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.

Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.