Blind source separation technique based on independent component analysis (ICA) can separate blood volume pulse (BVP) from the facial video and then realize the telemetry of heart rate, blood oxygen saturation, respiratory rate and other vital signs parameters. However, the superiority of ICA in BVP extraction has not been demonstrated in the existing researches. Some researchers suggested using traditional G-channel method for BVP extraction (G-BVP) instead of ICA method (ICA-BVP). This study investigated the applicability of ICA-BVP comparatively. To solve the inherent permutation problem of ICA, a spectral kurtosis-based method was proposed for BVP identification. The experimental results based on the facial video datasets from 9 subjects shows that ICA-BVP method has apparent advantages in motion artifacts attenuation and ambient light changes elimination. The kurtosis-based method achieved a good performance in BVP identification and dynamic heart rate (HR) estimation. In practical application, the proposed ICA-BVP method could present a better stability and accuracy in vital signs parameters extraction.
We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4–3 Hz), θ wave (4–7 Hz), α wave (8–13 Hz) and β wave (14–30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
ObjectiveTo systematically review the efficacy and safety of orlistat in the treatment of overweight and obese type 2 diabetes mellitus (T2DM). Methods PubMed, EMbase, The Cochrane Library, CNKI, WanFang Data and VIP databases were electronically searched to collect randomized controlled trials (RCTs) on the efficacy and safety of orlistat in the treatment of overweight and obese T2DM patients from inception to June 29th, 2022. Two reviewers independently screened the literature, extracted data and assessed the risk of bias of the included studies; then, meta-analysis was performed by using Stata 16.0 software. Results A total of 24 RCTs involving 3 702 patients were included. The results of meta-analysis showed that compared with control group, orlistat group could significantly decrease the levels of fasting blood glucose (WMD=1.04, 95%CI 0.80 to 1.29, P<0.001), 2h postprandial blood glucose (WMD=1.17, 95%CI 0.78 to 1.56, P<0.001) and hemoglobin A1c (WMD=0.84, 95%CI 0.51 to 1.17, P<0.001), and the levels of total cholesterol, triglyceride, low density lipoprotein, body weight and body mass index also significantly decreased (P<0.001). The incidence of adverse events such as increased defecation and abdominal pain of orlistat was slightly higher than that of control group; however, most could be self-healing.Conclusion Current evidence shows that orlistat can effectively and safely improve blood sugar, lipid index in overweight and obese T2DM patients. Due to limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.