Non-Contact Blood Pressure Estimation Using Accurate Cardiac Movements Extracted by Hidden Semi-Markov Model

This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner. Cardiac movements have been regarded as essential factors for BP estimation. However, accurately obtaining these movements by a radar system remains a challenging problem because these movements are too inconspicuous and could be easily hindered by respiration and random noise. In this paper, we propose a method that mainly focuses on cardiac feature extraction in radar-based BP monitoring. First, we employ an integrated-spectrum waveform. It is derived from short-time Fourier transform (STFT) and is capable of recording and preserving minor cardiac activities. Compared with the pulse-wave signals used in previous works, the integrated-spectrum focuses on energy changes introduced by short and high-frequency vibrations. It can eliminate the interference of respiration and random noise, and cardiac contractile movement can be reserved accurately. Second, we propose a cardiac features estimation method in which a hidden semi-Markov model (HSMM) is applied to the integrated-spectrum for feature extraction. Compared to the pulse wave signal, the Root-Mean-Square Error (RMSE) of the estimated interbeat intervals (IBI), Systolic time, and Diastolic time is reduced by 44.2%, 73.3%, and 76.7% respectively. The estimated accurate cardiac features are further used as inputs for a Random Forest model for BP prediction. Though previous work required the subject to hold one's breath, we achieved a comparable prediction accuracy even when our subject is breathing normally. The Diastolic BP (DBP) error of our model is
$4.27\pm 5.84$
mmHg (Mean Absolute Difference ± Standard Deviation), and the Systolic BP (SBP) error is
$6.63\pm 8.95$
mmHg.