In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.

Because of various inflammations and fractures of low limbs, the number of patients with knee joint stiffness is increasing. Walkers are commonly prescribed to improve these patients' stability and ambulatory ability. The evaluation on the assisted walking effect has become a hot problem, whose prerequisite is a comprehensive mechanical understanding of the upper extremity force. In order to study the upper extremity kinetics during walker-assisted gait of knee joint stiffness, this paper developed a new method to collect upper extremity kinetics data based on a special-designed walker dynamometer system. Handle reaction vector (HRV) data were collected from 15 healthy right-handed young subjects by simulation experiments for four knee joint stiffness modes. T test and support vector machine (SVM) were used to analyze these HRV data. The results indicated that knee joint stiffness had a great influence on the upper extremity force during the walker-assisted walking. The proposed method is hoped to beneficially influence walkerassisted gait retraining strategies for knee joint stiffness.

Background: Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder of the neuro-developmental type, marked by an ongoing pattern of inattention or hyperactivity/impulsivity, which interferes with functioning or development. The disorder affects approximately 5-7 % children and 2-5 % of adults worldwide. Numerous studies have indicated that genetic factors predominate the causes for ADHD. Nevertheless, no systematic study has summarized these findings and provided an objective and complete list of genes with a reported association to ADHD. Methods: Literature and enrichment metrics analyses were used to discover genes of specific significance associated with ADHD. We conducted a literature data mining (LDM) of over 2,410 articles covering publications from Jan. 1988 to Apr. 2016, where 235 genes were reported to be associated with the disease. Then we performed a gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) to study the functional profile and pathogenic significance of these genes associated with ADHD. Lastly, we performed a network connectivity analysis (NCA) to study the associations between the reported genes. Results: 181/235 genes enriched 100 pathways (p<1.1e-007), demonstrating multiple associations with ADHD. Twelve genes were discovered to be associated with ADHD, in terms of both functional diversity and replication frequency, including SLC6A3, DRD4, BDNF, DRD2, HTR2A, DBH, HTR1B, DRD5, GRM7, DRD3, TH and GRIN2A. In addition, one novel gene, SHANK2, was suggested worthy of further study. Moreover, SNEA and NCA results indicated that many of these genes form a functional network, playing roles in the pathogenesis of other ADHD related disorders. Conclusion: Our results suggest that the genetic causes of ADHD are linked to a genetic and functional network composed of a large group of genes. The gene lists, together with the literature and enrichment metrics provided in this study, could serve as groundwork for further biological/genetic studies in the field.