 7.885 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the completely subject-independent scheme. Note: NR stands for not reported. \(r\), RMSE, FD, and PRD represent Pearson’s correlation coefficient, root mean square error, Fréchet distance, and percentage root mean square difference, respectively.  
In this study, we propose a model named CNN-BiLSTM to reconstruct ECG signals. Although the CNN-BiLSTM model shows significant advantages compared with models in previous studies, it also has certain limitations.  
* The model proposed in this study was only verified on the MIMIC II dataset, and was not verified on multiple different datasets. Validation against multiple datasets is necessary in subsequent studies.
* An ECG signal has 12 leads, but we only used the signal from lead II and did not analyze the ECG signals of multiple leads. The model proposed in this study may not reconstruct the ECG signals of other leads from the PPG signal. In subsequent research, we will study multi-lead signals and reconstruct ECG signals of more leads from PPG.
* This study focused only on the properties of the complete ECG waveform and did not examine other characteristics, such as R waves and ST segments. A more comprehensive evaluation of the differences between reconstructed and reference ECG features is warranted in follow-up studies.
* The PPG signal needed to reconstruct the ECG signal in this study is the preprocessed signal. The preprocessed PPG signal is a relatively clean signal. In future studies, ECG signals will be reconstructed from noisy PPG signals.