Elsevier

Applied Soft Computing

Volume 7, Issue 1, January 2007, Pages 343-352
Applied Soft Computing

Genetic programming for epileptic pattern recognition in electroencephalographic signals

https://doi.org/10.1016/j.asoc.2005.07.004Get rights and content

Abstract

This paper reports how the genetic programming paradigm, in conjunction with pattern recognition principles, can be used to evolve classifiers capable of recognizing epileptic patterns in human electroencephalographic signals. The procedure for feature extraction from the raw signal is detailed, as well as the genetic programming system that properly selects the features and evolves the classifiers. Based on the data sets used, two different epileptic patterns were detected: 3 Hz spike-and-slow-wave-complex (SASWC) and spike-or-sharp-wave (SOSW). After training, classifiers for both patterns were tested with unseen instances, and achieved sensibility = 1.00 and specificity = 0.93 for SASWC patterns, and sensibility = 0.94 and specificity = 0.89 for SOSW patterns. Results are very promising and suggest that the methodology presented can be applied to other pattern recognition tasks in complex signals.

Introduction

According to the World Health Organization (WHO) [1], up to 5% of the world population may have some kind of epileptic event during lifetime. Epilepsy is the commonest neurological disorder of the brain and its incidence is not limited to a specific age, race or geographical location. Epilepsy can have several physical, psychological and social consequences, including mood disorders, injuries and sudden death. When correctly diagnosed, up to 70% of epileptic patients can respond satisfactory to the clinical treatment. Thus, efforts toward its diagnosis and treatment are always of great importance.

This paper has a multidisciplinary nature, in the crossroads of evolutionary computation, pattern recognition and biomedical signal processing. It is reported the application of the genetic programming (GP) paradigm [2] to evolve a classifier capable of recognizing epileptic patterns in human electroencephalogram (EEG) signals recorded from the scalp of real patients. This work was also motivated by the difficulty in the interpretation of clinical EEG tests. Hopefully, this method can be useful in the automatic detection of epileptic events, not only in the clinical setting, but also in long-term electroencephalographic monitoring.

Since around the 1980s, many researchers have proposed methods for the quantitative analysis of the EEG. Many statistical, syntactic or artificial intelligence based techniques were used for pattern recognition, together or not with a variety of digital signal processing methods. Due to the complexity of the task, up to now, no method yet gave a widespread support to identify consistently every possible pattern of interest in the EEG. Therefore, proposed methods are usually applied to particular patterns or conditions. Some works related to the detection of epileptic events in the EEG are cited below. There is no intention to compare methods themselves or them with the current work, but it is interesting to illustrate the diversity of techniques previously used.

Many different approaches of neural networks to detect epileptic patterns in the EEG were reported, for instance, using simple multilayer perceptrons [3], simple Kohonen maps [4], or self-organizing maps with time–frequency analysis techniques [5], [6]. Adaptive filtering based on digital signal processing techniques were used by Stelle [7]. Wavelets were used by Khan and Gotman [8], and also by Geva and Keren [9], who hybridized wavelets with fuzzy techniques. Gotman and co-workers [10], [11] have proposed an heuristic mathematical method, based on the graphical similarity between EEG patterns that has been cited frequently in the literature.

The objective of this work is to present a methodology for pattern recognition, based on classifiers evolved by using genetic programming. The paper is organized as follows: first, an brief description of EEG and epileptic patterns is presented so as to allow readers to understand the nature of the application. Then, some basic aspects of the genetic programming are presented, since this is the technique used for problem solving. Next, it is described in detail the methodology for the application of GP to pattern recognition, including database handling, data reduction and supervised training. In the sequence, results of the application of the proposed methodology for two different epileptic patterns are shown. Finally, results are discussed and conclusions are drawn, pointing future directions of research and other possible applications.

Section snippets

Epilepsy and electroencephalography

The EEG signal is the recording of the electric activity of the brain at the scalp, usually taken concurrently with several electrodes. The EEG is a very complex and nonstationary signal and its characteristics are spatio-temporal dependent. The EEG is different at every point of the scalp where it is being recorded, and it is function of the underlying brain activity of the region. Although nonstationary, the EEG can be divided into epochs in which its statistical properties are reasonably

Genetic programming

Genetic programming (GP), first proposed by Koza [2], is a search and optimization technique belonging to a class of computational models collectively known as evolutionary algorithms, to which also belong genetic algorithms (GA). These techniques are roughly based on the evolution of living beings centered in the Darwinian principle of natural selection. Both GP and GA are based on the concept of evolution, throughout generations, of a population of candidate solutions. Individuals of this

Methodology

In this work, one is sought to categorize temporal input data into predefined classes. This is done by means of extracting relevant features (or attributes) from the data, out from the background of irrelevant information. Therefore, an usual methodology for pattern recognition was followed, comprising feature extraction, feature selection and classification [22].

The first step is feature extraction: the raw signal is converted into features that are supposed to be more condensed (in the sense

Supervised training

After running the GP algorithm, for both case studies, mathematical expressions (classifiers) were found. The best S-expressions found by GP are shown in Table 1. For the first case study, the corresponding expression has 36 nodes (19 operators, 13 terminals and 4 numerical constants) and only features WL, SSC and ZC were found to be relevant. The corresponding mathematical expression, after algebraic simplification, is shown in Eq. (11). For the second case study, the S-expression found has

Discussion and conclusions

The number of elements (functions and terminals) of the resulting classifiers for both case studies is an estimate of the hardness of the pattern recognition task. For the second case study (SOSW), the classifier found is much more complex than the first. This is a consequence of the underlying difficulty to identify unequivocally SOSW patterns (when compared with SASWC patterns). This is consistent with the fact that SOSW patterns are often misinterpreted by EEG experts as artifacts and vice

Acknowledgments

The author would like to thank all the people that anonymously contributed to the availability of the EEG databases used in this work This work was partially supported by a research grant from the Brazilian National Research Council—CNPQ (Process No. 305720/04-0).

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