Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring

Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities.EEG signals capture important information pertinent to different physiological brain states.In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring.The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression.To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie’s wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification.Extensive experimental work is conducted, utilizing four classification models.The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value.The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application’s accuracy

Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks

Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders

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

Evidence Theory-Based Approach for Epileptic Seizure Detection Using EEG Signals

Electroencephalogram (EEG) is one of the potential physiological signals used for detecting epileptic seizure. Discriminant features, representing different brain conditions, are often extracted for diagnosis purposes. On-line detection necessitates that these features are to be computed efficiently. In this work, an evidence theory-based approach for epileptic detection, using such features, and several classifiers, is proposed. Within the framework of the evidence theory, each of these classifiers is considered a source of information and as such, it may have its own local view of the current brain state. To reach a global view, these sources are fused using the Dempster's rule of combination. Each classifier is given a certain weight, during the fusion process, based on both its overall classification accuracy as well as its precision rate for the respective class. Experimental work is done where five time domain features are obtained from EEG signals and used by a set classifiers, namely, Bayesian, K-nearest neighbor, neural network, linear discriminant analysis, and support vector machine classifiers. Higher classification accuracy of 89.5% is achieved by the proposed approach compared to 75.07% and 87.71% accuracy obtained from the worst and best classifier from the used set of classifiers, and those are linear discriminant analysis and support vector machine, respectively. 2012 IEEE.Qatar National Research FundScopu