Abstract:Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is used to decompose each frame into a number of subbands. Different entropies as well as a group of features with which to characterize the spike events are extracted … Show more
“…The research of [ 52 ] shows that one of the main points in epileptic seizure detection is finding the most relevant feature. The authors proposed using a graph eigen decomposition (GED)-based approach, which reduces unnecessary attributes.…”
Based on the growing interest in encephalography to enhance human–computer interaction (HCI) and develop brain–computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band—electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions—valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20–8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.
“…The research of [ 52 ] shows that one of the main points in epileptic seizure detection is finding the most relevant feature. The authors proposed using a graph eigen decomposition (GED)-based approach, which reduces unnecessary attributes.…”
Based on the growing interest in encephalography to enhance human–computer interaction (HCI) and develop brain–computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band—electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions—valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20–8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.
“…As EEG signals are typical non-stationary time-series signals, time-frequency analysis approaches such as Short-Time Fourier Transform, Wavelet Transform, and Empirical Mode Decomposition have been commonly employed to generate time-frequency representations for EEG signals [ 25 , 26 , 27 ]. Stockwell transform (S-transform), proposed by Stockwell et al [ 28 ], is a combined approach of short-time Fourier transform and wavelet transform, allowing for multi-resolution analysis of time series with relatively low computational complexity.…”
Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.
“…Many researchers have recently used graph theory to analyze multi-channel EEG signals. Molla et al used the graph eigen decomposition-based method to select the features for classification in a feedforward neural network [14]. Zhao et al constructed a graph according to the correlation matrix to enhance the feature embedding of EEG signals without manually designed features [15].…”
Epileptic seizure is one of the most common neurological disorders characterized by sudden abnormal discharge of neurons in the brain. Automated seizure detection using electroencephalograph (EEG) recordings would improve the quality of treatment and reduce medical overhead. The purpose of this paper is to design an automated seizure detection framework that can effectively identify seizure and non-seizure events by discovering connectivity between brain regions. In this work a weighted directed graph-based method with effective brain connectivity (EBC) is proposed for seizure detection. The weighted directed graph is built by analyzing the correlation among the different regions of the brain. Then, graph theory-based measures are used to extract features for classification. Furthermore, we illustrate the ability of the proposed method to achieve seizure detection for the patient-specific model and the cross-patient model. The results show that the proposed method achieves accuracy values of 99.97% and 98.29% for the patient-specific model and the cross-patient model in the CHB-MIT dataset, respectively. These results demonstrate that the proposed method achieves an effective classification performance and can be used to provide assistance for automatic seizure detection and clinical diagnosis.
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