980 research outputs found
Non-linear classifiers applied to EEG analysis for epilepsy seizure detection
This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions. (C) 2017 Elsevier Ltd. All rights reserved
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Epilepsy is a neurological disorder and for its detection, encephalography
(EEG) is a commonly used clinical approach. Manual inspection of EEG brain
signals is a time-consuming and laborious process, which puts heavy burden on
neurologists and affects their performance. Several automatic techniques have
been proposed using traditional approaches to assist neurologists in detecting
binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal.
These methods do not perform well when classifying ternary case e.g. ictal vs.
normal vs. inter-ictal; the maximum accuracy for this case by the
state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a
system based on deep learning, which is an ensemble of pyramidal
one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model,
the bottleneck is the large number of learnable parameters. P-1D-CNN works on
the concept of refinement approach and it results in 60% fewer parameters
compared to traditional CNN models. Further to overcome the limitations of
small amount of data, we proposed augmentation schemes for learning P-1D-CNN
model. In almost all the cases concerning epilepsy detection, the proposed
system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
We propose a novel Coupled Hidden Markov Model to detect epileptic seizures
in multichannel electroencephalography (EEG) data. Our model defines a network
of seizure propagation paths to capture both the temporal and spatial evolution
of epileptic activity. To address the intractability introduced by the coupled
interactions, we derive a variational inference procedure to efficiently infer
the seizure evolution from spectral patterns in the EEG data. We validate our
model on EEG aquired under clinical conditions in the Epilepsy Monitoring Unit
of the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstrate
that our model outperforms three baseline approaches which rely on a classical
detection framework. Our model also demonstrates the potential to localize
seizure onset zones in focal epilepsy.Comment: To appear in MICCAI 2018 Proceeding
Dynamical Component Analysis (DyCA) and its application on epileptic EEG
Dynamical Component Analysis (DyCA) is a recently-proposed method to detect
projection vectors to reduce the dimensionality of multi-variate deterministic
datasets. It is based on the solution of a generalized eigenvalue problem and
therefore straight forward to implement. DyCA is introduced and applied to EEG
data of epileptic seizures. The obtained eigenvectors are used to project the
signal and the corresponding trajectories in phase space are compared with PCA
and ICA-projections. The eigenvalues of DyCA are utilized for seizure detection
and the obtained results in terms of specificity, false discovery rate and miss
rate are compared to other seizure detection algorithms.Comment: 5 pages, 4 figures, accepted for IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP) 201
Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset
Epileptic seizure or epilepsy is a chronic neurological disorder that occurs due to brain neurons\u27 abnormal activities and has affected approximately 50 million people worldwide. Epilepsy can affect patients’ health and lead to life-threatening emergencies. Early detection of epilepsy is highly effective in avoiding seizures by intervening in treatment. The electroencephalogram (EEG) signal, which contains valuable information of electrical activity in the brain, is a standard neuroimaging tool used by clinicians to monitor and diagnose epilepsy. Visually inspecting the EEG signal is an expensive, tedious, and error-prone practice. Moreover, the result varies with different neurophysiologists for an identical reading. Thus, automatically classifying epilepsy into different epileptic states with a high accuracy rate is an urgent requirement and has long been investigated. This PhD thesis contributes to the epileptic seizure detection problem using Machine Learning (ML) techniques.
Machine learning algorithms have been implemented to automatically classifying epilepsy from EEG data. Imbalance class distribution problems and effective feature extraction from the EEG signals are the two major concerns towards effectively and efficiently applying machine learning algorithms for epilepsy classification. The algorithms exhibit biased results towards the majority class when classes are imbalanced, while effective feature extraction can improve classification performance.
In this thesis, we presented three different novel frameworks to effectively classify epileptic states while addressing the above issues. Firstly, a deep neural network-based framework exploring different sampling techniques was proposed where both traditional and state-of-the-art sampling techniques were experimented with and evaluated for their capability of improving the imbalance ratio and classification performance. Secondly, a novel integrated machine learning-based framework was proposed to effectively learn from EEG imbalanced data leveraging the Principal Component Analysis method to extract high- and low-variant principal components, which are empirically customized for the imbalanced data classification. This study showed that principal components associated with low variances can capture implicit patterns of the minority class of a dataset. Next, we proposed a novel framework to effectively classify epilepsy leveraging summary statistics analysis of window-based features of EEG signals. The framework first denoised the signals using power spectrum density analysis and replaced outliers with k-NN imputer. Next, window level features were extracted from statistical, temporal, and spectral domains. Basic summary statistics are then computed from the extracted features to feed into different machine learning classifiers. An optimal set of features are selected leveraging variance thresholding and dropping correlated features before feeding the features for classification.
Finally, we applied traditional machine learning classifiers such as Support Vector Machine, Decision Tree, Random Forest, and k-Nearest Neighbors along with Deep Neural Networks to classify epilepsy. We experimented the frameworks with a benchmark dataset through rigorous experimental settings and displayed the effectiveness of the proposed frameworks in terms of accuracy, precision, recall, and F-beta score
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