Seizure detection approach using S-transform and singular value decomposition

Highlights

• S-transform in combination with singular value decomposition (SVD) is employed to extract EEG features.

• Bayesian linear discriminant analysis (BLDA) is used as the classifier.

• Postprocessing is utilized to obtain more accurate and stable detection results.

• Experiments with long-term EEG demonstrate the effectiveness.

Abstract

Automatic seizure detection plays a significant role in the diagnosis of epilepsy. This paper presents a novel method based on S-transform and singular value decomposition (SVD) for seizure detection. Primarily, S-transform is performed on EEG signals, and the obtained time–frequency matrix is divided into submatrices. Then, the singular values of each submatrix are extracted using singular value decomposition (SVD). Effective features are constructed by adding the largest singular values in the same frequency band together and fed into Bayesian linear discriminant analysis (BLDA) classifier for decision. Finally, postprocessing is applied to obtain higher sensitivity and lower false detection rate. A total of 183.07 hours of intracranial EEG recordings containing 82 seizure events from 20 patients were used to evaluate the system. The proposed method had a sensitivity of 96.40% and a specificity of 99.01%, with a false detection rate of 0.16/h.

Introduction

Epilepsy is a chronic neurological disorder characterized by paroxysmal and excessive neuronal discharges, which can result in loss of awareness or consciousness and disturbances of movement, sensation, mood, or mental function [1]. Approximately 1% of the world's population suffers from epilepsy [2], [3]. Electroencephalography (EEG) is an important tool for the diagnosis of epilepsy, which reflects the electrical activity of the brain [4]. At present, long-term EEG recordings are usually inspected by experts visually to identify seizure activities, which is a time-consuming and tedious task. As a result, automatic seizure detection technology is very necessary to assist medical staff in analyzing EEG recordings.

In recent years, there have been various kinds of automatic seizure detection methods proposed. The method presented by Gotman [5] was widely applied, this technique decomposed EEG signals into half waves and detected seizures using peak amplitude, duration, slope, and sharpness. Later, Grewal and Gotman developed a seizure warning system utilizing spectral feature extraction and Bayes's theorem [6]. Nicolaou and Georgiou proposed a seizure detection algorithm based on permutation entropy (PE) and support vector machine (SVM) [7] to classify segments of normal and epileptiform EEGs. In addition, time–frequency analysis methods have also been employed for seizure detection [8], [9], [10], [11], such as short-time Fourier transform (STFT) and wavelet transform (WT). Short-time Fourier transform decomposes EEG signals into time–frequency domain using a fixed and moving window function, but it has the limitation of analyzing signals at single resolution because of fixed window width. Wavelet transform solves the problem of STFT and provides multiresolution analysis via varying window width, which uses short windows at high frequencies and long windows at low frequencies. However, its accuracy depends on the chosen basis wavelet, and its computation is complicated.

The S-transform first introduced by Stockwell et al. [12] is an effective time–frequency analysis technique that has been widely used for signal processing, such as detection of multiple power quality disturbances [13], electrocardiogram (ECG) beat classification [14], and heart sound segmentation [15]. Stockwell transform is a combination of continuous wavelet transform (CWT) and STFT and overcomes the disadvantages of them [16]. It presents a good time–frequency resolution characteristic by using a moving and scalable localizing Gaussian window. Singular value decomposition (SVD) is a data decomposition approach that describes the distribution of matrix data and can reduce the effect of noise. Using SVD, Kim et al. extracted illumination–invariant features for face recognition [17]. Kanjilal et al. employed SVD on the composite maternal ECG signal for fetal ECG extraction [18]. Singular value decomposition was also utilized for earthquake prediction [19]. The singular values are very stable and robust to the change of matrix elements. In this study, we used the singular values of EEG time–frequency matrix obtained with S-transform as features for seizure detection.

Many of classifiers have been used for seizure detection, such as support vector machine (SVM), extreme learning machine (ELM), and artificial neural network (ANN). Bayesian linear discriminant analysis (BLDA) can be treated as an extension of Fisher's linear discriminant analysis (FLDA) [20], which is an efficient method for machine learning. In contrast to FLDA, BLDA employs regularization to avoid overfitting to high dimensional and noisy datasets and has shown its superior performance for motor imagery classification [21]. Bayesian linear discriminant analysis was employed to train the classifier in this study.

In this paper, we first propose a novel method for seizure detection using S-transform in combination with SVD. The rest of this paper is organized as follows. Section 2 introduces the material and methods including six parts: (1) a brief introduction of the intracranial EEG (iEEG) dataset, (2) the S-transform time–frequency analysis, (3) the feature extraction method based on SVD, (4) the Bayesian linear discriminant analysis, (5) postprocessing, and (6) the performance evaluation approach. Section 3 shows the experimental results, and is followed by a discussion of the proposed method in Section 4. Finally, the conclusion is brought forward in Section 5.