EEG Signals Of Epileptic Patients Research Articles

Psychosocial Factors and Psychological Characteristics of Personality of Patients with Chronic Diseases Using Artificial Intelligence Data Mining Technology and Wireless Network Cloud Service Platform.

It was to explore the application value of health cloud service platform based on data mining algorithm and wireless network in the analysis of psychosocial factors and psychological characteristics of personality of patients with chronic diseases. Based on the demand analysis of cloud service platform for chronic diseases, a health cloud service platform including three modules was established: support layer, application layer, and interaction layer; and K-means algorithm and Apriori algorithm were used to mine and process data. The changes of pulse wave and EEG signal of epileptic seizures before and after processing by wireless network health cloud service platform were analyzed. 42 patients with idiopathic generalized epilepsy were selected as the research subjects, and 40 volunteers with normal physical examination during the same period were selected as the control group. The differences in the basic clinical characteristics data, Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Symptom Checklist 90 (SCL-90), and Eysenck Personality Questionnaire-Revision Short Scale for Chinese (EPQ-RSC) were compared between the two groups. It was found that the initial EEG signals of epileptic patients had noise pollution before and after the seizure, and the noise in the EEG signals was filtered out after digital technology processing in the cloud service platform. The maximum number of epileptic patients aged 18∼30 years was 17 (40.48%), and the mean scores of HAMD and HAMA scales in the epileptic group were significantly higher than those in the control group (P < 0.001). The total score of SCL-90, somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychosis in the epilepsy group were obviously higher than those in the control group (P < 0.01). The mean value of EPQ-RSC and neuroticism (N) was clearly higher (P < 0.05), the mean value of extroversion (E) was significantly lower (P < 0.01), and the mean value of Lie Scale was significantly higher (P < 0.05) in the epileptic group in contrast with those in the control group. It indicates that the cloud service platform for chronic diseases based on artificial intelligence data mining technology and wireless network has potential application value. Epilepsy patients with chronic diseases should be paid more attention to their psychosocial factors and psychological characteristics of personality in the treatment process.

Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.

Design of a Network Permutation Entropy and Its Applications for Chaotic Time Series and EEG Signals

Measuring the complexity of time series provides an important indicator for characteristic analysis of nonlinear systems. The permutation entropy (PE) is widely used, but it still needs to be modified. In this paper, the PE algorithm is improved by introducing the concept of the network, and the network PE (NPE) is proposed. The connections are established based on both the patterns and weights of the reconstructed vectors. The complexity of different chaotic systems is analyzed. As with the PE algorithm, the NPE algorithm-based analysis results are also reliable for chaotic systems. Finally, the NPE is applied to estimate the complexity of EEG signals of normal healthy persons and epileptic patients. It is shown that the normal healthy persons have the largest NPE values, while the EEG signals of epileptic patients are lower during both seizure-free intervals and seizure activity. Hence, NPE could be used as an alternative to PE for the nonlinear characteristics of chaotic systems and EEG signal-based physiological and biomedical analysis.

Epileptic seizure identification using entropy of FBSE based EEG rhythms

This paper has proposed a new method for classification of epileptic seizures based on weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier–Bessel series expansion (FBSE) of electroencephalogram (EEG) signals. In this method, each EEG signal is considered for the FBSE to determine the coefficients then the selected order values of these coefficients are used to obtain the rhythms (δ, θ, α, β, and γ). The feature used in this work is WMRPE which has been extracted from rhythms of EEG signal. These computed feature values are then used in distinct classifiers such as random forest (RF), least squares support vector machine (LS-SVM), and regression for classification of epileptic seizure EEG signals. The classification is validated using 10-fold cross-validation technique in order to verify the robustness of the proposed work and the optimization of classification performance has been implemented with feature ranking methods. The proposed method is also tested with additive white Gaussian noise (AWGN) at different signal to noise ratio (SNR) levels. In this present work, the proposed method provides better classification accuracy results for seven different classification problems in which six are two-class classification problems and the remaining one is three-class classification problem. In two-class classification problems, the EEG signals of epileptic patients at the time of seizure interval has been classified with six distinct classes of EEG signals i.e., healthy subjects with eyes open condition, healthy subjects with eyes closed condition, epileptic patients at the time of seizure-free interval captured from hippocampal formation zone, epileptic patients at the time of seizure-free interval captured from epileptogenic zone, epileptic patients at the time of seizure-free interval, and subjects at the time of non-seizure (healthy and seizure-free) interval. In three-class classification problem, the EEG signals corresponding to epileptic patients during seizure interval, healthy subjects with eyes open condition, and epileptic patients at the time of seizure-free interval captured from hippocampal formation zone have been classified with the help of proposed method. The obtained results show that the WMRPE with FBSE based rhythms provides better classification accuracies in comparison to the existing rhythms based method and other existing methods. The proposed method is also perform well in noisy environment at different SNR levels.

Frequency behaviours of electroencephalography signals in epileptic patients from a wavelet thresholding perspective

The objective of this research paper is to study the frequency behaviors of EEG signals of Epileptic Patients at five different empirical groups such as alpha, beta, theta, delta and gamma bands through wavelet transforms. The Thresholding methods are used for frequency realization of different frequency bands in EEG waveforms. The wavelets such as (Haar, dB2 dB 4 and Sym8) are taken into consideration.

A Peak Synchronization Measure for Multiple Signals

Peaks signify important events in a signal. In a pair of signals how peaks are occurring with mutual correspondence may offer us significant insights into the mutual interdependence between the two signals based on important events. In this work we proposed a novel synchronization measure between two signals, called peak synchronization, which measures the simultaneity of occurrence of peaks in the signals. We subsequently generalized it to more than two signals. We showed that our measure of synchronization is largely independent of the underlying parameter values. A time complexity analysis of the algorithm has also been presented. We applied the measure on intracranial EEG signals of epileptic patients and found that the enhanced synchronization during an epileptic seizure can be modeled better by the new peak synchronization measure than the classical amplitude correlation method.