552 research outputs found
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification
Identifying seizure activities in non-stationary electroencephalography (EEG)
is a challenging task, since it is time-consuming, burdensome, and dependent on
expensive human resources and subject to error and bias. A computerized seizure
identification scheme can eradicate the above problems, assist clinicians and
benefit epilepsy research. So far, several attempts were made to develop
automatic systems to help neurophysiologists accurately identify epileptic
seizures. In this research, a fully automated system is presented to
automatically detect the various states of the epileptic seizure. The proposed
method is based on sparse representation-based classification (SRC) theory and
the proposed dictionary learning using electroencephalogram (EEG) signals.
Furthermore, the proposed method does not require additional preprocessing and
extraction of features which is common in the existing methods. The proposed
method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9
scenarios. It is also robust to the measurement noise of level as much as 0 dB.
Compared to state-of-the-art algorithms and other common methods, the proposed
method outperformed them in terms of sensitivity, specificity and accuracy.
Moreover, it includes the most comprehensive scenarios for epileptic seizure
detection, including different combinations of 2 to 5 class scenarios. The
proposed automatic identification of epileptic seizures method can reduce the
burden on medical professionals in analyzing large data through visual
inspection as well as in deprived societies suffering from a shortage of
functional magnetic resonance imaging (fMRI) equipment and specialized
physician
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Automatic classification of epileptic seizure types in electroencephalograms
(EEGs) data can enable more precise diagnosis and efficient management of the
disease. This task is challenging due to factors such as low signal-to-noise
ratios, signal artefacts, high variance in seizure semiology among epileptic
patients, and limited availability of clinical data. To overcome these
challenges, in this paper, we present SeizureNet, a deep learning framework
which learns multi-spectral feature embeddings using an ensemble architecture
for cross-patient seizure type classification. We used the recently released
TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of
SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of
up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross
validation for scalp EEG based multi-class seizure type classification. We also
show that the high-level feature embeddings learnt by SeizureNet considerably
improve the accuracy of smaller networks through knowledge distillation for
applications with low-memory constraints
Model Based Sparse Feature Extraction for Biomedical Signal Classification
This article focuses on model based sparse feature extraction of biomedical signals for classification problems, which stems from sparse representation in modern signal processing. In the presented work, a novel approach based on sparse principal component analysis (SPCA) is proposed to extract signal features. This method involves partitioning signals and utilizing SPCA to select only a limited number of signal segments in order to construct signal principal components during the training stage. For signal classification purposes, a set of regression models based on sparse principal components of the selected training signal segments is constructed. Within this approach, model residuals are estimated and used as signal features for classification. The applications of the proposed approach are demonstrated by using both the synthetic data and real EEG signals. The high classification accuracy results suggest that the proposed methods may be useful for automatic event detection using long-term observational signals. keywords: Sparse Principal Component Analysis, Sparse Feature Extraction, Signal Classification, Long-term Signal
Noise Reduction of EEG Signals Using Autoencoders Built Upon GRU based RNN Layers
Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an efficient noise reduction technique to get more accurate recordings. Numerous traditional techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), wavelet transformations and machine learning techniques were proposed for reducing the noise in EEG signals. The aim of this paper is to investigate the effectiveness of stacked autoencoders built upon Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) layers (GRU-AE) against PCA. To achieve this, Harrell-Davis decile values for the reconstructed signals’ signal-to- noise ratio distributions were compared and it was found that the GRU-AE outperformed PCA for noise reduction of EEG signals
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
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