Shang-Ming Zhou

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.

Guest EditorialIntegrated Healthcare Information Systems

The use of integrated information systems for healthcare has been started more than a decade ago. In recent years, rapid advances in information integration methods have spurred tremendous growth in the use of integrated information systems in healthcare delivery. Various techniques have been used for probing such integrated systems. These techniques include service-oriented architecture (SOA), EAI, workflow management, grid computing, and others. Many applications require a combination of these techniques, which gives rise to the emergence of enterprise systems in healthcare. Development of the techniques originated from different disciplines has the potential to significantly improve the performance of enterprise systems in healthcare. This editorial paper briefly introduces the enterprise systems in the perspective of healthcare informatics.

Alpha-Level Aggregation: A Practical Approach to Type-1 OWA Operation for Aggregating Uncertain Information with Applications to Breast Cancer Treatments

Type-1 Ordered Weighted Averaging (OWA) operator provides us with a new technique for directly aggregating uncertain information with uncertain weights via OWA mechanism in soft decision making and data mining, in which uncertain objects are modeled by fuzzy sets. The Direct Approach to performing type-1 OWA operation involves high computational overhead. In this paper, we define a type-1 OWA operator based on the \alpha-cuts of fuzzy sets. Then, we prove a Representation Theorem of type-1 OWA operators, by which type-1 OWA operators can be decomposed into a series of \alpha-level type-1 OWA operators. Furthermore, we suggest a fast approach, called Alpha-Level Approach, to implementing the type-1 OWA operator. A practical application of type-1 OWA operators to breast cancer treatments is addressed. Experimental results and theoretical analyses show that: 1) the Alpha-Level Approach with linear order complexity can achieve much higher computing efficiency in performing type-1 OWA operation than the existing Direct Approach, 2) the type-1 OWA operators exhibit different aggregation behaviors from the existing fuzzy weighted averaging (FWA) operators, and 3) the type-1 OWA operators demonstrate the ability to efficiently aggregate uncertain information with uncertain weights in solving real-world soft decision-making problems.

On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection

Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of rules, while maintaining the systems approximation performance. In this paper, four novel indexes for ranking the relative contribution of type-2 fuzzy rules are proposed, which are termed R-values, c-values, omega1-values, and omega2-values. The R-values of type-2 fuzzy rules are obtained by applying a QR decomposition pivoting algorithm to the firing strength matrices of the trained fuzzy model. The c-values rank rules based on the effects of rule consequents, while the omega1-values and omega2-values consider both the rule-base structure (via firing strength matrices) and the output contribution of fuzzy rule consequents. Two procedures for utilizing these indexes in fuzzy rule selection (termed ldquoforward selectionrdquo and ldquobackward eliminationrdquo) are described. Experiments are presented which demonstrate that by using the proposed methodology, the most influential type-2 fuzzy rules can be effectively retained in order to construct parsimonious type-2 fuzzy models.

No Increased Rate of Acute Myocardial Infarction or Stroke Among Patients with Ankylosing Spondylitis—A Retrospective Cohort Study Using Routine Data

OBJECTIVES To examine if people with ankylosing spondylitis (AS) are at higher risk of acute myocardial infarction (MI) or stroke compared to those without AS. METHODS Primary care records were linked with all hospital admissions and deaths caused by MI or stroke in Wales for the years 1999-2010. The linked data were then stratified by AS diagnosis and survival analysis was used to obtain the incidence rate of MI and separately cerebrovascular disease (CVD)/stroke. Cox regression was used to adjust for gender and age. Logistic regression was used to examine prevalence of diabetes, hypertension, or hyperlipidemia for those with AS compared to those without. RESULTS There were 1686 AS patients (75.9% male, average age 46.1 years) compared to 1,206,621 controls (48.9% male, average age 35.9 years). Age- and gender-adjusted hazard ratios for MI were 1.28 (95% CI: 0.93 to 1.74) P = 0.12, and for CVD/stroke 1.0 (95% CI: 0.73 to 1.39) P = 0.9, in AS compared to controls. The prevalence of diabetes and hypertension, but not hyperlipidemia/hypercholesterolemia, was higher in AS. CONCLUSIONS There is no increase in the MI or CVD/stroke rates in patients with AS compared to those without AS, despite higher rates of hypertension, which may be related to nonsteroidal anti-inflammatory drug use.

Incidence of Campylobacter and Salmonella infections following first prescription for PPI– a cohort study using routine data.

OBJECTIVES:To examine the incidence of Campylobacter and Salmonella infection in patients prescribed proton pump inhibitors (PPIs) compared with controls.METHODS:Retrospective cohort study using anonymous general practitioner (GP) data. Anonymised individual-level records from the Secure Anonymised Information Linkage (SAIL) system between 1990 and 2010 in Wales were selected. Data were available from 1,913,925 individuals including 358,938 prescribed a PPI. The main outcome measures examined included incidence of Campylobacter or Salmonella infection following a prescription for PPI.RESULTS:The rate of Campylobacter and Salmonella infections was already at 3.1–6.9 times that of non-PPI patients even before PPI prescription. The PPI group had an increased hazard rate of infection (after prescription for PPI) of 1.46 for Campylobacter and 1.2 for Salmonella, compared with baseline. However, the non-PPI patients also had an increased hazard ratio with time. In fact, the ratio of events in the PPI group compared with the non-PPI group using the prior event rate ratio was 1.17 (95% CI 0.74–1.61) for Campylobacter and 1.00 (0.5–1.5) for Salmonella.CONCLUSIONS:People who go on to be prescribed PPIs have a greater underlying risk of gastrointestinal (GI) infection beforehand and they have a higher prevalence of risk factors before PPI prescription. The rate of diagnosis of infection is increasing with time regardless of PPI use, and there is no evidence that PPI is associated with an increase in diagnosed GI infection. It is likely that factors associated with the demographic profile of the patient are the main contributors to increased rate of GI infection for patients prescribed PPIs.

Type-Reduction of General Type-2 Fuzzy Sets: The Type-1 OWA Approach

For general type‐2 fuzzy sets, the defuzzification process is very complex and the exhaustive direct method of implementing type‐reduction is computationally expensive and turns out to be impractical. This has inevitably hindered the development of type‐2 fuzzy inferencing systems in real‐world applications. The present situation will not be expected to change, unless an efficient and fast method of deffuzzifying general type‐2 fuzzy sets emerges. Type‐1 ordered weighted averaging (OWA) operators have been proposed to aggregate expert uncertain knowledge expressed by type‐1 fuzzy sets in decision making. In particular, the recently developed alpha‐level approach to type‐1 OWA operations has proven to be an effective tool for aggregating uncertain information with uncertain weights in real‐time applications because its complexity is of linear order. In this paper, we prove that the mathematical representation of the type‐reduced set (TRS) of a general type‐2 fuzzy set is equivalent to that of a special case of type‐1 OWA operator. This relationship opens up a new way of performing type reduction of general type‐2 fuzzy sets, allowing the use of the alpha‐level approach to type‐1 OWA operations to compute the TRS of a general type‐2 fuzzy set. As a result, a fast and efficient method of computing the centroid of general type‐2 fuzzy sets is realized. The experimental results presented here illustrate the effectiveness of this method in conducting type reduction of different general type‐2 fuzzy sets.

Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases.

A novel Bayesian learning method for information aggregation in modular neural networks

Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling.

Guest Editorial Special Section on Enterprise Systems

The six papers in this special section are devoted to the topic of enterprise systems (ES) or enterprise information systems (EIS). ES has emerged as a promising tool used for integrating and extending business processes across the boundaries of business functions at both intra and interorganizational levels.