In this paper, a feature extraction algorithm of weighted multiple multiscale entropy is proposed to solve the problem of information loss which is caused in the multiscale process of traditional multiscale entropy. Algorithm constructs the multiple data sequences from large to small on each scale. Then, considering the different contribution degrees of multiple data sequences to the entropy of the scale, the proportion of each sequence in the scale sequence is calculated by combining the correlation between the data sequences, so as to reconstruct the sample entropy of each scale. Compared with the traditional multiscale entropy the feature extraction algorithm based on weighted multiple multiscale entropy not only overcomes the problem of information loss, but also fully considers the correlation of sequences and the contribution to total entropy. It reduces the fluctuation between scales, and digs out the details of electroencephalography (EEG). Based on this algorithm, the EEG characteristics of autism spectrum disorder (ASD) children are analyzed, and the classification accuracy of the algorithm is increased by 23.0%, 10.4% and 6.4% as compared with the EEG extraction algorithm of sample entropy, traditional multiscale entropy and multiple multiscale entropy based on the delay value method, respectively. Based on this algorithm, the 19 channel EEG signals of ASD children and healthy children were analyzed. The results showed that the entropy of healthy children was slightly higher than that of the ASD children except the FP2 channel, and the numerical differences of F3, F7, F8, C3 and P3 channels were statistically significant (P<0.05). By classifying the weighted multiple multiscale entropy of each brain region, we found that the accuracy of the anterior temporal lobe (F7, F8) was the highest. It indicated that the anterior temporal lobe can be used as a sensitive brain area for assessing the brain function of ASD children.
ObjectiveTo systematically review the accuracy of the global leadership initiative on malnutrition (GLIM) in screening patients with cancer malnutrition. MethodsThe PubMed, Web of Science, Cochrane Library, Embase, CNKI, WanFang, SinoMed, and VIP databases were electronically searched to collect diagnostic tests related to the objects from January 2019 to March 2024. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using Stata 15.0 software. ResultsA total of 12 studies were included. The results of the meta-analysis showed that GLIM criteria for the diagnosis of malnutrition had a sensitivity of 0.69 (95%CI 0.63 to 0.76), specificity of 0.90 (95%CI 0.83 to 0.95), positive likelihood ratio of 7.18 (95%CI 4.17 to 12.35), negative likelihood ratio of 0.34 (95%CI 0.28 to 0.41), diagnostic odds ratio of 21.21 (95%CI 11.96 to 37.62), and area under the curve of 0.84 (95%CI 0.80 to 0.87). ConclusionCurrent evidence suggests that the GLIM criteria have diagnostic value as a tool for malnutrition in cancer patients, with moderate overall diagnostic efficacy.