299 research outputs found
Range entropy: A bridge between signal complexity and self-similarity
Approximate entropy (ApEn) and sample entropy (SampEn) are widely used for
temporal complexity analysis of real-world phenomena. However, their
relationship with the Hurst exponent as a measure of self-similarity is not
widely studied. Additionally, ApEn and SampEn are susceptible to signal
amplitude changes. A common practice for addressing this issue is to correct
their input signal amplitude by its standard deviation. In this study, we first
show, using simulations, that ApEn and SampEn are related to the Hurst exponent
in their tolerance r and embedding dimension m parameters. We then propose a
modification to ApEn and SampEn called range entropy or RangeEn. We show that
RangeEn is more robust to nonstationary signal changes, and it has a more
linear relationship with the Hurst exponent, compared to ApEn and SampEn.
RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1
(minimum entropy) and it has no need for signal amplitude correction. Finally,
we demonstrate the clinical usefulness of signal entropy measures for
characterisation of epileptic EEG data as a real-world example.Comment: This is the revised and published version in Entrop
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
Complexity Measures for Normal and Epileptic EEG Signals using ApEn, SampEn and SEN
There are numerous applications of EEG signal processing such as monitoring alertness, coma, and brain death, controlling an aesthesia, investigating epilepsy and locating seizure origin, testing epilepsy drug effects, monitoring the brain development, and investigating mental disorders; where data size is too long and requires long time to observe the data by clinician or neurologist. EEG signal processing techniques can be used effectively in such applications. The configuration of the signal waveform may contain valuable and useful information about the different state of the brain since biological signal is highly random in both time and frequency domain. Thus computerized analysis is necessary. Being a non-stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. The importance of entropy based features to recognize the normal EEGs, and ictal as well as interictal epileptic seizures. Three features, such as, Approximate entropy, Sample entropy, and Spectral entropy are used to take out the quantitative entropy features from the given EEG time series data of various time frames of 0.88s, and 1s .Average value of entropies for epileptic time series is less than non epileptic time series
A machine learning system for automated whole-brain seizure detection
Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier
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
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
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
Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection
Electroencephalogram (EEG) that measures the electrical activity of the brain has been widely employed for diagnosing epilepsy which is one kind of brain abnormalities. With the advancement of low-cost wearable brain-computer interface devices, it is possible to monitor EEG for epileptic seizure detection in daily use. However, it is still challenging to develop seizure classification algorithms with a considerable higher accuracy and lower complexity. In this study, we propose a lightweight method which can reduce the number of features for a multiclass classification to identify three different seizure statuses (i.e., Healthy, Interictal and Epileptic seizure) through EEG signals with a wearable EEG sensors using Extended Correlation-Based Feature Selection (ECFS). More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into five frequency bands and secondly, we extract the features while the unnecessary feature space was eliminated by developing the ECFS method. Finally, the features were fed into five different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Network and Multilayer Perceptron. Experimental results have shown that Logistic Model Trees provides the highest accuracy of 97.6% comparing to other classifiers
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