Computational Intelligence and Neuroscience

Volume 2017 (2017), Article ID 1240323, 11 pages

https://doi.org/10.1155/2017/1240323

## Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals

^{1}KACST, Riyadh, Saudi Arabia^{2}KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), Electrical Engineering Department, King Saud University, Riyadh, Saudi Arabia

Correspondence should be addressed to Turky N. Alotaiby

Received 26 April 2017; Revised 15 August 2017; Accepted 4 October 2017; Published 31 October 2017

Academic Editor: Pedro Antonio Gutierrez

Copyright © 2017 Turky N. Alotaiby et al. 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.

#### Abstract

This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysis classifier, which is then employed in the testing phase. A leave-one-out cross-validation strategy is adopted in the experiments. The experimental results for seizure prediction obtained from the records of 24 patients from the CHB-MIT database reveal that the proposed predictor can achieve an average sensitivity of 0.89, an average false prediction rate of 0.39, and an average prediction time of 68.71 minutes using a 120-minute prediction horizon.

#### 1. Introduction

Epilepsy is a brain disorder characterized by excessive, infrequent, and synchronous discharge of a large number of neurons [1] and affects 1% of the world’s population [2]. Epileptic seizure can be managed in two-thirds of the patients using prescription drugs, while another 8% can be cured using resected surgery. Seizures of about 25% of patients with epilepsy cannot be managed sufficiently by any available therapy [2, 3]. Therefore, the early anticipation of seizures could be very valuable for those patients, caregivers, or family members to save patients and others from possible hazards [4, 5]. An effective seizure prediction approach would improve the quality of patients’ daily lives. Electroencephalogram (EEG) is the most often used brain disorders’ diagnostic tool, specifically for epilepsy [6]. It is measuring the voltage fluctuations resulting from ionic current within the neurons of the brain through electrodes [7]. There are two types of EEGs: intracranial EEG (iEEG) and scalp EEG (sEEG). In iEEG, electrodes are placed directly on the exposed surface of the brain to record the electrical signals. However, in sEEG, the electrical signals are collected with electrodes placed on the scalp area according to certain placement specifications, such as the International 10-20 System.

Seizure prediction is based on the hypothesis that there exists a transition state (preictal) between the interictal (normal state) and the ictal state (seizure). There are numbers of clinical evidences that support this hypothesis. These evidences include increases in cerebral blood flow [8, 9], cerebral oxygenation [10], cortical excitability [11], highly significant blood-oxygen-level-dependent signal on fMRI studies [12], and variations in heart rate [13, 14]. Accordingly, researchers have invested a great deal of effort over the last decades on attempting to predict epileptic seizures based on iEEG and sEEG signals, where the latter are more convenient to apply clinically. Around forty years ago, Viglione and his colleagues presented the first attempt for seizure prediction [15, 16]. After that, many researchers published their attempts to predict epileptic seizures suing different methods.

Several time-domain techniques have been reported in the literature for seizure prediction [17–22, 22–24, 24–39]. Transform methods [40–54], attractor state analysis [55], and neural mass models [56] have been used for EEG seizure prediction. A comprehensive review of the most recently developed seizure prediction methods can be found in [2, 57–59].

Common spatial pattern (CSP) is a feature extraction algorithm used in different applications, such as electromyography (EMG) signal separation [60], EEG signal analysis for motor imagery purposes [61, 62], and, more recently, seizure detection [63–65]. The objective of this paper is to develop a patient-specific CSP-based seizure prediction algorithm for sEEG signals. The extracted feature using the CSP will be fed to a linear classifier to classify the epoch as either a preictal or interictal segment. Note that the data segment preceding the seizure onset is called the preictal interval and ranges from a few seconds to several hours long [38, 47, 54]. The performance of the proposed predictor is compared with the random and Poisson predictors and with existing sEEG-based prediction methods [17, 18, 28, 41, 45, 47, 48, 54, 55]. The results show that the proposed prediction method could be of potential value for early warnings for epileptic patients and/or their caregivers.

The remainder of the paper is organized as follows. The CSP mathematical formulation is discussed in Section 2. The data collection and seizure prediction approach are presented in Section 3. Section 4 presents the prediction performance metrics. The experimental results and comparisons with other existing seizure prediction algorithms are provided in Section 5. Finally, Section 6 offers concluding remarks.

#### 2. Common Spatial Pattern (CSP)

CSP is a statistical method that was introduced to the field of EEG analysis by Koles et al. [66, 67] and is used to extract spatial filters for discriminating between two classes of EEG signals. In this work, the CSP method is used to distinguish between two classes, preictal and interictal EEG activities, by constructing a projection matrix, , that minimizes the variance for preictal activity and maximizes it for the other class. The following steps describe the mathematical formulation of the CSP approach [66, 67]:(1)Calculate the normalized covariance matrix for each data segment where is the number of channels, is the number of samples, and is the transpose operation.(2)Perform an averaging process on the covariance matrices of each class () to find two discriminated covariance matrices, (preictal state) and (interictal state), and then find the composed covariance matrix :(3)Decompose the composed matrix using singular value decomposition (SVD) to find the Eigenvalue matrix and normalized Eigenvector matrix :(4)Form a new matrix : to obtain the following two matrices: and share common eigenvectors. Hence, the sum of the corresponding Eigenvalues of the two matrices is always 1.(5)Apply the SVD to the matrices and as follows:Note that , where is the identity matrix, , and represent the matrix of eigenvectors and the diagonal matrix of Eigenvalues, respectively. The Eigenvalues are then sorted in descending order; thus, the CSP projection matrix is formulated as .

#### 3. Materials and Methods

##### 3.1. Clinical Data

In this work, long-term continuous multichannel sEEG recordings of 24 patients from a publicly available dataset (Children’s Hospital Boston [CHB-MIT] database [68]), which consists of sEEG recordings from pediatric subjects with intractable seizures, were used. Subjects were monitored for up to several days following withdrawal of antiseizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. This data contains 987.85 hours, with 170 seizures. Each seizure onset is marked by an experienced electroencephalographer and corresponds to the onset of a rhythmic activity that is associated with a clinical seizure [11, 22, 26–32]. The data is multichannel in nature, with 23 or 18 channels for each patient obtained by sampling at a rate of 256 Hz. The International 10-20 System of EEG electrode positions and nomenclature was used for these recordings. A summary of this dataset is presented in Table 1. The data is segmented into one-hour-long records. Records that do not contain seizure activity are referred to as nonseizure records, and those that contain one or more seizures are referred to as seizure records.