Objective To assess the correlation between bispectral index (BIS) and richmond agitation sedation scale (RASS) and sedation-agitation scale (SAS) through the spearman correlation coefficient by systematic review. Methods Databases including PubMed, EMbase, Web of Science, The Cochrane Library (Issue 7, 2016), CNKI, VIP, WanFang Data and CBM were searched from inception to July 2016 to collect literature on the correlation between BIS and RASS and SAS. The studies were screened according to the inclusion and exclusion criteria. After extracting data and assessing the quality of the included studies, meta-analysis was conducted using Comprehensive Meta Analysis 3.0 software. Results A total of 12 studies involving 397 patients were included. BIS was positively correlated with RASS score and SAS, and the summary correlation coefficient was 0.742 with 95% CI 0.678 to 0.795 and 0.605 with 95% CI 0.517 to 0.681, respectively. Conclusion BIS has a good correlation with RASS and SAS, which will provide more options for assessing sedation of patients with mechanical ventilation in ICU.
Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of x, y and z axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.