Chun-An Chou

Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood

We investigated the development of spontaneous (resting state) cerebral electric fields and their network organization from early to late childhood in a large community sample of children. Critically, we examined electrocortical maturation across one-year windows rather than creating aggregate averages that can miss subtle maturational trends. We implemented several novel methodological approaches including a more fine grained examination of spectral features across multiple electrodes, the use of phase-lagged functional connectivity to control for the confounding effects of volume conduction and applying topological network analyses to weighted cortical adjacency matrices. Overall, there were major decreases in absolute EEG spectral density (particularly in the slow wave range) across cortical lobes as a function of age. Moreover, the peak of the alpha frequency increased with chronological age and there was a redistribution of relative spectral density toward the higher frequency ranges, consistent with much of the previous literature. There were age differences in long range functional brain connectivity, particularly in the alpha frequency band, culminating in the most dense and spatially variable networks in the oldest children. We discovered age-related reductions in characteristic path lengths, modularity and homogeneity of alpha-band cortical networks from early to late childhood. In summary, there is evidence of large scale reorganization in endogenous brain electric fields from early to late childhood, suggesting reduced signal amplitudes in the presence of more functionally integrated and band limited coordination of neuronal activity across the cerebral cortex.

Voxel Selection Framework in Multi-Voxel Pattern Analysis of fMRI Data for Prediction of Neural Response to Visual Stimuli

Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging approach for probing the neural correlates of cognition. MVPA allows cognitive states to be modeled as distributed patterns of neural activity and classified according to stimulus conditions. In practice, building a robust, generalizable classification model can be challenging because the number of voxels (features) far exceeds the number of stimulus instances/data observations. To avoid model overfitting, there is a need to select informative voxels before building a classification model. In this paper, we propose a robust feature (voxel) selection framework using mutual information (MI) and partial least square regression (PLS) to establish an informativeness index for prioritizing selection of voxels based on the degree of their association to the experimental conditions. We evaluated the robustness of our proposed framework by assessing performance of standard classification algorithms, when combined with our feature selection approach, in a publicly-available fMRI dataset of object-level representation widely used to benchmark MVPA performance (Haxby, 2001). The computational results suggest that our feature selection framework based on MI and PLS drastically improves the classification accuracy relative to those previously reported in the literature. Our results also suggest that highly informative voxels may provide meaningful insight into the functional-anatomic relationship of brain activity and stimulus conditions.

A Gaussian mixture model based discretization algorithm for associative classification of medical data

A new supervised discretization algorithm is proposed.Multi-modal distributed numerical variables/features are particularly handled.The proposed approach outperforms existing algorithms in rule-based classification. Knowledge-based systems such as expert systems are of particular interest in medical applications as extracted if-then rules can provide interpretable results. Various rule induction algorithms have been proposed to effectively extract knowledge from data, and they can be combined with classification methods to form rule-based classifiers. However, most of the rule-based classifiers can not directly handle numerical data such as blood pressure. A data preprocessing step called discretization is required to convert such numerical data into a categorical format. Existing discretization algorithms do not take into account the multimodal class densities of numerical variables in datasets, which may degrade the performance of rule-based classifiers. In this paper, a new Gaussian Mixture Model based Discretization Algorithm (GMBD) is proposed that preserve the most frequent patterns of the original dataset by taking into account the multimodal distribution of the numerical variables. The effectiveness of GMBD algorithm was verified using six publicly available medical datasets. According to the experimental results, the GMBD algorithm outperformed five other static discretization methods in terms of the number of generated rules and classification accuracy in the associative classification algorithm. Consequently, our proposed approach has a potential to enhance the performance of rule-based classifiers used in clinical expert systems.

A systems approach for scheduling aircraft landings in JFK airport

The aircraft landings scheduling problem at an airport has become very challenging due to the increase of air traffic. Traditionally, this problem has been widely studied by formulating it as an optimization model solved by various operation research approaches. However, these approaches are not able to capture the dynamic nature of the aircraft landing scheduling problem appropriately and handle uncertainty easily. A systems approach provides an alternative to solve such a problem from a systematic perspective. In this regard, the concept of general systems problem solving (GSPS) was first introduced in 1970s, and yet the power of the GSPS methodology is not fully discovered as it had only been applied to few domains. In this paper, a new general systems problem solving framework integrating computational intelligence techniques (GSPS-CI) is introduced. The two main functions of the framework are: (1) adaptive network based fuzzy inference system (ANFIS) to predict flight delays, and (2) fuzzy decision making procedure to schedule aircraft landings. The effectiveness of the GSPS-CI framework is tested on the JFK airport in USA, one of the most complex real-life systems.

Applied optimization and data mining

This special volume is dedicated to Professor Panos Pardalos in honor of his 60th birthday (in 2014) and of his fundamental contributions to the research areas of applied optimization and data mining. Pardalos’s distinguished pioneer work also established an important interdisciplinary field of applied optimization and data mining, which has been attracting broad attention from both academia and industry. In this era of big data, it has become increasingly important to develop large-scale optimization and data mining models to solve challenging data-driven problems in engineering and sciences. Successful, practical applications have been increasingly visible in various domains such as transportation, logistics, healthcare, energy, etc. To honor Professor Pardalos, we invited a selected number of his distinguished colleagues as well as leading researchers in applied optimization and data mining for their contributions. The twenty papers in this special volume can be categorized into several major areas including applied mathematical optimization in data mining, transportation, scheduling, energy, healthcare, and finance. There are five papers mainly focusing on modeling and algorithms of different optimization problems, such as multi-objective optimization, network optimization, and risk averse optimization; four papers are linked to transportation problems; three papers are devoted to scheduling problems; four papers investigate supervising learning (or classification) problems particularly; three papers are related to energy; one paper is moti-

Prediction of Mortality and Survival of Patients After Cardiac Surgery Using Fuzzy EuroSCORE System and Reliability Analysis

Abstract Cardiac surgery is an important medical treatment for coronary vessel patients. Different models have been introduced to determine the risk factors related to side effects of this operation. The goal of this research is to study EuroSCORE (European System for Cardiac Operative Risk Evaluation) as a useful method for predicting the risk of mortality after cardiac surgery, and to introduce a new way of inference, called Fuzzy EuroSCORE. In addition, a systems reliability analysis will be used to calculate the survival possibility of patients after a certain time period after cardiac surgery. To model and simulate the suggested system, eight important parameters of EuroSCORE table are chosen using experts knowledge and a new method is applied based on a fuzzy inference system. To calculate the risk of mortality after cardiac surgery, the patients are categorized into 3 different groups of low risk, medium risk, and high risk. The range of the mortality risk is determined by appropriate medical data in the fuzzy EuroSCORE system. Additionally, a defect density function for the cardiovascular problem is suggested using the systems reliability analysis. Finally, the prospect of patients survival after a certain time period after cardiac surgery is predicted.

Multi-pattern generation framework for logical analysis of data

Logical analysis of data (LAD) is a rule-based data mining algorithm using combinatorial optimization and boolean logic for binary classification. The goal is to construct a classification model consisting of logical patterns (rules) that capture structured information from observations. Among the four steps of LAD framework (binarization, feature selection, pattern generation, and model construction), pattern generation has been considered the most important step. Combinatorial enumeration approaches to generate all possible patterns were mostly studied in the literature; however, those approaches suffered from the computational complexity of pattern generation that grows exponentially with data (feature) size. To overcome the problem, recent studies proposed column generation-based approaches to improve the efficacy of building a LAD model with a maximum-margin objective. There was still a difficulty in solving subproblems efficiently to generate patterns. In this study, a new column generation framework is proposed, in which a new mixed-integer linear programming approach is developed to generate multiple patterns having maximum coverage in subproblems at each iteration. In addition to the maximum-margin objective, we propose an alternative objective (minimum-pattern) to solve the LAD problem as a minimum set covering problem. The proposed approaches are evaluated on the datasets from the University of California Irvine Machine Learning Repository. The computational experiments provide comparable performances compared with previous LAD and other well-known classification algorithms.

Column-generation framework of nonlinear similarity model for reconstructing sibling groups

Establishing family relationships, such as parentage and sibling relationships, is fundamental in biological research, especially in wild species, as they are often important to understanding evolutionary, ecological, and behavioral processes. Because it is commonly impossible to determine familial relationships from field observations alone, the reconstruction of sibling relationships often depends on informative genetic markers coupled with accurate sibling reconstruction algorithms. Most studies in the literature reconstruct sibling relationships using methods that are based on either statistical analyses (i.e., likelihood estimation) or combinatorial concepts (i.e., Mendelian inheritance laws) of genetic data. We present a novel computational framework that integrates both combinatorial concepts and statistical analyses into one sibling reconstruction optimization model. To solve this integrated model, we propose a column-generation approach with a branch-and-price method. Under the assumption of pars...

A Simple Distance Based Seizure Onset Detection Algorithm Using Common Spatial Patterns

Existing seizure onset detection methods usually rely on a large number of extracted features regardless of computational efficiency, which reduces their applicability for real-time seizure detection. In this study, a simple distance based seizure onset detection algorithm is proposed to distinguish seizure and non-seizure EEG signals. The proposed framework first applies the common spatial patterns (CSP) method to enhance the signal-to-noise ratio and reduce the dimensionality of EEG signals, and then uses the autocorrelation of the averaged spatially filtered signal to classify incoming signals into a seizure or non-seizure state. The proposed approach was tested using CHB-MIT dataset that contains continuous scalp EEG recordings from 23 patients. The results showed \(\sim \)95.87 % sensitivity with an average latency of 2.98 s and 2.89 % false detection rate. More interestingly, the average process time required to classify each window (1–5 s of EEG signals) was 0.09 s. The outcome of this study has a high potential to improve the automatic seizure onset detection from EEG recordings and could be used as a basis for developing real-time monitoring systems for epileptic patients.

Graph Theoretic Compressive Sensing Approach for Classification of Global Neurophysiological States from Electroencephalography (EEG) Signals

We present a data fusion framework integrating graph theoretic and compressive sensing (CS) techniques to detect global neurophysiological states using high-resolution electroencephalography (EEG) recordings. Acute stress induction (and control procedures) were used to elicit distinct states of neurophysiological arousal. We recorded EEG signals (128 channels) from 50 participants under two different states: hand immersion in room temperature water (control condition) or in chilled (~3 °C) water (stress condition). Thereafter, spectral graph theoretic Laplacian eigenvalues were extracted from these high-resolution EEG signals. Subsequently, the CS technique was applied for the classification of acute stress using the Laplacian eigenvalues as features. The proposed method was compared to a support vector machine (SVM) approach using conventional statistical features as inputs. Our results revealed that the proposed graph theoretic compressive sensing approach yielded better classification performance (~90 % F-score) compared to SVM with statistical features (~50 % F-Score). This finding indicates that the spectral graph theoretic compressive sensing approach presented in this work is capable of classifying global neurophysiological arousal with higher fidelity than conventional signal processing techniques.