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

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

Epileptic seizure is the common neurological disorder, which is generally identified by electroencephalogram (EEG) signals. In this paper, a new feature extraction methodology based on optimum allocation sampling (OAS) and Teager energy operator (TEO) is proposed for detection of seizure EEG signals. The OAS scheme selects the finite length homogeneous sequence from non-homogeneous recorded EEG signal. The trend of selected sequence by OAS is still non-linear, which is analyzed by non-linear operator TEO. The TEO convert non-linear but homogenous EEG sequence into amplitude-frequency modulated (AM-FM) components. The statistical measures of AM-FM components used as input features to least squares support vector machine classifier for classification of seizure and seizure-free EEG signals. The proposed methodology is evaluated on a benchmark epileptic seizure EEG database. The experimental results demonstrate that the proposed scheme has capability to effectively distinguish seizure and seizure-free EEG signals.

In his paper a method of seizure detection has been proposed based on the Discrete Wavelet Transform (DWT) analysis of the dominant Intrinsic mode function(IMF) resulting from the Empirical Mode Decomposition(EMD) of the EEG signals. Considering the normalized energy, Fourier spectrum and cross-correlation coefficient analysis, only the 4th Level DWT coefficients of the dominant IMF is found reasonable for feature computation. In order to reduce the dimension of the feature vector, Higher order statistics of these coefficients are employed to form he feature vector. The reduced feature vector thus formed is found effective for distinguishing seizure and non-seizure EEG signals when fed to a k-nearest neighborhood (k-NN) classifier. Extensive simulations are carried out using a benchmark EEG dataset. It is shown that the proposed method is capable of producing greater sensitivity, specificity and accuracy in comparison to that obtained by a sate-of-the-art method using the same EEG dataset and classifier.

Epilepsy is a common neurological disorder involving spontaneous seizures. While electroencephalography (EEG) is a useful diagnostic approach of epilepsy that records the electrical activity of the brain, detection of epileptic seizures is still clinically difficult. This study proposes a segmental classification approach for the detection of epileptic seizure in EEG signals. Regularized least squares and smoothness priors methods are applied to minimize the nonstationary components in the signals. The optimal frequency band energy features are selected by using the sequential floating forward selectrion (SFFS) algorithm, with linear, quadratic and cubic discriminant function as classifiers. The results show that when the quadratic discriminant function is applied, the sensitivity and specificity of seizure detection reach a maximum of 98.1% and 95.6% respectively for discriminating health subjects against epileptic subjects in seizure period, and the overall classification rate is 97.2%.

Epilepsy is a neurological disorder that affects people of any age, which can be detected by Electroencephalogram (EEG) signals. This paper proposes a novel method called Volume of Phase Space Representation (VOPSR) to classify seizure and seizure-free EEG signals automatically. Primarily, the recorded EEG signal is disintegrated into several Intrinsic Mode Functions (IMFs) using the Empirical Mode Decomposition (EMD) method and the three-dimensional phase space have been reconstructed for the obtained IMFs. The volume is measured for the obtained 3D-PSR for different IMFs called VOPSR, which is used as a feature set for the classification of Epileptic seizure EEG signals. Support vector machine (SVM) is used as a classifier for the classification of epileptic and epileptic-free EEG signals. The classification performance of the proposed method is evaluated under different kernels such as Linear, Polynomial and Radial Basis Function (RBF) kernels. Finally, the proposed method outperforms noteworthy state-of-the-art classification methods in the context of epileptic EEG signals, achieving 99.13% accuracy (average) with the Linear, Polynomial, and RBF kernels. The proposed technique can be used to detect epilepsy from the EEG signals automatically without human intervention.

The EEG signals prove to be an efficient tool in analyzing Epileptic seizure. The parameters like mean, standard deviation and their confidence interval of the seizure EEG signal are compared with the normal EEG signal. The seizure EEG signals have higher values of parameter estimates when compared to the normal EEG signals. In this paper Morlet wavelet transform is also performed on the EEG signals. Significant variations are observed in the Morlet coefficient of the seizure EEG when compared to the normal EEG signal.KeywordsEpileptic SeizureEEG signalsparameter estimationwavelet transform

To elucidate the neurophysiological mechanisms underlying seizures according to electroencephalogram (EEG) signals, a neurophysiologically-based neural mass model that can produce EEG-like signals was adopted to simulate ictal and interictal EEG signals. A delay unit and a gain unit were added to the Wendling model to fit EEG signals in the time domain. An optimal parameter set minimising the error between observed and simulated EEG was identified using a genetic algorithm. To compare the inhibition and excitation during the ictal and interictal periods, the model parameters were determined for two sets of EEG signals using the proposed method. The results show that the model with identified parameters can simulate the real EEG signal well, with a mean square error of 0.0315-0.2138. Fifty repetitions for every selected EEG signal showed that the dispersion of the identified parameters was small in most cases, and the identification procedure generally showed similar values. Comparison of the model parameters of seizure and non-seizure EEG signals showed enhanced excitability, attenuated inhibition, and a more concentrated energy distribution in the frequency domain during the ictal periods. The experimental results for long-term EEG signals revealed continuous changes in the model parameters during epileptic seizures.

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

An electroencephalogram (EEG) is utilized to assess activity in the brain and find various epileptic conditions of the brain. But, EEG signals are regularly corrupted with different types of physiological artifacts. Due to this reason, epileptic seizure detection and further analysis of the seizure information becomes challenging and leads to ambiguous decisions made by neuro-specialists. Hence, it is important to remove these EEG physiological artifacts in the pre-processing phase itself. This research paper discusses EEG physiological artifact removal based on multiresolutional analysis and thresholding. Multiresolutional analysis is employed using discrete wavelet transform (DWT). Using DWT, the signal containing the seizure information is separated in to two components namely, high frequency and low frequency. Threshold is applied to these separated frequency components to eliminate artifacts from the original EEG seizure signal. The outcome of the investigation proved that the proposed technique removes the physiological disturbances in the seizure EEG signal by retaining the significant seizure EEG data. The examination also investigates the performance of the wavelet function to remove physiological artifacts in terms of signal-to-noise ratio and cross relation coefficient. The analysis proves that the proposed technique is able to conserve seizure information by removing artifacts in the examined datasets which is then verified using a simulated noisy signal.KeywordsEEGMultiresolutionPhysiological artifacts seizureWavelet transform

Preprocessing and signal enhancement methods can be applied to improve quality of signal detection in the presence of background noise. This article concerns the novel preprocessing techniques for enhancement of EEG signals and background noise suppression during epileptic seizure by using Independent Component Analysis (ICA) and Novelty Filtering which employs an artificial neural network performing autoassociative task. ICA is used for removing biological artifacts and noise from EEG signals and stabilizing its probabilistic behavior. At the next stage Novelty Filtering technique has been used to suppress background EEG activities and improvement of signal-to-noise ratio for the detection and identification of epileptic seizure. A novelty filter consist of neural networks of retinotopic topology having a one-to-one correspondence between input and output units to perform autoassociative mappings. Filtering effect is obtained by subtracting the network output from the input vectors. The results show that novelty filtering combined with ICA is a powerful preprocessing tool for the detection and enhancement of epileptic seizure activity in the nonstationary EEG signals.KeywordsNovelty FilteringAutoassociative Neural NetworksIndependent Component analysisEpileptic SeizureEEG Signals

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

Field-programmable gate arrays (FPGAs) are being extensively used for a wide range of digital applications due to their flexibility and reprogrammability. This paper presents a FPGA implementation of the second-order difference plot (SODP) technique which can be used for the classification of ictal and seizure-free electroencephalogram (EEG) signals. Empirical mode decomposition (EMD) can break down an EEG signal into simple oscillatory modes called intrinsic mode functions (IMFs). The hardware design developed takes a sampled IMF of an EEG signal as input and generates its SODP while simultaneously calculating the 95 percent confidence ellipse area of the SODP. The ellipse area can be used as a parameter for detecting epileptic seizures in EEG signals. The digital circuit was designed in the Vivado integrated development environment (IDE) using Verilog hardware description language (HDL) and a Xilinx Artix-7 xc7a100tcsg324 FPGA was used to verify operation of the physical implementation. The hardware was tested on EEG data made publicly available by the University of Bonn and the results were found to be consistent with MATLAB simulations.

Detecting epileptic seizures from EEG signals allows for earlier and more accurate diagnosis of patients with epilepsy. This study evaluates the performance of three traditional machine learning (ML) models: support vector machines (SVM), random forests (RF), and K-nearest neighbors (KNN) for the binary classification of EEG segments with or without seizures. The CHB-MIT database was used, without preprocessing, to mimic a real clinical setting. Linear temporal features were extracted in three configurations: 9 widely cited features, 21 novel features, and a combination of all 30 features. The results show that RF is the most robust and reliable classifier; with 30 features, it achieved 96.75% precision, 94.30% accuracy, 91.71% recall, 94.14% F1 score, and 96.88% specificity. These findings emphasize the importance of proper feature selection and suggest new possibilities for improving the accuracy of traditional ML models in detecting epileptic seizures from EEG signals.

Among all of the Machine Learning techniques used for classification tasks, Artificial Neural Networks (ANNs) have obtained much success in their applications. However, their development usually requires a manual effort from the human expert in which several parameter configurations (architectures, training parameters, etc) are tried. This paper proposes a new evolutionary method that evolves ANNs without any participation from the human expert. This system can be used to evolve feed-forward and recurrent ANNs. A real-world problem has been used to test the behaviour of this system: detection of epileptic seizures in EEG signals. A comparison of the results obtained using recurrent and feedforward ANNs to solve this problem is presented in this paper. This comparison shows the good accuracies obtained by this method (almost 100%). Moreover, these results show an important feature: the system tries to evolve simple ANNs, with a low number of neurons and connections (in many cases, the networks have only 1 hidden neuron).

A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques