Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.

The contemporary diagnosis of epileptic seizures is dominated by non-invasive EEG signal analysis and classification. In this paper, we propose a patient-specific seizure detection technique, which selects the optimal feature subsets and trains a dedicated classifier for each patient in order to maximize the classification performance. Our method exploits time domain, frequency domain, time-frequency domain and non-linear feature sets. Then, by using Conditional Mutual Information Maximization (CMIM) as the feature selection method the optimal feature subset is chosen over which the Support Vector Machine is trained as the classifier. In this study, both train and test sets contain 50% of seizure and non-seizure segments of the EEG signal. From the CHB-MIT Scalp benchmark EEG dataset, we used the EEG data from four subjects with overall 21 hours of recording. Support Vector Machine (SVM) with linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set: i.e., an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average of 93.78% sensitivity and a specificity of 99.05% are obtained using CMIM.

Accurate classification of brain activity from electroencephalogram signals is essential for diagnosing neurological disorders such as epilepsy. In this paper, we propose an explainable deep learning method for epileptic seizure detection. The proposed approach converts electroencephalogram signals into audio waveforms, which are then transformed into time–frequency representations using two distinct continuous wavelet transforms, i.e., the Morlet and the Mexican Hat. These wavelet-based spectrograms effectively capture both temporal and spectral characteristics of the electroencephalogram signal data and serve as inputs to a set of convolutional neural network models with the aim to detect seizure activity. To improve model transparency, the proposed method integrates three class activation mapping techniques aimed to visualize the salient regions in the wavelet images that influence each prediction. Experimental evaluation on a real-world dataset emphasizes the efficacy of wavelet-based preprocessing in electroencephalogram signal analysis in prompt epileptic seizure detection, showing an accuracy equal to 0.922.

During the long-term operation of wind turbines, due to environmental factors and equipment aging, the health and reliability of each component will gradually decline, leading to failure. To assess the health status of wind turbines, timely grasp the subsequent changes and development trends, it is necessary to extract degradation characteristics, including time domain, frequency domain, and time-frequency domain characteristics. These degradation characteristics can reflect the operating status of the equipment, help build health indicator curves, and evaluate the health status of high-speed shaft bearings of wind turbines. Selecting reasonable degradation characteristics is an important prerequisite for constructing a health index curve, and using evaluation indicators to construct a comprehensive evaluation function to screen degradation characteristics. The feature fusion method based on a self-organizing feature mapping network is used to fuse multiple selected degradation features and fuse the selected multiple degradation features into a curve that can reflect the bearing degradation process. Finally, a quantitative analysis is performed on the health index curve to scientifically assess the health status of bearings. Bearings are one of the key components of wind turbines. Based on the health index curve constructed in this article, an appropriate prediction model is selected to predict the health index trend of bearings. A timely and effective grasp of the health trends of wind turbine bearings is of great practical significance for formulating scientific and reasonable maintenance measures for wind farms. The work of this article will be divided into the following four parts: (1) Extracting the time domain, frequency domain, and time-frequency domain degradation characteristics of high-speed shaft bearing vibration signals of wind turbines; (2) Comprehensive evaluation using monotonicity, correlation, and robustness constructs function to screen degenerate features; (3) Use self-organizing feature mapping network. The network integrates the selected degradation features and constructs a health index curve; (4) Based on the constructed health index curve, optimize the BiLSTM network hyperparameters through Bayesian and establish a BO-BiLSTM network model to achieve a more accurate and scientific prediction of the health index trend.

Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

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

Epilepsy is a damage in brain cells that cause a disturbance in the electrical signals of the brain, leading to nervous system disorder. Studies show that about 1% of the world’s population suffers from this disorder [1]. Epilepsy can be diagnosed by studying the electroencephalogram (EEG)signals,, i.e. the electrical signals emitted from the brain and represent its activity. This paper proposed an epileptic seizures detection based on analysis of EEG signals. The detection is carried out firstly recording the EEG signals using the EEG device. The noise is then eliminated before features extraction process is carried out using HOS-WPD. These features are then used to train two classifiers, namely Navie Bayes and FFNN, by which the signals are classified as either benign or seizure. Experimental evaluation was carried out to compare the detection performance of both algorithms in terms of Precision, Recall, and Accuracy and using MIT BIH Dataset.

We present a new method for separation of the rhythms of the electroencephalogram (EEG) signal. The proposed method is based on the Hilbert-Huang transform (HHT). The HHT consists two steps namely empirical mode decomposition (EMD) and the Hilbert transform (HT). The EMD decomposes EEG signal into set of narrow-band intrinsic mode functions (IMFs), and the Hilbert transformation of these IMFs provide instantaneous frequency estimation of the IMFs. The instantaneous frequency estimation of IMFs have been used as a feature to identify the IMFs in order to separate rhythms of EEG signal. The central tendency measure (CTM) has been used to quantify the variability in second order difference (SOD) plots of rhythms of the EEG signal. The CTM parameter is very effective to discriminate epileptic seizure EEG signals from the seizure-free EEG signals. The experimental results show the effectiveness of the proposed method for epileptic seizure detection.

Automatic detection of epileptic seizure using dynamic fuzzy neural networks

Epilepsy is a neurological disorder that affects a serious number of people all around the world. Detection of epileptic seizures using EEG signals occupies an important part in the diagnosis of epilepsy. The aim of this study is to develop a method for seizure detection based on Empirical Mode Decomposition. In this method, EEG signals are decomposed to their Intrinsic Mode Functions and first 4 IMFs's maximum, minimum, mean, standart deviation and energy values are used for classification. This method was tested on 123 minutes of iktal data and 200 minutes of inter-iktal data using 3 different classifiers. For all clasifiers, over %80 sensitivity and over %95 specificity were otained. These results show that epileptic seizure detection in EEG records via EMD is very promising.

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.

Epilepsy is most prevalent nervous disorder which is becoming a global health concern nowadays. It impacts patient’s livelihood due to rapid onset of seizures or occurring seizures. Therefore, this paper proposes an approach for automated prediction and detection of epileptic seizure stages using electroencephalogram (EEG) signals with ensemble empirical mode decomposition (EEMD) technique. The acquired EEG signals are disintegrated by EEMD to various intrinsic mode functions (IMFs). The signal is reconstructed from the summation of significant IMFs and multi-entropy features are extracted from the reconstructed signal. Different tree classifiers are studied to analyze the effectiveness of current approach. The performance evaluation demonstrates that J48 graft tree classifier with EEMD and multi-entropy features is an effective method for classification of different stages of epileptic seizures with accuracy of 95.33%, specificity of 97.6%, and sensitivity of 95.3%. Thus, the current method would be an efficient for automated prediction and detection of epileptic seizures in real-time.

In this paper, a new epileptic seizure detection method using fuzzy-rules-based sub-band specific features and layered directed acyclic graph support vector machine (LDAG-SVM) is proposed for classification of electroencephalogram (EEG) signals. Wavelet transformation is used to decompose the input EEG signals into various sub-bands. The nonlinear features, namely approximate entropy, largest Lyapunov exponent and correlation dimension, are extracted from each sub-band. In this proposed work, sub-band specific feature subset that is reduced in size and capable of discriminating samples is selected by employing fuzzy rules. For classification purpose, a new LDAG-SVM is used for detecting epileptic seizure. Every sub-band has its own characteristics. If appropriate features which characterize the specific sub-band are selected, then the classification accuracy is improved and computational complexity is reduced. The important advantage of the fuzzy logic is its close relation to human thinking. Due to the lengthy record and intra-professional variability, automation of epileptic detection is inevitable. Fuzzy rules are the natural choice of employing human expertise to build machine learning system. Performances of the proposed methods are evaluated using two different benchmark EEG datasets, namely Bonn and CHB-MIT. The performance measures such as classification accuracy, sensitivity, specificity, execution time and receiver operating characteristics are used to measure and analyze the performances of the proposed classifier. The proposed LDAG-SVM with fuzzy-rules-based selected sub-band specific features provides better performance in terms of improved classification accuracy with reduced execution time compared to existing methods.

Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there is still a large group of epilepsy patients that continues to have frequent seizures. For these patients automated detection of epileptic seizures can be of great clinical importance. Seizure detection can influence daily care or can be used to evaluate treatment effect. Furthermore automated detection can be used to trigger an alarm system during seizures that might be harmful to the patient. This thesis focusses on accelerometry (ACM) based seizure detection. A detailed overview is provided, on the perspectives for long-term epilepsy monitoring and automated seizure detection. The value of accelerometry for seizure detection is shown by means of a clinical evaluation and the first steps are made towards automatic detection of epileptic seizures based on ACM. With accelerometers movements are recorded. A large group of epileptic seizures manifest in specific movement patterns, so called motor seizures. Chapter 2 of this thesis presents an overview of the published literature on available methods for epileptic seizure detection in a long-term monitoring context. Based on this overview recommendations are formulated that should be used in seizure detection research and development. It is shown that for seizure detection in home environments, other sensor modalities besides EEG become more important. The use of alternative sensor modalities (such as ACM) is relatively new and so is the algorithm development for seizure detection based on these measures. It was also found that for both the adaptation of existing techniques and the development of new algorithms, clinical information should be taken more into account. The value of ACM for seizure detection is shown by means of a clinical evaluation in chapter 3. Here 3-D ACM- and EEG/video-recordings of 18 patients with severe epilepsy are visually analyzed. A striking outcome presented in this chapter is the large number of visually detected seizures versus the number of seizures that was expected on forehand and the number of seizures that was observed by the nurses. These results underscore the need for an automatic seizure detection device even more, since in the current situation many seizures are missed and therefore it is possible that patients do not get the right (medical) treatment. It was also observed that 95% of the ACM-patterns during motor seizures are sequences of three elementary patterns: myoclonic, tonic and clonic patterns. These characteristic patterns are a starting point for the development of methods for automated seizure detection based on ACM. It was decided to use a modular approach for the detection methodology and develop algorithms separately for motor activity in general, myoclonic seizures and tonic seizures. Furthermore, clinical information is incorporated in the detection methodology. Therefore in this thesis features were used that are either based on the shape of the patterns of interest as described in clinical practice (chapter 4 and 7), or the features were based on a physiological model with parameters that are related to seizure duration and intensity (chapter 5 and 6). In chapter 4 an algorithm is developed to distinguish periods with and without movement from ACM-data. Hence, when there is no movement there is no motor seizure. The amount of data that needs further analysis for seizure detection is thus reduced. From 15 ACM-signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space a linear threshold function is used for classification. For training and testing the algorithm ACM data along with video data are used from nocturnal recordings in mentally retarded patients with severe epilepsy. Using this algorithm the amount of data that needs further analysis is reduced considerably. The results also indicate that the algorithm is robust for fluctuations across patients and thus there is no need for training the algorithm for each new patient. For the remaining data it needs to be established whether the detected movement is seizure related or not. To this purpose a model is developed for the accelerometer pattern measured on the arm during a myoclonic seizure (chapter 5). The model consists of a mechanical and an electrophysiological part. This model is used as a matched wavelet filter to detect myoclonic seizures. In chapter 6 the model based wavelet is compared to three other time frequency measures: the short time Fourier transform, the Wigner distribution and the continuous wavelet transform using a Daubechies wavelet. All four time-frequency methods are evaluated in a linear classification setup. Data from mentally retarded patients with severe epilepsy are used for training and evaluation. The results show that both wavelets are useful for detection of myoclonic seizures. On top of that, our model based wavelet has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiological meaningful. Besides myoclonic seizures, the model is also useful for the detection of clonic seizures; physiologically these are repetitive myoclonic seizures. Finally for the detection of tonic seizures, in chapter 7 a set of features is studied that incorporate the mean characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification in the multi-dimensional feature space. For training and testing the algorithm, again data are used from recordings in mentally retarded patients with severe epilepsy. The results show that our approach is useful for the automated detection of tonic seizures based on 3-D ACM and that it is a promising contribution in a complete multi-sensor seizure detection setup.