710 research outputs found
Classification of Epileptic EEG Signals by Wavelet based CFC
Electroencephalogram, an influential equipment for analyzing humans
activities and recognition of seizure attacks can play a crucial role in
designing accurate systems which can distinguish ictal seizures from regular
brain alertness, since it is the first step towards accomplishing a high
accuracy computer aided diagnosis system (CAD). In this article a novel
approach for classification of ictal signals with wavelet based cross frequency
coupling (CFC) is suggested. After extracting features by wavelet based CFC,
optimal features have been selected by t-test and quadratic discriminant
analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency
Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio
Seizure characterisation using frequency-dependent multivariate dynamics
The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques
and pre-surgical evaluations. In this paper, we expand on recent use of multivariate techniques to study the crosscorrelation
dynamics between electroencephalographic (EEG) channels. The Maximum Overlap Discrete Wavelet
Transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The
dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the
eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency dependent
changes in the correlation structure between channels which may be indicative of seizure activity.
The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over
time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies
during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures,
suggesting that monitoring frequency dependent correlation structure can characterise changes in EEG signals during
these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine
additional seizure characteristics
A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients
Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction
Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient
Epileptic Seizure Detection And Prediction From Electroencephalogram Using Neuro-Fuzzy Algorithms
This dissertation presents innovative approaches based on fuzzy logic in epileptic seizure detection and prediction from Electroencephalogram (EEG). The fuzzy rule-based algorithms were developed with the aim to improve quality of life of epilepsy patients by utilizing intelligent methods. An adaptive fuzzy logic system was developed to detect seizure onset in a patient specific way. Fuzzy if-then rules were developed to mimic the human reasoning and taking advantage of the combination in spatial-temporal domain. Fuzzy c-means clustering technique was utilized for optimizing the membership functions for varying patterns in the feature domain. In addition, application of the adaptive neuro-fuzzy inference system (ANFIS) is presented for efficient classification of several commonly arising artifacts from EEG. Finally, we present a neuro-fuzzy approach of seizure prediction by applying the ANFIS. Patient specific ANFIS classifier was constructed to forecast a seizure followed by postprocessing methods. Three nonlinear seizure predictive features were used to characterize changes prior to seizure. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. The ANFIS classifier was constructed based on these features as inputs. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. In this dissertation, the application of the neuro-fuzzy algorithms in epilepsy diagnosis and treatment was demonstrated by applying the methods on different datasets. Several performance measures such as detection delay, sensitivity and specificity were calculated and compared with results reported in literature. The proposed algorithms have potentials to be used in diagnostics and therapeutic applications as they can be implemented in an implantable medical device to detect a seizure, forecast a seizure, and initiate neurostimulation therapy for the purpose of seizure prevention or abortion
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
Epileptic seizures are one of the most crucial
neurological disorders, and their early diagnosis will help the
clinicians to provide accurate treatment for the patients. The
electroencephalogram (EEG) signals are widely used for epileptic
seizures detection, which provides specialists with substantial
information about the functioning of the brain. In this paper,
a novel diagnostic procedure using fuzzy theory and deep
learning techniques is introduced. The proposed method is
evaluated on the Bonn University dataset with six classification
combinations and also on the Freiburg dataset. The tunable-
Q wavelet transform (TQWT) is employed to decompose the
EEG signals into different sub-bands. In the feature extraction
step, 13 different fuzzy entropies are calculated from different
sub-bands of TQWT, and their computational complexities are
calculated to help researchers choose the best set for various
tasks. In the following, an autoencoder (AE) with six layers
is employed for dimensionality reduction. Finally, the standard
adaptive neuro-fuzzy inference system (ANFIS), and also its
variants with grasshopper optimization algorithm (ANFIS-GOA),
particle swarm optimization (ANFIS-PSO), and breeding swarm
optimization (ANFIS-BS) methods are used for classification.
Using our proposed method, ANFIS-BS method has obtained
an accuracy of 99.7
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