A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography

Blood pressure monitoring is very important for the prevention of cardiovascular diseases. In this paper, we proposed a multi-type features fusion (MTFF) neural network model for blood pressure (BP) prediction based on photoplethysmography (PPG). The model includes two convolutional neural networks (CNN) which used to train the morphological and frequency spectrum features of PPG signal, and one Bi-directional long short term memory (BLSTM) network which used to train the temporal features of PPG signal. These multi-features were fused through a specific fusion module after training, so more information of PPG signals were obtained and the hidden relationship between the fused features and blood pressure was established. The standard deviation (STD) and mean absolute error (MAE) of the fusion model are 7.25 mmHg and 5.59 mmHg respectively for systolic blood pressure (SBP), 4.48 mmHg and 3.36 mmHg respectively for diastolic blood pressure (DBP). The results are in full compliance with the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) international standards. We conclude that the MTFF neural network proposed in this paper can accurately predict blood pressure. The significant difference from the traditional methods of BP prediction based on manual calculation of features is that our method automatically extracts PPG features through the deep learning model which can easily handle the complicated and tedious calculation. Compared with other similar BP prediction methods based on deep learning, three different features are trained and fused, which further improves the accuracy of BP prediction.

Automated summary

The proposed multi-type features fusion (MTFF) neural network model based on photoplethysmography (PPG) accurately predicts blood pressure by automatically extracting PPG signal features through deep learning, avoiding tedious calculations, and training and fusing multiple features to improve prediction accuracy.