A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation

• Published in: Neural networks 2022-08, Vol.152, p.181-190
• Main Authors: Chen, Yongyi, Zhang, Dan, Karimi, Hamid Reza, Deng, Chao, Yin, Wutao
• Format: Article
• Language: English
• Subjects: Blood pressure (BP); BP range constraint; Parallel mixed domain attention mechanism; Photoplethysmography (PPG); Receptive field; Soft threshold
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2022.04.017

Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitable for continuous measurement. Aiming at the deficiencies in existing studies, a novel cuffless BP estimation framework of Receptive Field Parallel Attention Shrinkage Network (RFPASN) and BP range constraint is proposed. Firstly, RFPASN uses the multi-scale large receptive field convolution module to capture the long-term dynamics in the photoplethysmography (PPG) signal without using long short-term memory (LSTM). On this basis, the features acquired by the parallel mixed domain attention module are used as thresholds, and the soft threshold function is used to screen the input features to enhance the discriminability and robustness of features, which can significantly improve the prediction accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Finally, in order to prevent large fluctuations in the prediction results of RFPASN, RFPASN based on BP range constraint is proposed to make the prediction results of RFPASN more accurate and reasonable. The performance of the proposed method is demonstrated on a publically available MIMIC-II database. The database contains normal, hypertensive and hypotensive people. We have achieved MAE of 1.63/1.59 (DBP) and 2.26/2.15 (SBP) mmHg for BP on total population of 1562 subjects. A comparative study shows that the proposed algorithm is more promising than the state-of-the-art. •In order to effectively extract the information that can represent BP changes in the PPG signal, the BP prediction model based on Receptive Field Parallel Attention Shrinkage Network (RFPASN) is built in this paper. It is an “end-to-end” BP estimation method.•We propose Receptive Field Parallel Attention Shrinkage Block (RFPASB), which can capture the long-term dynamics in the PPG signal. Then, the features with strong discriminability are extracted by parallel mixed domain attention module, which is used as the threshold of soft threshold function, so as to retain important features and eliminate redundant information.•In order to prevent unreasonable prediction results from RFPASN and limit BP prediction results to a reasonable range, we construct abnormal BP discriminative model to limit the range of RFPASN prediction results to further improve the accuracy and reasonablene