###### Abstract  
Monitoring people's blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard. blood pressure estimation; electrocardiogram; attention mechanism; CNN-LSTM +
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Some studies have shown that ECG and PPG signals can be used to effectively estimate BP and increase the accuracy of the estimation [6; 7; 8; 9; 10; 11]. [PERSON] et al. applied a support vector machine and random forest regression model to estimate the BP [7]. [PERSON] et al. adopted a nonlinear equation to express the relationship between pulse arrival time (PAT) and BP [8]. [PERSON] and [PERSON] used linear regression to find the relationship between BP, PAT, and heart rate [9]. For these approaches, only simple machine learning methods were adopted to estimate BP. Therefore, novel machine learning methods have been successfully used in many applications and can effectively increase the accuracy of BP estimation. Recently, deep learning had been successfully applied in many applications, and then much research had been proposed to accurately estimate BP [10; 11; 12; 13; 14; 15]. [PERSON] et al. used a backpropagation neural network and radial basis function to estimate the BP. However, the backpropagation neural network has weak generalization ability [10]. [PERSON] applied deep learning and human physiological characteristics to estimate BP, which can obtain better results compared with backpropagation neural networks [11]. However, it has high computational complexity and is not suitable for real-time applications. [PERSON] et al. proposed a convolutional neural network (CNN)-based BP estimation model, which processes sequential ECG and PPG signals for estimating BP [12]. However, the CNN-based BP estimation model did not use the time-serious information of ECG and PPG signals. Therefore, [PERSON] used long short-term memory (LSTM) to improve the performance of the BP estimation [13]. Nowadays, CNN-LSTM has been developed to combine the advantages of CNN and LSTM. CNN-LSTM can estimate BP by using ECG and PPG signals [14; 15], but these methods are still not convenient enough. To improve the performance of CNN-LSTM, an attention mechanism had been proposed and applied to many applications [16; 17]. Therefore, integrating the attention mechanism with CNN-LSTM can efficiently increase the accuracy of BP estimation. In this study, an attention mechanism-based CNN-LSTM is proposed to predict BP by using ECG and PPG signals. To precisely represent the characteristics of ECG and PPG signals, time and frequency domain ECG and PPG signals are selected as the inputs of neural networks. To correctly extract the features of ECG and PPG signals, the CNNs are adopted to process the time and frequency domain signals. To identify the meaningful features, the attention mechanism is applied.