In order to solve the problem of early classification of Alzheimer’s disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD (n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer’s disease.
ObjectiveTo investigate the safety and feasibility of the treatment of laparoscopic splenectomy for patients with traumatic splenic rupture. MethodsBetween October 2006 and October 2009, 48 cases of traumatic splenic rupture underwent laparoscopic splenectomy were analyzed in this hospital. According to the differrent styles of splenic stalk, different operative methods were taken， including titanic clipping in 12 cases， titanic clipping combining silk suture ligation in 8 cases， snare combining titanic clipping in 10 cases， LigaSure in 8 cases， and EndoGIA in 8 cases. ResultsLaparoscopic splenectomy was successfully completed in 32 cases; Handassisted laparoscopic splenectomy was applied in 14 cases, and 2 cases were converted to laparotomy because of tight spleen adhesion with surrounding tissues and bleeding rupture of the short gastric vessels. The operation time was 120－170 min with an average 140 min; the estimated intraoperative amount of blood loss was 300－1 200 ml with an average 800 ml. No postoperative complication occurred such as gastric fistula, pancreatic fistula or hemorrhage. Conclusion According to the differrent styles of splenic stalk, individual operative method can improve mission success rate in the laparoscopic splenectomy in traumatic splenic rupture.