Blood Pressure and Heart Rate Measurements Using Photoplethysmography with Modified LRCN

In this study, single-channel photoplethysmography (PPG) signals were used to estimate the heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A deep learning model was proposed using a long-term recurrent convolutional network (LRCN) modified from a deep learning algorithm, the convolutional neural network model of the modified inception deep learning module, and a long short-term memory network (LSTM) to improve the model's accuracy of BP and HR measurements. The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository. How to design a filter of PPG signals and how to choose the loss functions for deep learning model were also discussed in the study. Finally, the stability of the proposed model was tested using a 10-fold cross-validation, with an MAE +/- SD of 2.942 +/- 5.076 mmHg for SBP, 1.747 +/- 3.042 mmHg for DBP, and 1.137 +/- 2.463 bpm for the HR. Compared with its existing counterparts, the model entailed less computational load and was more accurate in estimating SBP, DBP, and HR. These results established the validity of the model.

Automated summary

A deep learning model was proposed in this study to estimate heart rate, diastolic blood pressure, and systolic blood pressure using single-channel photoplethysmography signals, achieving improved accuracy with the use of a long-term recurrent convolutional network and long short-term memory network. The model was validated through stability testing with promising results, demonstrating its effectiveness compared to existing counterparts.