2,164 research outputs found
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
EEG sleep stages identification based on weighted undirected complex networks
Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks.
Methods
each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks.
Results
In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals.
Conclusions
An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard
Development of electroencephalogram (EEG) signals classification techniques
Electroencephalography (EEG) is one of the most important signals recorded from
humans. It can assist scientists and experts to understand the most complex part of the
human body, the brain. Thus, analysing EEG signals is the most preponderant process
to the problem of extracting significant information from brain dynamics. It plays a
prominent role in brain studies. The EEG data are very important for diagnosing a
variety of brain disorders, such as epilepsy, sleep problems, and also assisting
disability patients to interact with their environment through brain computer interface
(BCI). However, the EEG signals contain a huge amount of information about the
brain’s activities. But the analysis and classification of these kinds of signals is still
restricted. In addition, the manual examination of these signals for diagnosing related
diseases is time consuming and sometimes does not work accurately. Several studies
have attempted to develop different analysis and classification techniques to categorise
the EEG recordings.
The analysis of EEG recordings can lead to a better understanding of the cognitive
process. It is used to extract the important features and reduce the dimensions of EEG
data. In the classification process, machine learning algorithms are used to detect the
particular class of EEG signal based on its extracted features. The performance of these
algorithms, in which the class membership of the input signal is determined, can then
be used to infer what event in the real-world process occurred to produce the input
signal. The classification procedure has the potential to assist experts to diagnose the
related brain disorders. To evaluate and diagnose neurological disorders properly, it is
necessary to develop new automatic classification techniques. These techniques will
help to classify different EEG signals and determine whether a person is in a good
health or not. This project aims to develop new techniques to enhance the analysis and
classification of different categories of EEG data.
A simple random sampling (SRS) and sequential feature selection (SFS) method
was developed and named the SRS_SFS method. In this method, firstly, a SRS
technique was used to extract statistical features from the original EEG data in time
domain. The extracted features were used as the input to a SFS algorithm for key features selection. A least square support vector machine (LS_SVM) method was then
applied for EEG signals classification to evaluate the performance of the proposed
approach.
Secondly, a novel approach that combines optimum allocation (OA) and spectral
density estimation methods was proposed to analyse EEG signals and classify an
epileptic seizure. In this study, the OA technique was introduced in two levels to
determine representative sample points from the EEG recordings. To reduce the
dimensions of sample points and extract representative features from each OA sample
segment, two power spectral density estimation methods, periodogram and
autoregressive, were used. At the end, three popular machine learning methods
(support vector machine (SVM), quadratic discriminant analysis, and k-nearest
neighbor (k-NN)) were employed to evaluate the performance of the suggested
algorithm.
Additionally, a Tunable Q-factor wavelet transform (TQWT) based algorithm was
developed for epileptic EEG feature extraction. The extracted features were forwarded
to the bagging tree, k-NN, and SVM as classifiers to evaluate the performance of the
proposed feature extraction technique. The proposed TQWT method was tested on two
different EEG databases.
Finally, a new classification system was presented for epileptic seizures detection in
EEGs blending frequency domain with information gain (InfoGain) technique. Fast
Fourier transform (FFT) or discrete wavelet transform (DWT) were applied
individually to analyse EEG recording signals into frequency bands for feature
extraction. To select the most important feature, the infoGain technique was employed.
A LS_SVM classifier was used to evaluate the performance of this system.
The research indicates that the proposed techniques are very practical and effective
for classifying epileptic EEG disorders and can assist to present the most important
clinical information about patients with brain disorders
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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