Juan R. Rabuñal

Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks

About 1% of the people in the world suffer from epilepsy. The main characteristic of epilepsy is the recurrent seizures. Careful analysis of the electroencephalogram (EEG) recordings can provide valuable information for understanding the mechanisms behind epileptic disorders. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. Wavelet transform (WT) is an effective analysis tool for non-stationary signals, such as EEGs. The line length feature reflects the waveform dimensionality changes and is a measure sensitive to variation of the signal amplitude and frequency. This paper presents a novel method for automatic epileptic seizure detection, which uses line length features based on wavelet transform multiresolution decomposition and combines with an artificial neural network (ANN) to classify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A famous public dataset was used to evaluate the proposed method. The high accuracy obtained for three different classification problems testified the great success of the method.

Artificial neural networks in real-life applications

Part I: Biological Modelization, Part II: Time Series Forecasting, Part III: Data mining, Part IV: Civil Engineering, Part V: Financial Analysis, Part VI: Other applications.

Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ann and gp

This paper proposes an application of Genetic Programming (GP) and Artificial Neural Networks (ANN) in hydrology, showing how these two techniques can work together to solve a problem, namely for modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal of this research is to design a real-time alarm system to warn of floods or subsidence in various types of urban basins. Results look promising and appear to offer some improvement for analyzing river basin systems over stochastic methods such as unitary hydrographs.

A new approach to the extraction of ANN rules and to their generalization capacity through GP

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.

Performance of artificial neural networks in nearshore wave power prediction

In this paper the assessment of the wave energy potential in nearshore coastal areas is investigated by means of artificial neural networks (ANNs). The performance of the ANNs is compared with in situ measurements and spectral numerical modelling (the conventional tool for wave energy assessment). For this purpose, 13 years of records of two buoys, one offshore and one inshore, with an hourly frequency are used to develop an ANN model for predicting the nearshore wave power. The best suited architecture was selected after assessing the performance of 480 ANN models involving twelve different architectures. The results predicted by the ANN model were compared with the measured data and those obtained by means of the SWAN (Simulating Waves Nearshore) spectral model. The quality in the predictions of the ANN model shows that this type of artificial intelligence models constitutes a powerful tool to forecast the wave energy potential at particular coastal site with great accuracy, and one that overcomes some of the disadvantages of the conventional tools for nearshore wave power prediction.

Optical Fish Trajectory Measurement in Fishways through Computer Vision and Artificial Neural Networks

Vertical slot fishways are hydraulic structures that allow the upstream migration of fish through obstructions in rivers. The appropriate design of a vertical slot fishway depends on the interplay between hydraulic and biological variables because the hydrodynamic properties of the fishway must match the requirements of the fish species for which it is intended. One of the primary difficulties associated with studies of real fish behavior in fishway models is that the existing mechanisms to measure the behavior of the fish in these assays, such as direct observation or placement of sensors on the specimens, are impractical or unduly affect the animal behavior. This paper proposes a new procedure for measuring the behavior of the fish. The proposed technique uses artificial neural networks and computer vision techniques to analyze images obtained from the assays by means of a camera system designed for fishway integration. It is expected that this technique will provide detailed information about the fish behavior, and it will help to improve fish passage devices, which is currently a subject of interest in the area of civil engineering. A series of assays has been performed to validate this new approach in a full-scale fishway model with living fish. We have obtained very promising results that allow accurate reconstruction of the movements of the fish within the fishway.

Generation and simplification of Artificial Neural Networks by means of Genetic Programming

The development of Artificial Neural Networks (ANNs) is traditionally a slow process in which human experts are needed to experiment on different architectural procedures until they find the one that presents the correct results that solve a specific problem. This work describes a new technique that uses Genetic Programming (GP) in order to automatically develop simple ANNs, with a low number of neurons and connections. Experiments have been carried out in order to measure the behavior of the system and also to compare the results obtained using other ANN generation and training methods with evolutionary computation (EC) tools. The obtained results are, in the worst case, at least comparable to existing techniques and, in many cases, substantially better. As explained herein, the system has other important features such as variable discrimination, which provides new information on the problems to be solved.

Drug Discovery and Design for Complex Diseases through QSAR Computational Methods

There is a need for a study of the complex diseases due to their important impact on our society. One of the solutions involves the theoretical methods which are fast and efficient tools that can lead to the discovery of new active drugs specially designed for these diseases. The Quantitative Structure - Activity Relationship models (QSAR) and the complex network theory become important solutions for screening and designing efficient pharmaceuticals by coding the chemical information of the molecules into molecular descriptors. This review presents the most recent studies on drug discovery and design using QSAR of several complex diseases in the fields of Neurology, Cardiology and Oncology.

Optimization of existing equations using a new Genetic Programming algorithm

A method based on Genetic Programming (GP) to improve previously known empirical equations is presented. From a set of experimental data, the GP may improve the adjustment of such formulas through the symbolic regression technique. Through a set of restrictions, and the indication of the terms of the expression to be improved, GP creates new individuals. The methodology allows us to study the need of including new variables in the expression. The proposed method is applied to the shear strength of concrete beams. The results show a marked improvement using this methodology in relation to the classic GP and international code procedures.

Artificial Intelligence Techniques for Colorectal Cancer Drug Metabolism: Ontologies and Complex Networks

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.