###### Abstract  
Traditional cuff-based blood pressure measurement methods suffer from issues such as intermittency and applicability, while cuff-less continuous blood pressure estimation techniques are increasingly gaining attention due to their non-invasive and continuous monitoring advantages. In this paper, aiming at the challenges faced by existing cuff-less continuous blood pressure estimation models in terms of accuracy, data requirements, and generalization ability, a series of innovative approaches are proposed. Deep learning techniques are introduced to design an end-to-end blood pressure estimation model with high accuracy, ease of training, and strong generalization ability. To address the insufficient accuracy of traditional neural networks in cuff-less continuous blood pressure estimation, we propose an end-to-end, beat-to-beat blood pressure estimation model that combines the temporal convolutional network (TCN) and convolutional block attention module (CBAM). By enhancing the model's ability to process time series data and focus on key features of photoplethysmography (PPG), the blood pressure estimation accuracy during the resting state is significantly improved. The absolute mean error and standard deviation of systolic blood pressure (SBP) estimation using the algorithm in this chapter on the University of California, Irvine (UCI) physiological signal dataset are 5.3482 mmHg and 8.3410 mmHg, respectively, which are superior to other deep learning models based on convolutional neural network and recurrent neural network architectures. cuffless blood pressure estimation; time convolutional network; attention mechanism +
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This approach aids doctors in accurately diagnosing patients' conditions and devising more tailored treatment plans. It allows for the timely detection of blood pressure variations and enables the adoption of necessary measures to control the progression of hypertension and other cardiovascular diseases. This holds immense significance in preventing cardiovascular and cerebrovascular events and reducing disease risks. Nevertheless, existing continuous blood pressure monitoring methods often require invasive devices like intravascular catheters, which are not only cumbersome to operate but also prone to infections and other complications [3]. Given the limitations and shortcomings of these traditional blood pressure measurement techniques in practical applications, there is an urgent need for a non-invasive, continuous, and accurate blood pressure measurement method. In recent years, advancements in sensing technology, computer science, and artificial intelligence have spurred increasing numbers of researchers to explore continuous blood pressure estimation methods utilising biosignals such as PPG and electrocardiogram (ECG). When juxtaposed with conventional blood pressure measurement techniques, these novel approaches exhibit various advantages, including real-time continuous monitoring, non-invasiveness, and user-friendliness [4]. Numerous notable achievements have been attained in studies focused on estimation methodologies that integrate PPG and ECG data, showcasing impressive accuracy in blood pressure estimations. Nonetheless, there are certain drawbacks associated with ECG signal measurement. Specifically, it necessitates the prolonged attachment of patch electrodes to the skin, which can hinder skin ventilation and induce discomfort for the user [5]. Furthermore, blood pressure estimation techniques relying on multiple physiological signals, including ECG and PPG, encounter challenges related to data synchronisation, information fusion, elevated development costs, complex implementation, and limited noise resistance [6]. In contrast, methodologies solely reliant on a single PPG signal offer a streamlined approach, eliminating the complexities and inconveniences associated with multiple sensors. This approach simplifies the data collection and processing workflow.