## 2 Related Works  
For the non-invasive continuous blood pressure estimation task using single-channel PPG, existing methodologies can be broadly categorised into traditional machine learning and deep learning approaches. Traditional machine learning techniques often entail manual feature extraction from raw PPG data, followed by the development of a regression model based on conventional machine learning algorithms to estimate systolic and diastolic blood pressure in individuals. For instance, [PERSON] et al. [7] selected specific features from the PPG signal, such as the half-width and two-thirds width of the pulse amplitude, systolic ascending time, and diastolic time, to establish a linear regression model for estimating systolic and diastolic blood pressure. Their method demonstrated impressive accuracy on a dataset comprising 15 young and healthy subjects. In another study, [PERSON] et al. [8] extracted 21 parameters from over 15,000 heartbeat training samples in the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database. These parameters served as input features for an Artificial Neural Network (ANN) aimed at estimating blood pressure from PPG signals. This approach proved to be more precise than the linear regression method. [PERSON] et al. [9] extracted 14 features from the second derivative of PPG (SDPPG) and combined them with the 21 features used by [PERSON] et al. They employed a support vector machine as a BP estimator to improve the prediction of systolic and diastolic blood pressure. Compared to the 21 features and ANN applied by [PERSON] et al., this approach enhanced the accuracy of BP estimation by 40%. The primary challenge faced by methods that use manual features and classic machine learning for blood pressure estimation lies in the requirement for high-quality PPG signals to accurately locate PPG waveform feature points. However, PPG signals collected by wearable devices often contain significant noise and motion artefacts, making it difficult to accurately identify critical feature points such as peaks, valleys, dicrotic notches, and inflection points. Consequently, a series of features derived from the positions of these feature points also suffer from considerable errors. Additionally, the design and selection of manual features require professional knowledge and experience and often involve multiple trials to determine the optimal feature set, posing certain limitations in practical applications. With the steady enhancement of computing power, researchers have shifted their focus towards automatic feature extraction methods for PPG signals utilising deep learning techniques [10]. In contrast to traditional machine learning models, deep learning models excel at learning features directly from raw data, eliminating the need for manual feature extraction and harnessing the latent information embedded within the raw signals. The most prevalent models can be broadly categorised into convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrids of CNN and RNN. [PERSON] et al. [11] introduced a method that leverages both the time and frequency domains of PPG signals as inputs, employing a CNN framework to forecast BP without requiring explicit feature extraction. When tested on a dataset comprising 26 volunteers, the approach yielded a total mean absolute error (MAE) of 5.28 mmHg and 4.92 mmHg for estimated systolic and diastolic blood pressure, respectively. [PERSON] and team [12] reformulated BP estimation as a sequence prediction challenge, utilising a deep recurrent neural network to model the temporal dependencies inherent in BP dynamics. Their experiments, conducted on a dataset of 84 healthy subjects, revealed that their model surpassed the accuracy of conventional BP prediction models. While these studies demonstrated impressive performance on smaller datasets, findings from similar investigations on larger public datasets differed. [PERSON] et al.