Feature selection of stabilometric parameters based on principal component analysis

This study addresses the challenge of identifying the features of the Centre of pressure (COP) trajectory that are most sensitive to postural performance, with the aim of avoiding redundancy and allowing a straightforward interpretation of the results. Postural sway in 50 young, healthy subjects was measured by a force platform. Thirty-seven stabilometric parameters were computed from the one-dimensional and two-dimensional COP time series. After normalisation to the relevant biomechanical factors, by means of multiple regression models, a feature selection process was performed based on principal component analysis. Results suggest that COP two-dimensional time series can be primarily characterised by four parameters, describing the size of the COP path over the support surface; the principal sway direction; and the shape and bandwidth of the power spectral density plot. COP one-dimensional time series (antero-posterior (AP) and medio-lateral (ML)) can be characterised by six parameters describing COP dispersion along the AP direction; mean velocity along the ML and AP directions; the contrast between ML and AP regulatory activity; and two parameters describing the spectral characteristics of the COP along the AP direction. On the basis of the results obtained, some guidelines are suggested for the choice of stabilometric parameters to use, with the aim of promoting standardisation in quantitative posturography.     

Natural image profiles are most likely to be step edges

We introduce Geometric Texton Theory (GTT), a theory of categorical visual feature classification that arises through consideration of the metamerism that affects families of co-localised linear receptive-field operators. A refinement of GTT that uses maximum likelihood (ML) to resolve this metamerism is presented. We describe a method for discovering the ML element of a metamery class by analysing a database of natural images. We apply the method to the simplest case--the ML element of a canonical metamery class defined by co-registering the location and orientation of profiles from images, and affinely scaling their intensities so that they have identical responses to 1-D, zeroth- and first-order, derivative of Gaussian operators. We find that a step edge is the ML profile. This result is consistent with our proposed theory of feature classification.     

The cerebral hemispheres differ in their capacity for content-to-level binding but not for identification: Evidence from conjunction errors obtained with bilateral hierarchical stimuli

It is widely assumed that the right and left cerebral hemispheres are specialised for processing the global and local information of hierarchical stimuli, respectively. This idea has further been specified in the content-to-level binding theory (Hübner & Volberg, 2005) by stating that the hemispheres differ in their efficiency for binding the contents of a stimulus to their respective level. In contrast, it is assumed that the hemispheres do not differ in their capacity for the identification of the information at the two levels. This latter hypothesis was tested in the present experiment by presenting a hierarchical letter to each visual field. As expected, there were visual field effects only for errors involving the erroneous binding between a letter and a level. For errors that result from the mislocalisation of a letter, there were no visual field effects. Together, the data support the hypothesis that the hemispheres do not differ in their identification capacity.     

Neuro-fuzzy feature evaluation with theoretical analysis

The article provides a fuzzy set theoretic feature evaluation index and a connectionist model for its evaluation along with their theoretical analysis. A concept of weighted membership function is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. It is shown theoretically that the evaluation index has a fixed upper bound and a varying lower bound, and it monotonically increases with the lower bound. A relation between the evaluation index, interclass distance and weighting coefficients is established. Effectiveness of the algorithms for evaluating features both individually and in a group (considering their independence and dependency) is demonstrated along with comparisons on speech, Iris, medical and mango-leaf data. The results are also validated using scatter diagram and k-NN classifier.     

银质针疗法及其操作技术

银质针是上海伤科八大家之一——陆氏伤科的治伤工具,以“循经取穴”、“以痛为腧”和“功能运动中的痛点”为取穴原则,在软组织疼痛性疾病的治疗方面具有显著优势。本文介绍银员针疗法的发展应用概况、银质针的规格和特点、银质针疗法的取穴原则和银质针的操作方法。     

Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study

Objective

We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification.

Methods and materials

The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely, Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof.

Results

We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value.

Conclusions

Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality).     

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C

Objective

Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance.

Methods and materials

The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis).

Results

Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the evolutionary method is viewed in comparison to the traditional one. The multifaceted discrimination into all five degrees of fibrosis and the slightly less difficult common separation into solely three related stages are both investigated. The resulting performance proves the superiority over the standard support vector classification and the attained formula is helpful in providing an immediate calculation of the liver stage for new cases, while establishing the presence/absence and comprehending the weight of each medical factor with respect to a certain fibrosis level.

Conclusion

The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage. Perhaps most importantly, it significantly surpasses the classical support vector machines, which are both widely used and technically sound. All these therefore confirm the promise of the new methodology towards a dependable support within the medical decision-making.     

An affective computing approach to physiological emotion specificity: toward subject-independent and stimulus-independent classification of film-induced emotions

The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k-nearest neighbors method), and cross-validation method (subject- and stimulus-(in)dependence). Analyses were run on a data set of 34 participants watching two sets of three 10-min film clips (fearful, sad, neutral) while autonomic, respiratory, and facial muscle activity were assessed. Results demonstrate that the three states can be classified with high accuracy by most classifiers, with the sparsest model having only five features, even for the most difficult task of identifying the emotion of an unknown subject in an unknown situation (77.5%). Implications for choosing PCA parameters are discussed.     

医德教育生态环境的本质内涵与理论特征论析

医德教育生态环境是大学生道德发展的生态化环境,主要包括人化环境、物化环境、活化环境、文化环境等。高校德育生态环境的最大特点是生态性,最突出的特点是育德性。     

综合医院精神科联络会诊分析

目的探讨综合医院各临床科室请精神科联络会诊的特点。方法收集2007年7月～2008年8月共计14个月的会诊病例,通过会诊邀请单及自编的会诊情况问卷收集资料。采用SPSS12．0进行统计。结果邀请会诊年龄在5～97岁,平均（52±21）岁,男性占50％以上;按会诊分配比例由高到低的科室为神经内科、骨科、脑外科、老年病科、消化科、心内科等。结论精神科联络会诊在各年龄段均有,以中老年为主;申请科室中除神内、脑外以外,骨科和老年病科也较多;年转入率减低,而精神科药物使用在各科室逐渐增多。生物-心理-社会医学模式渐入人心。