Research Letter Discrimination between Ictal and Seizure-Free EEG Signals Using Empirical Mode Decomposition

A new method for analysis of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) and FourierBessel (FB) expansion has been presented in this paper. The EMD decomposes an EEG signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs). The mean frequency (MF) for each IMF has been computed using FB expansion. The MF measure of the IMFs has been used as a feature in order to identify the difference between ictal and seizure-free intracranial EEG signals. It has been shown that the MF feature of the IMFs has provided statistically significant difference between ictal and seizure-free EEG signals. Simulation results are included to illustrate the effectiveness of the proposed method

To Design and Implement a Recommender System based on Brainwave: Applying Empirical Model Decomposition (EMD) and Neural Networks

Recommender systems collect and analyze users’ preferences to help users overcome information overload and make their decisions. In this research, we develop an online book recommender system based on users’ brainwave information. We collect users’ brainwave data by utilizing electroencephalography (EEG) device and apply empirical mode decomposition (EMD) to decompose the brainwave signals into intrinsic mode functions (IMFs). We propose a back-propagation neural networks (BPNN) model to portrait the user’s brainwave preference correlations based on IMFs of brainwave signals, thereby designing and developing the book recommender system. The experimental results show that the recommender system combined with the brainwave analysis can improve accuracy significantly. This research has highlighted a future direction for research and development on human-computer interaction (HCI) design and recommender system

An improved technique for SAR internal wave signal detection

634-640One improved boundary process method for synthetic aperture radar (SAR) internal wave signal detection, which tries to reduce the errors during the extending process for empirical mode decomposition (EMD), has been introduced in this paper. This method is based on EMD and the criterion created by the theory that the max normalized deflection stands for the largest energy. The method was first applied both on several time series decompositions and on SAR nonlinear internal wave temporal series decompositions. The comparison of results showed that this method was more feasible and precise indicating that the method can detect the internal wave signals successfully from SAR image even with a lot noise

Statistical analysis of geomagnetic field intensity differences between ASM and VFM instruments onboard Swarm constellation

From the very first measurements made by the magnetometers onboard Swarm satellites launched by European Space Agency (ESA) in late 2013, it emerged a discrepancy between scalar and vector measurements. An accurate analysis of this phenomenon brought to build an empirical model of the disturbance, highly correlated with the Sun incidence angle, and to correct vector data accordingly. The empirical model adopted by ESA results in a significant decrease in the amplitude of the disturbance affecting VFM measurements so greatly improving the vector magnetic data quality. This study is focused on the characterization of the difference between magnetic field intensity measured by the absolute scalar magnetometer (ASM) and that reconstructed using the vector field magnetometer (VFM) installed on Swarm constellation. Applying empirical mode decomposition method, we find the intrinsic mode functions (IMFs) associated with ASM–VFM total intensity differences obtained with data both uncorrected and corrected for the disturbance correlated with the Sun incidence angle. Surprisingly, no differences are found in the nature of the IMFs embedded in the analyzed signals, being these IMFs characterized by the same dominant periodicities before and after correction. The effect of correction manifests in the decrease in the energy associated with some IMFs contributing to corrected data. Some IMFs identified by analyzing the ASM–VFM intensity discrepancy are characterized by the same dominant periodicities of those obtained by analyzing the temperature fluctuations of the VFM electronic unit. Thus, the disturbance correlated with the Sun incidence angle could be still present in the corrected magnetic data. Furthermore, the ASM–VFM total intensity difference and the VFM electronic unit temperature display a maximal shared information with a time delay that depends on local time. Taken together, these findings may help to relate the features of the observed VFM–ASM total intensity difference to the physical characteristics of the real disturbance thus contributing to improve the empirical model proposed for the correction of data

Epileptic Seizures Prediction Using Machine Learning Methods

Epileptic seizures occur due to disorder in brain functionality which can affect patient’s health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures’ sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects

Discrimination between Ictal and Seizure-Free EEG Signals Using Empirical Mode Decomposition

A new method for analysis of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) and Fourier-Bessel (FB) expansion has been presented in this paper. The EMD decomposes an EEG signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs). The mean frequency (MF) for each IMF has been computed using FB expansion. The MF measure of the IMFs has been used as a feature in order to identify the difference between ictal and seizure-free intracranial EEG signals. It has been shown that the MF feature of the IMFs has provided statistically significant difference between ictal and seizure-free EEG signals. Simulation results are included to illustrate the effectiveness of the proposed method

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems