Grammatical evolution for features of epileptic oscillations in clinical intracranial electroencephalograms

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Abstract

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indicate the region(s) of brain that cause epileptic seizures, which could be surgically removed for therapy. If this presumption is true, then the effectiveness of surgical treatment could depend on the effectiveness in pinpointing critically diseased brain, which in turn depends on the most accurate detection of pathological oscillations. Moreover, the accuracy of detecting pathological oscillations depends greatly on the selected feature(s) that must objectively distinguish epileptic events from average activity, a task that visual review is inevitably too subjective and insufficient to resolve. Consequently, this work suggests an automated algorithm that incorporates grammatical evolution (GE) to construct the most sufficient feature(s) to detect epileptic oscillations within the iEEG of a patient. We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (∼65–95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming. We demonstrate that a detector with a grammatically evolved feature exhibits a sensitivity and selectivity that is comparable to a previous detector with a genetically programmed feature, making GE a useful alternative to designing detectors.

Highlights

► Grammatical Evolution selects and combines features for accurate pattern-detection. ► Evolutionary computation improves classification over conventional feature-selection. ► Grammatical Evolution produces simpler features than Genetic Programming. ► Grammatical Evolution allows a more reliable approach to detect epileptic discharges.

Introduction

The intracranial electroencephalogram (iEEG) is a very valuable diagnostic tool for surgical treatment of epilepsy. An electroencephalogram (EEG) measures the electrical activity of neuronal populations in the brain using a metallic electrode, and the iEEG is an invasive application of an EEG in which EEG electrodes are placed on top of or deep within the surface of the brain. For patients with epilepsy, the iEEG is reserved for preoperative evaluation of epileptic seizures prior to invasive treatment (e.g., surgery) and purposed to locate the area(s) of the brain from which epileptic seizures are generated and actually arise (Diehl and Lüders, 2000, Engel, 1996). Ultimately, with information about an estimated epileptic focus (i.e. the nucleus or nuclei for epileptic seizures), a neurosurgeon can excise the portion of brain that is putatively responsible for dysfunction without damaging important functional parts of brain (Luders & Comair, 2000). However, a means to reliably estimate the epileptic focus is obviously necessary for accurate and precise surgical treatment.

Recent studies suggest that an epileptic focus is possibly reliably identifiable according to particular pathological patterns in the epileptic brain, finding that certain electrographic signatures seemingly distinguish areas in which seizures do and do not occur in human epilepsy (Jacobs et al., 2007, Jacobs et al., 2008, Jacobs et al., 2009, Staba et al., 2002, Worrell et al., 2004, Worrell et al., 2008). For instance, oscillatory epileptic activity between 60 and 100 Hz, previously called high-frequency epileptiform oscillations (Worrell et al., 2004) but we here call slow ripples (Firpi et al., 2007), 100–200 Hz, called ripples (Bragin, Engel, Wilson, Fried, & Buzsaki, 1999), or 200–500 Hz, called fast ripples (Bragin et al., 1999), in human iEEG are reported to spatially coincide with the location of epileptic seizures. Although, this information requires further study, especially a correlative analysis that contrasts the location of pathological biomarkers and surgically removed brain against the results of surgery, to determine any true usefulness, optimistically these findings make evident a potential biomarker that either alone or in combination with other relevant biomarkers may reveal the cause of epileptic seizures (Engel, Bragin, Staba, & Mody, 2009) or improve the effectiveness of surgical treatment for patients with epilepsy. Consequently, there exists considerable value in designing an appropriate algorithm to automatically detect pathological oscillations within iEEG, which would provide an objective means to precisely pinpoint epileptic brain.

The detection of pathological oscillations is simply an application of classical binary classification. That is, a pathological oscillation must be quantitatively distinguished from noise (or normal background) with at least one feature in three basic stages: some improvement of the signal-to-noise ratio (e.g., band-pass filtering) for the iEEG, extraction of feature(s); and binary classification, which permits detection of the beginning and ending of an oscillation. Furthermore, we take the position that the success in classification highly depends upon the success in finding the best feature(s) that probabilistically separate(s) the two classes, which would greatly simplify the task of a classifier. Previous approaches to automatically discriminate pathological oscillations and background from iEEG include techniques that rely on either an arbitrary manual selection of features (Smart et al., 2005, Staba et al., 2002), an automated selection of features using genetic programming (Firpi et al., 2007, Smart et al., 2007), or automatic creation of a feature directly from iEEG signals using particle swarm optimization (Firpi et al., 2007). While manually selecting features – usually from intuition or some understanding of the problem – provides a means to detect pathological oscillations, it is usually not the best approach as demonstrated recently when compared against evolutionary algorithms (Firpi et al., 2007, Smart et al., 2007). On the other hand, evolutionary algorithms such as genetic programming (GP) or particle swarm optimization (PSO) may provide a better means to detect pathological oscillations, but there is still room to improve their usage. Therefore, we introduce an alternate evolutionary algorithm (EA), called grammatical evolution (GE), for selecting and combining features to distinguish pathological oscillations and normal activity within recorded iEEG signals. We propose GE rather than modifying the previously published applications of evolutionary algorithms (EAs) because GE can circumvent the technical limitations of the earlier EAs while constructing features that possibly improve detection over the state of the art.

Section snippets

Grammatical evolution

Grammatical evolution (O’Neill and Ryan, 2001) parallels the creation of a protein from deoxyribonucleic acid (DNA) while simulating Darwinian processes of natural evolution to stochastically produce a solution for a given problem. Before we describe the operation of GE, we describe the process of creating a protein to help understand the inspiration for GE. A protein begins as DNA, which is a sequence of biological blocks called nucleotides. The DNA is transcribed to ribonucleic acid (RNA) by

Data

We selected a sample of data to analyze (see ‘Methods’) from an existing large collection of clinical iEEG recordings from six patients with epilepsy. The patients underwent long-term continuous clinical video-iEEG monitoring and recording so that a neurosurgeon was able to preoperatively estimate an epileptic focus for surgical treatment. The preoperative video-iEEG recordings were used in an earlier medical study that involved the collection of the above data at Emory University (EUH) and the

Manual markings

For each patient, we prepared two sets of raw data across multiple iEEG electrodes using a MATLAB (Mathworks, Natick, MA) graphical user interface (GUI) (Gardner, Worrell, Marsh, Dlugos, & Litt, 2007) that automatically stored the time-stamp (i.e., the temporal beginning and ending) of a manually marked event: training data, with which to select and combine the best feature(s) using GE and cross-validate a chosen classifier, and testing data to verify the performance of a detector with a

Experiment 1

We determined that some methods performed differently from others, that the number of features did not change the performance of each method, that the number of features did not affect the differences in performance between methods, and that each method demonstrated some unbalance in performance but the imbalanced performance of GE differed from the other methods. Fig. 1, Fig. 2, Fig. 3 illustrated these findings.

For a single feature, we found that none of the methods differed (p = 0.073) in mean

Discussion

The findings in the presented experiments were important in several key aspects. First, we determined that forward and backward sequential selection and genetic programming returned features for detecting slow ripples with balanced sensitivity and selectivity whereas grammatical evolution returned features for detecting slow ripples with more selectivity than sensitivity. This finding was interesting since all four automatic approaches objectively maximized accuracy with no statistically

Conclusions

Overall, GE facilitated a sound approach to detect interictal epileptic oscillations in clinical iEEG. Because the GE required only a small sample of manually marked events and the general framework of the detector did limit detection to a certain event or electrographic recording, we presented a versatile approach that permitted the detection of interictal activity in different recordable bandwidths (e.g., fast ripples, ripples, spikes).

Acknowledgment

Dr. Smart is supported by the National Institute of General Medical Sciences (IRACDA Grant: 5K12 GM000680-07).

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