Identification of epilepsy stages from ECoG using genetic programming classifiers

doi:10.1016/j.compbiomed.2013.08.016

Computers in Biology and Medicine

Volume 43, Issue 11, November 2013, Pages 1713-1723

https://doi.org/10.1016/j.compbiomed.2013.08.016 Get rights and content

Abstract

Objective: Epilepsy is a common neurological disorder, for which a great deal of research has been devoted to analyze and characterize brain activity during seizures. While this can be done by a human expert, automatic methods still lag behind. This paper analyzes neural activity captured with Electrocorticogram (ECoG), recorded through intracranial implants from Kindling model test subjects. The goal is to automatically identify the main seizure stages: Pre-Ictal, Ictal and Post-Ictal. While visually differentiating each stage can be done by an expert if the complete time-series is available, the goal here is to automatically identify the corresponding stage of short signal segments.

Methods and materials: The proposal is to pose the above task as a supervised classification problem and derive a mapping function that classifies each signal segment. Given the complexity of the signal patterns, it is difficult to a priori choose any particular classifier. Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. Two GP-based classifiers are used and extensively evaluated. The signals from epileptic seizures are obtained using the Kindling model of elicited epilepsy in rodent test subjects, for which a seizure was elicited and recorded on four separate days.

Results: Results show that signal segments from a single seizure can be used to derive accurate classifiers that generalize when tested on different signals from the same subject; i.e., GP can automatically produce accurate mapping functions for intra-subject classification. A large number of experiments are performed with the GP classifiers achieving good performance based on standard performance metrics. Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable gate array, achieving a low average classification error of 14.55%, sensitivity values between 0.65 and 0.97, and specificity values between 0.86 and 0.94.

Conclusions: The proposed approach achieves good results for stage identification, particularly when compared with previous works that focus on this task. The results show that the problem of intra-class classification can be solved with a low error, and high sensitivity and specificity. Moreover, the limitations of the approach are identified and good operating configurations can be proposed based on the results.

Introduction

Epilepsy is a common neurological ailment, characterized by the chronic seizures it causes as part of its symptomatology. According to different studies, epilepsy incidence varies depending on the region and the considered population. For instance, [1] estimates that the number of people with epilepsy is between 11/100,000 and 134/100,000. Other studies concluded that 3% to 5% of the general population experiences one or more seizures during their life-time [2]. A more conservative estimate is that epilepsy affects 1% of the world population [3], [4]. According to [5], two thirds of affected individuals develop seizures that can be controlled by anti-epileptic medication, while another 7% or 8% can be cured by surgery. However, the symptomatology of the remaining 25% cannot be controlled by current therapies.

Epileptic seizures are sudden disruptive episode of mental functions, that develop over four principal stages [6]: (1) The Basal stage, (2) Pre-Ictal Stage, (3) The Ictal Stage; and (4) The Post-Ictal Stage. At each stage we can appreciate different frequencies and waveforms; i.e., each stage is characterized by a different signal morphology. However, while a human expert has no problem in identifying each stage, an automatic method for stage identification has not been developed.

Concretely, the proposed system is designed to automatically determine the stage to which a small signal segment belongs. The task is posed as a supervised learning problem, where the system input is a short signal segment and the output is the corresponding stage of the seizure. However, deriving automatic processing methods for these signals is definitely not a straightforward endeavor, given the complexities of the signals generated during a seizure, which often contain several frequency components [7], [8]. Therefore, in this paper the problem is solved using a Genetic Programming (GP) classifier, that analyzes statistical features of each signal segment and derives a non-linear mapping following a symbolic regression strategy [9], [10]. GP is a stochastic population based meta-heuristic which is well suited to automatically derive mapping functions. In particular, GP is used when the overall structure of a solution cannot be defined a priori and only a high-level description of the desired functionality is available.

This paper presents a continuation of preliminary research carried out by the authors addressing stage identification [11]. However, the present work presents vastly more experimental tests and validation, two orders of magnitude more in total experimental runs to obtain statistically sound results while considering many different parametrizations of the system. Moreover, experimental tests include more test subjects, two variants of GP classifiers and a prototype for an embedded system that could perform stage identification in a real-world scenario.

The remainder of this paper proceeds as follows. First, Section 2 presents a brief introduction to epilepsy models and brain signals recorded through ECoG. Then, Section 3 reviews related work. Afterwards, Section 5 presents the supervised learning problem and the proposed GP-based solution. Section 6 presents the experimental setup and provides a detailed discussion of the results. Finally, concluding comments are given in Section 7.

Section snippets

Epilepsy model, seizure stages and signal recording

Epileptic seizures are mostly spontaneous, a characteristic that makes them particularly difficult to study. Therefore, for research purposes seizures are often elicited in a controlled manner using animal subjects, commonly rodents. Indeed, one of the most used models is the amygdala Kindling model for temporal lobe epilepsy [12], [13], since signal morphology is very similar to those produced by a human brain during a seizure. Using animal models, it is possible to reproduce a chronic brain

Automatic analysis of epilepsy signals

Hitherto, most efforts towards the automatic analysis of epileptic brain signals have centered on the problem of predicting the onset of a seizure or identifying if a signal exhibits traits of an epileptic episode. For instance, [28] analyzes the non-linear dynamics of the signal in an attempt to anticipate a seizure. Similarly, [29], [30] use hybrid features and apply computational techniques to recognize the signal dynamics exhibited during the Pre-Ictal stage.

Moreover, given the difficulty

Genetic programming

Evolutionary computation (EC) as a field, deals with the development and analysis of meta-heuristics (and hyper-heuristics) that are based on a simplified model of Neo-Darwinian evolution. These are population-based search methods, where candidate solutions are stochastically selected and varied to produce new solutions for a specified problem. This process is carried out iteratively until a predefined termination criterion is met. In general, to implement and execute an evolutionary algorithm

Problem statement and proposed solution

In this paper, the goal is to detect the three main stages of an epileptic seizure (Pre-Ictal, Ictal and Post-Ictal) given a short segment of an ECoG signal. This goal is posed as a classification problem, where the signal segment represents a pattern $x \in R^{p}$ , with p the total number of sample points which is dependent on the sampling rate and the signal duration. For instance, since the sampling rate during recording is 256 Hz, if we take a 2 s signal then p=512. Then, this can be defined as a

Experiments and results

The goal of the experimental work is to evaluate the accuracy of intra-subject classification of epilepsy signals. In other words, to test the performance of classifiers that are trained and tested with signal samples from a single test subject that are recorded in different days.

Conclusions

This paper presents an automatic method for identifying the three main stages of an epileptic seizure from ECoG signals. The proposal is based on posing the problem as a supervised learning problem and solving it with GP. The results exhibit strong statistical tendencies of the GP classifiers that suggest that the approach is able to solve the intra-patient classification problem. These results are unique and show substantial improvement when compared with previous methods [36], [37], [38].

Conflict of interest statement

None declared.

Acknowledgments

Funding for this work provided by CONACYT (Mexico) Basic Science Research Project no. 178323 and DGEST (Mexico) Research Project no. TIJ-ING-2012-110. Fifth author is supported by a CONACYT (Mexico) doctoral scholarship no. 226981. Thanks are extended to Francisco Sancho from Hospital Universitario de Valencia, for his collaboration and support during the signal recording process. Finally, thanks are given to Moises Zonta, Ivan Garcia and Enrique Naredo from Instituto Tecnológico de Tijuana for

Arturo Sotelo received a degree in Electronic and Communications Engineering from the Escuela Superior de Ingeniería Mecánica y Eléctrica del Instituto Politécnico Nacional in 1990, and a Master's degree in Science in the field of Digital Systems in 1997 from the Centro de Investigación en Desarrollo de Tecnología Digital del Instituto Politécnico Nacional, México. He is currently a Ph.D. candidate from the Universidad Politécnica de Valencia, Spain, and a research professor at ITT. He has

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All of these feature sets are summarized in Table 2 and described in the following sections. Additionally, five additional features which were successfully used in (Sotelo et al., 2013) for epilepsy state identification are considered. The proposed features are basic statistical measures calculated explicitly in the time domain over the raw epochs E, with E being the epoch vector in the time domain.
The neurological disorder known as epilepsy is characterized by involuntary recurrent seizures that diminish a patient's quality of life. Automatic seizure detection can help improve a patient's interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved.
In this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings.
Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. Comparison with existing methods: A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance.
The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.
Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data
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Pseudocode for GP Algorithm. For pattern classification problems to quantitatively discriminate non-biomarker (e.g., basal or baseline activity) and biomarker (e.g., spikes, seizures, PGOs, abnormal CBF activations) brain signals from epilepsy patients, GP has been used for both noninvasive and invasive electroencephalography (Fernández-Blanco et al., 2013; Firpi et al., 2005b, 2005c, 2006; Guo et al., 2011; Lopes, 2007; Marchesi et al., 1997b; Smart et al., 2007; Sotelo et al., 2013a, 2013b), MEG (Georgopoulos et al., 2009; Theofilatos et al., 2009), and fMRI (Burrell et al., 2007b). However, GP is not a feature selection algorithm.
Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient.
Seizure states identification in experimental epilepsy using gabor atom analysis
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The study of the seizure states is motivated by the interest in evaluating experimental epilepsy treatments. However, as a first step, this work focuses on identifying three of the main seizure states: Pre-Ictal, Ictal, and Post-Ictal as it is done in (Sotelo et al., 2007, 2012, 2013). For instance, this makes it possible to estimate when the Ictal state develops.
Epileptic seizures evolve through several states, and in the process the brain signals may change dramatically. Signals from different states share similar features, making it difficult to distinguish them from a time series; the goal of this work is to build a classifier capable of identifying seizure states based on time–frequency features taken from short signal segments.
There are different amounts of frequency components within each Time–Frequency window for each seizure state, referred to as the Gabor atom density. Taking short signal segments from the different states and decomposing them into their atoms, the present paper suggests that is possible to identify each seizure state based on the Gabor atom density. The brain signals used in this work were taken form a database of intracranial recorded seizures from the Kindling model.
The findings suggest that short signal segments have enough information to be used to derive a classifier able to identify the seizure states with reasonable confidence, particularly when used with seizures from the same subject. Achieving average sensitivity values between 0.82 and 0.97, and area under the curve values between 0.5 and 0.9.
The experimental results suggest that seizure states can be revealed by the Gabor atom density; and combining this feature with the epoch's energy produces an improved classifier. These results are comparable with the recently published on state identification. In addition, considering that the order of seizure states is unlikely to change, these results are promising for automatic seizure state classification.
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Enrique Guijarro has a doctorate in Industrial Engineering from Universidad Politécnica de Valencia, Spain. Currently, he is a titular professor at the Electronica Engineering Department in UPV, and a researcher at the Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada en el SerHumano (i3BH).

Leonardo Trujillo received an Electronic Engineering (2002) and a Masters in Computer Science (2004) from the Technical Institute of Tijuana in México. He then received a doctorate in Computer Science from CICESE research center, Mexico (2008), developing Genetic Programming (GP) applications for Computer Vision problems; focusing on feature extraction and image description. He is currently professor at the Technical Institute of Tijuana in México (ITT), where he is President of the Doctorate Program and head of the Cybernetics research group. He actively collaborates with other institutions, particularly the University of Bordeaux and INRIA in France where he has received the distinction of invited professor and researcher in 2010 and 2011. Moreover, he is actively involved with research work with the University of Extremadura in Spain and Trinity College Dublin in Ireland. Currently, he is involved in interdisciplinary research in the fields of Evolutionary Computation, Computer Vision, Image Analysis, Pattern Recognition and Autonomous Robotics. He is a level 1 member of the National Research System of México and head of a basic science project from CONACYT México, studying problem difficulty and prediction of expected performance for GP systems, developing new theory and applications. The work of him has led to the publishing of over 40 works in international journals and conferences, receiving several awards and distinctions.

Luis N. Coria is a researcher of the Cibernetics group at Instituto Tecnologico de Tijuana (ITT) and invited professor at Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN), both in Tijuana, Mexico. He participates in the Robotics and Mechatronics network at Instituto Politecnico Nacional (IPN). He is Electronics Engineer (1999) and M.Sc. in Digital Systems (2005). His Ph.D. is in Electronics and Communications (2010) and now his interest is focused in Chaotic Systems, Nonlinear Systems Analysis and Signal Analysis with nonconventional methods. His recent contributions are on the analysis of Nonlinear Cancer and Biological models. Furthermore, currently he is focused on the analysis and characterization of brain signals from epileptic models.

Yuliana Martínez is first year student in the Ph.D. program in Engineering Sciences from the Instituto Tecnologico de Tijuana. She received a Masters degree in Computer Science from the Instituto Tecnologico de Tijuana (2009–2011), and an Engineering Degree in Computing from instituto Tecnologico de Los Mochis (2002–2007). Her main area of research is genetic programming, in the study of one of the most important problems within the community, prediction of performance and difficulty of problems in GP.

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Identification of epilepsy stages from ECoG using genetic programming classifiers

Abstract

Introduction

Section snippets

Epilepsy model, seizure stages and signal recording

Automatic analysis of epilepsy signals

Genetic programming

Problem statement and proposed solution

Experiments and results

Conclusions

Conflict of interest statement

Acknowledgments

Lancet Neurol.

Electroencephalogr. Clin. Neurophysiol.

Electroencephalogr. Clin. Neurophysiol.

Prog. Neurobiol.

Clin. Neurophysiol.

Epilepsy Res.

Clin. Neurophysiol.

Epilepsy Res.

Neuropharmacology

Appl. Soft Comput.

Eng. Appl. Artif. Intell.

Expert Syst. Appl.

Pattern Recognition Lett.

Anticonvulsant and neurotoxic effects of intracerebroventricular injection of phenytoin, phenobarbital and carbamazepine in an amygdala-Kindling model of epilepsy in the rat

Epilepsy Res.

Special issue on epileptic seizure prediction

IEEE Trans. Biomed. Eng.

Time–frequency analysis of short segments of biomedical data

IEE Proc. Sci. Meas. Technol.

Genetic Programming IIAutomatic Discovery of Reusable Programs

Analysis and classification of epilepsy stages with genetic programming