The noninvasive peripheral oxygen saturation (SpO₂) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects’ PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO₂ and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.

Nuclear magnetic resonance (NMR) has various applications in underground engineering, particularly in tunnels, where it plays a crucial role in the early detection of potential disaster-inducing water sources. Water bodies that pose a threat in tunnels often manifest as fissure water nestled between rock masses. However, the current application of tunnel NMR detection is limited to the one-dimensional (1-D) layered models, specifically magnetic resonance sounding (MRS), which fails to capture the spatial structure of such fissure water. To precisely locate disaster-inducing water bodies, the MRS method needs to be extended to two-dimensional (2-D) structural detection. Thus, we propose a method for imaging the structure of tunnel fissure water based on multipoint geostatistical Bayesian framework. This method relies solely on data acquired from 1-D MRS measurements, complemented by borehole data obtained during the construction process, to detect 2-D structures of fissure water, without the need of complicated 2-D measurement process. Furthermore, we enhance the Markov chain Monte Carlo (MCMC) algorithm to improve the sampling performance of Bayesian method through a biased-estimation constraint strategy. Through synthetic experiments, we demonstrate the effectiveness of this method in predicting the intricate distribution of fissure water and its interconnected structure. Practical validation is provided on the basis of a series of field-measured data collected in an actual tunnel.