Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy (original) (raw)
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Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)-were used that have valid outputs for any given data leng...
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Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (S...
EEG Subband Analysis using Approximate Entropy for the Detection of Epilepsy
Abstract: Epilepsy is a neurological disorder which affects the nervous system. Epileptic seizures are due to sudden hyperactivity in certain parts of the brain. Electroencephalogram (EEG) is the commonly used modality for the detection of epilepsy. Automatic seizure detection helps in diagnosis and monitoring of epilepsy especially during long term recordings of EEG. This paper presents non linear analysis of EEG for the detection of epilepsy using approximate entropy (ApEn). The proposed method involves ApEn measured from EEG subbands applied as features to an artificial neural network (ANN) classifier. The ApEn measured from delta, theta, alpha, beta and gamma subbands of normal EEG, ictal and inter ictal EEGs are used as features. In the present work detection of epilepsy is considered as a two class problem. In the first case the classification is done between normal and ictal EEGs and in the second case, classification is done between normal and inter ictal EEGs. For both cases artificial neural networks with back propagation training are used as classifiers. The classification accuracy of 100% is obtained for normal and ictal groups and that of 98.9% is obtained for normal and inters ictal EEGs. Keywords: Electroencephalogram (EEG), ictal and inter ictal EEG, approximate entropy, neural network classifier.
Epilepsy is associated with the abnormal electrical activity in the brain which is detected by recording EEG (Electroencephalogram) signals. This signal is non-linear and chaotic and hence, it is very time-consuming and tedious to analyse them visually. In this work, we have extracted five entropy features such as Approximate Entropy, Sample Entropy, Fuzzy Entropy, Permutation Entropy and Multi-scale Entropy for characterizing the focal signals. We have used Sequential Forward Feature Selection (SFFS) algorithm to select two significant features for epilepsy classification. These two features are given as input to the Least Square Support Vector Machine (LS-SVM) classifier to differentiate normal and focal signal. The classification accuracy of our method is 82%. Moreover, the average computational time for the selected feature set is 47.94 seconds.
Epileptic EEG activity detection for children using entropy-based biomarkers
Neuroscience Informatics
Seizures, which last for a while and are a symptom of epilepsy, are bouts of excessive and abnormally synchronized neuronal activity in the patient’s brain. For young children, in particular, early diagnosis and treatment are essential to optimize the likelihood of the best possible child- specific result. Electroencephalogram (EEG) signals can be inspected to look for epileptic seizures. However, certain epileptic patients with severe cases show high rates of misdiagnosis or failure to notice the seizures, and they do not demonstrate any improvement in healing as a result of their inability to respond to medical treatment. The purpose of this study was to identify EEG biomarkers that may be used to distinguish between children with epilepsy and otherwise healthy and normal subjects. Savitzky-Golay (SG) filter was used to record and analyze the data from 19 EEG channels. EEG background activity was used to calculate amplitude-aware permutation entropy (AAPE) and enhanced permutation entropy (impe). The hypothesis that the irregularity and complexity in epileptic EEG were decreased in comparison with healthy control participants was tested statistically using the t-test (p < 0, 05). As a method of dimensionality reduction, principle component analysis (PCA) was used. The EEG signals of the patients with epileptic seizures were then separated from those of the control individuals using decision tree (DT) and random forest (RF) classifiers. The findings indicate that the EEG of the AAPE and impe was decreased for epileptic patients. A comparison study has been done to see how well the DT and RF classifiers work with the SG filter, AAPE and impe features, and PCA dimensionality reduction technique. When identifying patients with epilepsy and control subjects, PCA with DT and RF produced accuracies of 85% and 80%, respectively, but without the PCA, DT and RF showed accuracies of 75% and 72.5%, respectively. As a result, the EEG may be a trustworthy index for looking at short-term indicators that are sensitive to epileptic identification and classification.