Blood Pressure Estimation from Photoplethysmography with Motion Artifacts Using Long Short Term Memory Network

2022 ◽  
Vol 54 ◽  
pp. 31-39
Author(s):  
Anuradhi Welhenge ◽  
Attaphongse Taparugssanagorn
Keyword(s):  
Blood Pressure ◽  
Elderly Population ◽  
Short Term Memory ◽  
Continuous Measurement ◽  
Motion Artifacts ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Blood Pressure Estimation

Continuous measurement of the Blood Pressure (BP) is important in hypertensive patientsand elderly population. Traditional cuff based methods are difficult to use since it is uncomfortable towear a cuff throughout the day. A more suitable method is to estimate the BP using the Photoplethysmography(PPG) signal. However, it is difficult to estimate a BP when the PPG is corrupted withMotion Artifacts (MAs). In this paper, Long Short Term Memory (LSTM) an extension of RecurrentNeural Networks (RNN) is used used to improve the accuracy of the estimation of the BP from thecorrupted PPG. It shows that an accuracy of 97.86 is achieved.

scholarly journals Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

2021 ◽  
Vol 11 (24) ◽  
pp. 12019
Author(s):  
Chia-Chun Chuang ◽  
Chien-Ching Lee ◽  
Chia-Hong Yeng ◽  
Edmund-Cheung So ◽  
Yeou-Jiunn Chen
Keyword(s):  
Blood Pressure ◽  
Neural Networks ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Blood Pressure Estimation ◽  
Fully Connected ◽  
Electrocardiogram Ecg

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.

scholarly journals Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 96
Author(s):  
Dongseok Lee ◽  
Hyunbin Kwon ◽  
Dongyeon Son ◽  
Heesang Eom ◽  
Cheolsoo Park ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Short Term Memory ◽  
Fine Tuning ◽  
Estimation Model ◽  
Short Term ◽  
Term Memory ◽  
Training Time ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Continuous Blood Pressure

Continuous blood pressure (BP) monitoring is important for patients with hypertension. However, BP measurement with a cuff may be cumbersome for the patient. To overcome this limitation, various studies have suggested cuffless BP estimation models using deep learning algorithms. A generalized model should be considered to decrease the training time, and the model reproducibility should be taken into account in multi-day scenarios. In this study, a BP estimation model with a bidirectional long short-term memory network is proposed. The features are extracted from the electrocardiogram, photoplethysmogram, and ballistocardiogram. The leave-one-subject-out (LOSO) method is incorporated to generalize the model and fine-tuning is applied. The model was evaluated using one-day and multi-day tests. The proposed model achieved a mean absolute error (MAE) of 2.56 and 2.05 mmHg for the systolic and diastolic BP (SBP and DBP), respectively, in the one-day test. Moreover, the results demonstrated that the LOSO method with fine-tuning was more compatible in the multi-day test. The MAE values of the model were 5.82 and 5.24 mmHg for the SBP and DBP, respectively.

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