Cuff-less wearable biosensor in continuous noninvasive human radial artery pulse waveform and blood pressure measurement using self-mixing interferometry

In this paper, we propose a compact, wearable biosensor for the noninvasive measurement of human radial artery pulse waveform curve (PWC) and blood pressure (BP). In this system, self-mixing interferometry (SMI) technology is employed to measure the weak arterial vascular deformation, enabling accurate PWC retrieval. Based on the reconstructed PWC features, BP values are precisely estimated by means of deep learning method. Here continuous wavelet transform (CWT), enabling visualization of the relationship between the SMI signal temporal frequency components and the PWC characteristics, is highlighted for PWC flipping points seeking and convolutional neural network (CNN) input parameter acquisition. For the first time, a novel deep learning network preprocessing method is proposed that allows direct feature extraction from the CWT scalogram of SMI signal without the complicated PWC reconstruction algorithm. The robustness and accuracy of our device are validated by a series of clinical measurements, mean absolute error (MAE) and standard deviation (STD) values are calculated and compared with the existing models. We approach minimal BP estimation results (MAE ± STD) of 1.41 ± 1.89 mmHg for systolic blood pressure (SBP) and 1.78 ± 2.01 mmHg for diastolic blood pressure (DBP), respectively. The luxuriant novelties and remarkable performance clearly demonstrate our wearable sensor’s great potential in BP monitoring, and other clinical applications.