Wavelet-based feature extraction for classification of epileptic seizure EEG signal

Electroencephalogram (EEG) signal-processing techniques are the prominent role in the detection and prediction of epileptic seizures. The detection of epileptic activity is cumbersome and needs a detailed analysis of the EEG data. Therefore, an efficient method for classifying EEG data is required. In this work, a constructive pattern recognition strategy for analysing EEG data as normal and epileptic seizure has been proposed. With this strategy, the signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to reduce the dimensionality of EEG data. These reduced features were used as input to Naïve Bayes and K-Nearest Neighbour Classifier to classify normal or epileptic seizure signal. The performance of classifier was evaluated in terms of accuracy, sensitivity and specificity. The experimental results show that PCA with Naïve Bayes classifier provides 98.6% accuracy and LDA with Naïve Bayes classifier attains improved result of 99.8% accuracy. Also, the result shows that PCA, LDA with K-NN achieves 98.5% and 100% accuracy. This evaluation is used to propose a reliable, practical epilepsy detection method to enhance the patient’s care and quality of life.     

Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis

In epilepsy diagnosis or epileptic seizure detection, much effort has been focused on finding effective combination of feature extraction and classification methods. In this paper, we develop a wavelet-based sparse functional linear model for representation of EEG signals. The aim of this modeling approach is to capture discriminative random components of EEG signals using wavelet variances. To achieve this goal, a forward search algorithm is proposed for determination of an appropriate wavelet decomposition level. Two EEG databases from University of Bonn and University of Freiburg are used for illustration of applicability of the proposed method to both epilepsy diagnosis and epileptic seizure detection problems. For this data considered, we show that wavelet-based sparse functional linear model with a simple classifier such as 1-NN classification method leads to higher classification results than those obtained using other complicated methods such as support vector machine. This approach produces a 100 % classification accuracy for various classification tasks using the EEG database from University of Bonn, and outperforms many other state-of-the-art techniques. The proposed classification scheme leads to 99 % overall classification accuracy for the EEG data from University of Freiburg.     

利用整体经验模态分解和随机森林的脑电信号分类研究

癫痫脑电信号的自动监测与分类在临床医学上具有重要意义。针对脑电信号的非平稳特点,提出一种基于整体经验模态分解和随机森林相结合的脑电信号分类方法。选取波恩大学脑电信号数据集中癫痫发作间期和发作期的200个单通道信号,共819 400个数据作为样本。首先利用整体模态分解将癫痫脑电信号分解成多个固有模态函数,然后对各阶固有模态函数提取有效特征,最后分别用随机森林和最小二乘支持向量机对脑电信号的特征进行分类。将随机森林与最小二乘支持向量机分类正确识别率对比,结果表明,随机森林分类方法对发作期和发作间期的癫痫脑电信号的分类效果比较理想,识别精度为99.60％,高于最小二乘支持向量机的准确性。该方法的提出能有效提高临床癫痫脑电信号分析的效率。     

Sparse representation-based EMD and BLDA for automatic seizure detection

Epilepsy is a serious chronic neurological disorder, which affects more than 50 million people worldwide, and automatic seizure detection on EEG recordings is extremely required in the diagnosis and monitoring of epilepsy. This paper presents a novel seizure detection method using sparse representation-based Earth Mover’s Distance (SR-EMD). In the proposed algorithm, wavelet decomposition is executed on the original EEG recordings with five scales, and the scales 3, 4 and 5 are selected to structure the distributions of EEG signals. Then, the Gaussian mixture models (GMMs) of EEG signals are estimated and the distances between GMMs are computed using SR-EMD as EEG features. After that, EEG features are sent to Bayesian linear discriminant analysis classifier for classification. To improve the detection accuracy, the post-processing procedure is employed finally. The long-term intracranial EEG dataset with 21 patients is used to evaluate the performance of the method, and the satisfactory sensitivity of 93.54 %, specificity of 97.57 % and false detection rate of 0.223/h are achieved. The results indicate that SR-EMD is more effective and efficient than the conventional Earth Mover’s Distance (EMD). Moreover, the good performance and fast speed of this algorithm make it suitable for the real-time seizure monitoring application.     

EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography (EEG) is an oversensitive operation and prone to errors, which has motivated the researchers to develop effective automated seizure detection methods. This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases. The proposed method consists of three steps: (i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis (MSPCA), (ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition (EMD), discrete wavelet transform (DWT), and dual-tree complex wavelet transform (DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals, and (iii) allocate the feature vector to the relevant class (i.e., seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA). The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process. The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.     

Baseline drift removal and denoising of MCG data using EEMD: Role of noise amplitude and the thresholding effect

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).     

Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform

Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide. The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram (EEG) as a noninvasive procedure to record neuronal activities in the brain. EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals. Shannon entropy, collision entropy, transfer entropy, conditional probability, and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform. Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification. Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector. The accuracy of the proposed approach is higher for Q=2 and J=10. Transfer entropy is observed to be significant for different class combinations. Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time. The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals. The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.     

一种基于EEG特征提取的癫痫棘波综合检测判决方法

癫痫特征的自动检测在临床应用上具有重要的意义。本研究综合小波变换、非线性能量算子、特征提取和神经网络等技术，提出了一种癫痫棘波检测系统，充分发挥各技术的优点，在对真实脑电数据的处理中，表现出良好的性能。     

A novel real-time patient-specific seizure diagnosis algorithm based on analysis of EEG and ECG signals using spectral and spatial features and improved particle swarm optimization classifier

This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by Gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature.     

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

Background

Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients’ EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals.

Methods

EEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method.

Results

It demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%.

Conclusions

This is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.

     

基于加权复杂网络度熵和的癫痫发作检测方法

癫痫发作检测一直是一项富有挑战性的工作,随着癫痫发病率的增加,高性能癫痫自动检测算法在临床上可以减轻医务工作者的工作量,具有重要的临床医学研究意义。提出基于加权水平可视图的癫痫检测新方法。首先利用加权水平可视图将单通道脑电信号转化为复杂网络,并提取生成的复杂网络的度的平方和权重度分布熵两个特征;最后将两个特征之和作为单特征输入到线性分类器中,用来识别癫痫间歇期和发作期信号。对波恩大学的癫痫脑电数据集进行实验,评价所提出的检测算法的性能。使用该癫痫脑电数据集间歇期和发作期各100个实验样本,样本长度为1 024。实验结果表明,所提出的方法具有较高的分类精度,可达到98.5%。由于分类的特征为单特征,所以更加简单高效,可用于癫痫发作在线自动检测。     

EEG signals classification of epileptic patients via feature selection and voting criteria in intelligent method

Epileptic disease can be diagnosed by using intelligent methods on the Electroencephalograph (EEG) signals. In this paper, wavelet packet transform (WPT) was used in each of the frequency bands and wavelet coefficients were obtained, then the energy and entropy function was done on the wavelet coefficients and used as initial feature vectors. In the next step, eight and 15 features from 30 initial energy and entropy features were selected as the final features because their receiver operating characteristic (ROC) curve areas were higher than others. There were seven classifier inputs. These seven classifiers consisted of four artificial neural networks (ANN) with different structures, support vector machines (SVM), K-nearest neighbours (KNN) and a hybrid network. Each classifier was trained by 0.5, 0.8 and 0.9 EEG signals. After the training process, a fusion network based on a voting criteria was used to make the algorithm robust against the possible changes in each classifier and increase the classification accuracy. Finally, the algorithm was tested by other EEG signals. As a result, normal and epileptic classes were detected with total classification accuracy of 99–100%.     

儿童失神癫(癎)脑电图的多尺度特征

目的:研究儿童失神癫癎脑电图的多尺度定量特征。方法:对15例失神癫癎患儿10次临床发作和20次亚临床癎样放电的脑电图进行子波分析,提取失神癫癎发作过程中脑电信号的多尺度定量典型特征,与发作前10 s及发作后10 s的脑电信号进行比较,并与12例正常同龄儿童脑电图进行比较。结果:研究显示儿童失神癫癎发作过程中脑电信号的多尺度典型特征主要表现为12尺度(对应频率3 Hz)的节律性活动显著增强,发作时20尺度(低频大尺度,对应频率0．12 Hz)结构与频率3 Hz的结构具有非正常的跳跃式尺度关系,3 Hz节律性棘慢复合波与大尺度(频率1 Hz以下)背景低频放电结构共同存在。发作过程中分尺度功率主要集中在20尺度和12尺度,其演变规律为20尺度能量逐渐减低,12尺度能量逐渐增加。10次临床发作的脑电信号均显示上述特征。发作前10 s和后10 s的脑电多尺度信号中仍然存在隐性的3 Hz棘慢复合波成分,与一般认为3 Hz棘慢复合波突起突止不同．而从传统的脑电图上无法分辨出发作前后的这些多尺度细节的定量特征。亚临床癎样放电的多尺度特征与发作期无明显差别,但持续时间短。结论:子波分析作为一种新的信号分析方法,适合于脑电信号的分析,可以获得比传统视觉脑电图更多的定量信息。通过对失神癫癎患儿的脑电信号进行子波分析,得到其发作过程中典型的多尺度定量特征,有助于失神癫癎发作的临床辅助诊断、预后评价以及神经电生理机理的基础研究。     

An automatic patient-specific seizure onset detection method in intracranial EEG based on incremental nonlinear dimensionality reduction

Epileptic seizure features always include the morphology and spatial distribution of nonlinear waveforms in the electroencephalographic (EEG) signals. In this study, we propose a novel incremental learning scheme based on nonlinear dimensionality reduction for automatic patient-specific seizure onset detection. The method allows for identification of seizure onset times in long-term EEG signals acquired from epileptic patients. Firstly, a nonlinear dimensionality reduction (NDR) method called local tangent space alignment (LTSA) is used to reduce the dimensionality of available initial feature sets extracted with continuous wavelet transform (CWT). One-dimensional manifold which reflects the intrinsic dynamics of seizure onset is obtained. For each patient, IEEG recordings containing one seizure onset is sufficient to train the initial one-dimensional manifold. Secondly, an unsupervised incremental learning scheme is proposed to update the initial manifold when the unlabelled EEG segments flow in sequentially. The incremental learning scheme can cluster the new coming samples into the trained patterns (containing or not containing seizure onsets). Intracranial EEG recordings from 21 patients with duration of 193.8h and 82 seizures are used for the evaluation of the method. Average sensitivity of 98.8%, average uninteresting false positive rate of 0.24/h, average interesting false positives rate of 0.25/h, and average detection delay of 10.8s are obtained. Our method offers simple, accurate training with less human intervening and can be well used in off-line seizure detection. The unsupervised incremental learning scheme has the potential in identifying novel IEEG classes (different onset patterns) within the data.     

Comparison of feature selection and classification methods for a brain–computer interface driven by non-motor imagery

The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain–computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the combination of features of two and three channels, and with a discriminative measure (r ²). Moreover, Bayesian classifier and support vector machine (SVM) with Gaussian kernel were compared. The results showed that the two classifiers provided similar classification accuracy. Conversely, the exhaustive search of the optimal combination of features from two and three channels significantly improved performance with respect to using r ² for channel selection. With features optimally extracted from three channels with optimized scaling filter in the discrete wavelet transform, the classification accuracy was on average 72.2%. Thus, the choice of features had greater impact on performance than the choice of the classifier for discrimination between the two non-motor imagery tasks investigated. The results are relevant for the choice of the translation algorithm for an on-line BCI system based on non-motor imagery.     

基于小波包变换的癫痫脑电棘波检测

为了能够较好地实现癫痫患者脑电的棘波检测,提出一种将棘波物理特征（幅度、频率）和小波包变换结合的算法,用于癫痫患者脑电信号的棘波检测。首先利用小波包变换对癫痫脑电信号进行小波包分解,将脑电波频率（0~30 Hz）划分为3层;其次根据脑电波的频率范围重构第三层节点频率S（3, 0）（0~10.85 Hz）、S（3, 1）（10.85~21.7 Hz）、S（3, 2）（21.7~32.55 Hz）的脑电信号;最后取棘波的幅度作为检测阈值分别提取癫痫患者健康期、癫痫发作间期及癫痫发作期的棘波。实验结果证明,当数据的采样频率为173.61 Hz、信号长度为23.6 s时,该算法能够提取不同癫痫患者在不同时期的棘波信号,该算法棘波的误检率为12.02%、漏检率为11.70%。因此,本文所采用的算法在癫痫棘波检测中具有良好的效果。     

Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset

In this paper, complexity analysis and dynamic characteristics of electroencephalogram (EEG) signal based on maximal overlap discrete wavelet transform (MODWT) has been exploited for the identification of seizure onset. Since wavelet-based studies were well suited for classification of normal and epileptic seizure EEG, we have applied MODWT which is an improved version of discrete wavelet transform (DWT). The selection of optimal wavelet sub-band and features plays a crucial role to understand the brain dynamics in epileptic patients. Therefore, we have investigated MODWT using four different wavelets, namely Haar, Coif4, Dmey, and Sym4 sub-bands until seven levels. Further, we have explored the potentials of six entropies, namely sigmoid, Shannon, wavelet, Renyi, Tsallis, and Steins unbiased risk estimator (SURE) entropies in each sub-band. The sigmoid entropy extracted from Haar wavelet in sub-band D4 showed the highest accuracy of 98.44% using support vector machine classifier for the EEG collected from Ramaiah Medical College and Hospitals (RMCH). Further, the highest accuracy of 100% and 94.51% was achieved for the University of Bonn (UBonn) and CHB-MIT databases respectively. The findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive respectively. Besides, in terms of dynamic characteristics, MODWT results revealed that the highest energy present in sub-bands D2, D3, and D4 and entropies in those respective sub-bands outperformed other entropies in terms of classification results for RMCH database. Similarly, using all the entropies, sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases respectively. In conclusion, the comparison results of MODWT outperformed DWT.     

Random ensemble learning for EEG classification

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients’ quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility.     

Fuzzy support vector machine for classification of EEG signals using wavelet-based features

Translation of electroencephalographic (EEG) recordings into control signals for brain–computer interface (BCI) systems needs to be based on a robust classification of the various types of information. EEG-based BCI features are often noisy and likely to contain outliers. This contribution describes the application of a fuzzy support vector machine (FSVM) with a radial basis function kernel for classifying motor imagery tasks, while the statistical features over the set of the wavelet coefficients were extracted to characterize the time–frequency distribution of EEG signals. In the proposed FSVM classifier, a low fraction of support vectors was used as a criterion for choosing the kernel parameter and the trade-off parameter, together with the membership parameter based solely on training data. FSVM and support vector machine (SVM) classifiers outperformed the winner of the BCI Competition 2003 and other similar studies on the same Graz dataset, in terms of the competition criterion of the mutual information (MI), while the FSVM classifier yielded a better performance than the SVM approach. FSVM and SVM classifiers perform much better than the winner of the BCI Competition 2005 on the same Graz dataset for the subject O3 according to the competition criterion of the maximal MI steepness, while the FSVM classifier outperforms the SVM method. The proposed FSVM model has potential in reducing the effects of noise or outliers in the online classification of EEG signals in BCIs.     

Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings.