## 5 Results
### Comparison and Analysis of Experimental Results  
The data for this experiment originates from the dataset obtained after preprocessing in Section 3.3. It is divided into a 7:1:2 ratio, with the first 70% serving as the training set, the subsequent 10% as the validation set, and the remaining 20% as the test set. Initially, the TCN-CBAM blood pressure estimation model is thoroughly trained using the training and validation sets until it reaches convergence. Subsequently, PPG data from the test set are inputted into the model to derive the corresponding estimated SBP and DBP values. Based on these estimated values and the actual values (specifically, the SBP and DBP from the test set labels), scatter plots and Bland-Altman plots are generated for analysis. The outcomes are illustrated in Figure 7.  
As evident from Figure 7, in the scatter plots of the experimental results for both SBP and DBP, a large number of data points are tightly clustered around the regression lines, each forming a distinct trend line. After conducting Pearson correlation analysis  
Figure 7: Scatter plot and Bland–Altman plot of model test results: (**a**) SBP, (**b**) DBP. between the model-estimated blood pressure and the reference blood pressure indicated in the label, the Pearson coefficient for the model's SBP estimate compared to the reference SBP is 0.8; moreover, the model's DBP estimate, compared to the reference DBP, is 0.6. This indicates a strong positive correlation between the model's estimated values and the actual measurements. Compared to SBP, the regression line for DBP estimation exhibits a slope closer to 1 and a smaller absolute value for the intercept, suggesting higher overall accuracy. Additionally, the tight distribution of scatter points demonstrates the model's high degree of generalization when processing blood pressure data from diverse individuals, accurately capturing the nuances of blood pressure variations. We assessed the performance of the TCN-CBAM model in blood pressure estimation tasks using the BHS standard by analyzing the estimation errors in the test set. The comparison between the error distribution and the BHS criteria is presented in Table 2.  
To verify the effectiveness of the TCN-CBAM beat-by-beat blood pressure estimation model proposed in this chapter, we selected ten machine learning models for comparison. Six of these are commonly used regression models in machine learning, namely, linear regression, support vector regression (SVR), multilayer perceptron (MLP), XGBoost (XGB), random forest (RF), and K-nearest neighbours regression (KNN). All six models are implemented using the sklearn machine learning library. The other three are deep learning models previously used in continuous blood pressure estimation studies: CNN, CNN-GRU, and CNN-LSTM. The number and size of convolutional kernels used in the CNN models are consistent with those in the TCN-CBAM model. The CNN-GRU model incorporates a two-layer GRU with 128 hidden nodes, while the CNN-LSTM model includes a two-layer LSTM with the same number of hidden nodes. The error distribution of the estimation results from the eleven aforementioned models is depicted in Figure 8. As evident from the figure, whether for SBP or DBP estimation, the estimation accuracy of the three structurally simpler machine learning models--linear, SVR, and MLP--significantly lags behind that of ensemble learning and deep learning models. While the ensemble learning models, specifically random forest and XGBoost, exhibit noticeable improvement in terms of error reduction, they still trail behind neural network architectures based on CNN and RNN.