192 research outputs found
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
An adaptive neuro-fuzzy inference system for the physiological presentation of seizure disorder
Seizure is the clinical manifestation of an excessive, hypersynchronous discharge of a population of cortical neurons accompanied by indescribable "pins- and needles-like” bodily sensations, smells or sounds, fear or depression, hallucinations, momentary jerks or head nods, staring with loss of awareness, and convulsive movements (i.e., involuntary muscle contractions) lasting for some seconds to a few minutes. In this work, an attempt is made to promote a better understanding of seizure disorder by proposing an adaptive neuro-fuzzy simulation model as a tool for capturing the physiological presentation of the disorder. Decision making was performed in two stages, namely the feature extractions using Microsoft Excel for corresponding digital value of the waveform of the EEG recordings of a seizure and those of a non-seizure patient directly from the EEG machine, and the transient features are accurately captured and localized in both time and amplitude. This extracted data were used for our Adaptive Neuro-Fuzzy Inference System (ANFIS) training and the ANFIS was trained with the backpropagation gradient descent method in combination with the least squares method to establish the validity of our ANFIS. The result shows an accuracy of 90.7% of predictions as the number of epochs increase.Keywords: Adaptive Neuro-Fuzzy Inference System, Electroencephalogram, Seizure Disorde
A hybrid unsupervised approach toward EEG epileptic spikes detection
Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy
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
A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved
Classification of Signals by Means of Genetic Programming
[Abstract] This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.Instituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; 10SIN105004P
An Enhanced Automated Epileptic Seizure Detection Using ANFIS, FFA and EPSO Algorithms
Objectives: Electroencephalogram (EEG) signal gives a viable perception about the neurological action of the human brain that aids the detection of epilepsy. The objective of this study is to build an accurate automated hybrid model for epileptic seizure detection. Methods: This work develops a computer-aided diagnosis (CAD) machine learning model which can spontaneously classify pre-ictal and ictal EEG signals. In the proposed method two most effective nature inspired algorithms, Firefly algorithm (FFA) and Efficient Particle Swarm Optimization (EPSO) are used to determine the optimum parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) network. Results: Compared to the FFA and EPSO algorithm separately, the composite (ANFIS+FFA+EPSO) optimization algorithm outperforms in all respects. The proposed technique achieved accuracy, specificity, and sensitivity of 99.87%, 98.71% and 100% respectively. Conclusion: The ANFIS-FFA-EPSO method is able to enhance the seizure detection outcomes for demand forecast in hospital
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform
Epilepsy is one of the common neurological disorders characterized by
recurrent and uncontrollable seizures, which seriously affect the life of
patients. In many cases, electroencephalograms signal can provide important
physiological information about the activity of the human brain which can be
used to diagnose epilepsy. However, visual inspection of a large number of
electroencephalogram signals is very time-consuming and can often lead to
inconsistencies in physicians' diagnoses. Quantification of abnormalities in
brain signals can indicate brain conditions and pathology so the
electroencephalogram (EEG) signal plays a key role in the diagnosis of
epilepsy. In this article, an attempt has been made to create a single
instruction for diagnosing epilepsy, which consists of two steps. In the first
step, a low-pass filter was used to preprocess the data and three separate
mid-pass filters for different frequency bands and a multilayer neural network
were designed. In the second step, the wavelet transform technique was used to
process data. In particular, this paper proposes a multilayer perceptron neural
network classifier for the diagnosis of epilepsy, that requires normal data and
epilepsy data for education, but this classifier can recognize normal
disorders, epilepsy, and even other disorders taught in educational examples.
Also, the value of using electroencephalogram signal has been evaluated in two
ways: using wavelet transform and non-using wavelet transform. Finally, the
evaluation results indicate a relatively uniform impact factor on the use or
non-use of wavelet transform on the improvement of epilepsy data functions, but
in the end, it was shown that the use of perceptron multilayer neural network
can provide a higher accuracy coefficient for experts.Comment: 8 pages, 4 tables, 3 figure
Data mining an EEG dataset with an emphasis on dimensionality reduction
The human brain is obviously a complex system, and exhibits rich spatiotemporal dynamics. Among the non-invasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Early attempts to analyse EEG data relied on visual inspection of EEG records. Since the introduction of EEG recordings, the volume of data generated from a study involving a single patient has increased exponentially. Therefore, automation based on pattern classification techniques have been applied with considerable success. In this study, a multi-step approach for the classification of EEG signal has been adopted. We have analysed sets of EEG time series recording from healthy volunteers with open eyes and intracranial EEG recordings from patients with epilepsy during ictal (seizure) periods. In the present work, we have employed a discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time - that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. Principal components analysis (PCA) and rough sets have been used to reduce the data dimensionality. A multi-classifier scheme consists of LVQ2.1 neural networks have been developed for the classification task. The experimental results validated the proposed methodology
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