299 research outputs found

    Range entropy: A bridge between signal complexity and self-similarity

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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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