Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier

Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

An improved EEG pattern classification system based on dimensionality reduction and classifier fusion

University of Technology, Sydney. Faculty of Engineering and Information Technology.Analysis of brain electrical activities (Electroencephalography, EEG) presents a rich source of information that helps in the advancement of affordable and effective biomedical applications such as psychotropic drug research, sleep studies, seizure detection and brain computer interface (BCI). Interpretation and understanding of EEG signal will provide clinicians and physicians with useful information for disease diagnosis and monitoring biological activities. It will also help in creating a new way of communication through brain waves. This thesis aims to investigate new algorithms for improving pattern recognition systems in two main EEG-based applications. The first application represents a simple Brain Computer Interface (BCI) based on imagined motor tasks, whilst the second one represents an automatic sleep scoring system in intensive care unit. BCI system in general aims to create a lion-muscular link between brain and external devices, thus providing a new control scheme that can most benefit the extremely immobilised persons. This link is created by utilizing pattern recognition approach to interpret EEG into device commands. The commands can then be used to control wheelchairs, computers or any other equipment. The second application relates to creating an automatic scoring system through interpreting certain properties of several biomedical signals. Traditionally, sleep specialists record and analyse brain signal using electroencephalogram (EEG), muscle tone (EMG), eye movement (EOG), and other biomedical signals to detect five sleep stages: Rapid Eye Movement (REM), stage 1,... to stage 4. Acquired signals are then scored based on 30 seconds intervals that require manually inspecting one segment at a time for certain properties to interpret sleep stages. The process is time consuming and demands competence. It is thought that an automatic scoring system mimicking sleep expert rules will speed up the process and reduce the cost. Practicality of any EEG-based system depends upon accuracy and speed. The more accurate and faster classification systems are, the better will be the chance to integrate them in wider range of applications. Thus, the performance of the previous systems is further enhanced using improved feature selection, projection and classification algorithms. As processing EEG signals requires dealing with multi-dimensional data, there is a need to minimize the dimensionality in order to achieve acceptable performance with less computational cost. The first possible candidate for dimensionality reduction is employed using channel feature selection approach. Four novel feature selection methods are developed utilizing genetic algorithms, ant colony, particle swarm and differential evolution optimization. The methods provide fast and accurate implementation in selecting the most informative features/channels that best represent mental tasks. Thus, computational burden of the classifier is kept as light as possible by removing irrelevant and highly redundant features. As an alternative to dimensionality reduction approach, a novel feature projection method is also introduced. The method maps the original feature set into a small informative subset of features that can best discriminate between the different class. Unlike most existing methods based on discriminant analysis, the proposed method considers fuzzy nature of input measurements in discovering the local manifold structure. It is able to find a projection that can maximize the margin between data points from different classes at each local area while considering the fuzzy nature. In classification phase, a number of improvements to traditional nearest neighbour classifier (kNN) are introduced. The improvements address kNN weighting scheme limitations. The traditional kNN does not take into account class distribution, importance of each feature, contribution of each neighbour, and the number of instances for each class. The proposed kNN variants are based on improved distance measure and weight optimization using differential evolution. Differential evolution optimizer is utilized to enhance kNN performance through optimizing the metric weights of features, neighbours and classes. Additionally, a Fuzzy kNN variant has also been developed to favour classification of certain classes. This variant may find use in medical examination. An alternative classifier fusion method is introduced that aims to create a set of diverse neural network ensemble. The diversity is enhanced by altering the target output of each network to create a certain amount of bias towards each class. This enables the construction of a set of neural network classifiers that complement each other