Vital signal monitoring is conducive to the diagnosis of a variety of diseases, which can be used to learn patients' health status. To avoid cross infection (for example infectious diseases), non-contact vital sign detection has attracted more and more attention. In this paper, an important hospital scenario, i.e., the medical staff and infectious disease patients are separated by glass wall, is considered. Under the considered scenario, an through-and-wall impulse radio ultra-wideband (IR-UWB) system placed on the room of the medical staff is adopted to obtain the information of the patient's heart rate and breathing rate, thus cutting off direct contact between the medical staff and infectious disease patients. To this end, the basic idea is that the received through-and-wall IR-UWB echo signal is subtracted to the environmental noise via a background noise removal method, then an modified two-dimension ensemble empirical mode decomposition (EEMD) method is proposed to analyze the remained echo signal, thus obtaining respiration rate and heartbeat rate. Compared with the conventional two-dimension EEMD, the modified two-dimension EEMD method has lower complexity and increases the computational speed. The experimental results of the proposed method show that the average errors of breathing and heart rates are 1.6% and 4.3%, respectively.

Parkinson’s disease (PD) can be divided into postural instability and difficult gait (PIGD) and tremor dominance (TD) subtypes. However, potential neural markers located in the dorsal ventral side of the subthalamic nucleus (STN) for delineating the two subtypes of PIGD and TD have not been demonstrated. Therefore, this study aimed to investigate the spectral characteristics of PD on the dorsal ventral side. The differences in the β oscillation spectrum of the spike signal on the dorsal and ventral sides of the STN during deep brain stimulation (DBS) were investigated in 23 patients with PD, and coherence analysis was performed for both subtypes. Finally, each feature was associated with the Unified Parkinson’s Disease Rating Scale (UPDRS). The β power spectral density (PSD) in the dorsal STN was found to be the best predictor of the PD subtype, with 82.6% accuracy. The PSD of dorsal STN β oscillations was greater in the PIGD group than in the TD group (22.17% vs. 18.22%; p < 0.001). Compared with the PIGD group, the TD group showed greater consistency in the β and γ bands. In conclusion, dorsal STN β oscillations could be used as a biomarker to classify PIGD and TD subtypes, guide STN-DBS treatment, and relate to some motor symptoms.