Entropies for detection of epilepsy in EEG

Kannathal, N.; Choo, Min Lim; Acharya, U. Rajendra; Sadasivan, P.K.

doi:10.1016/j.cmpb.2005.06.012

Cited by 683 publications

(345 citation statements)

References 30 publications

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“…The measures (RSP, SSE, TSE, GSE and RSE) were then computed for each averaged PSD. In order to explore the dependence of the TSE, GSE and RSE on the nonextensive parameter q, it was modified (q = {1.25,1.5,1.75,2}) according to previous studies which analyzed EEG recordings using analogous quantifiers [19], [20]. Finally, mean values for all channels were computed to obtain a quantitative measure per subject.…”

Section: Resultsmentioning

confidence: 99%

“…PSDs were averaged for each channel and subject to compute the mean power spectrum. Then, the RSP was calculated in the classical frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta1 (13)(14)(15)(16)(17)(18)(19), beta2 (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30-64 Hz). If we denote the power at each frequency band as P j , j = {delta, theta, alpha, beta1, beta2, gamma}, then, the RSP can be represented as a probability distribution,…”

Section: B Definition Of the Spectral Entropiesmentioning

confidence: 99%

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Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Poza

Hornero

Abásolo

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The aim of this study was to explore the ability of several spectral entropies to discriminate between spontaneous magnetoencephalographic (MEG) oscillations from 20 Alzheimer's disease (AD) patients and 21 controls. Hence, the relative spectral power (RSP) in classical frequency bands was calculated from the averaged power spectral density. Given the fact that the RSP can be viewed as a probability distribution function, the Shannon spectral entropy, Tsallis spectral entropy, generalized escort-Tsallis spectral entropy and Rényi spectral entropy were calculated from the RSP. Significant differences for each parameter were assessed with MannWhitney U test, whereas classification performance was studied using binary logistic regression. Results revealed an increase in the RSP of control subjects at beta and gamma bands, while AD patients showed an increase in the RSP values at delta and theta bands. Entropic quantifiers obtained statistically significant lower values for AD patients than for controls. This issue suggests a significant decrease in irregularity of AD patients' MEG activity.

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Section: Resultsmentioning

confidence: 99%

Section: B Definition Of the Spectral Entropiesmentioning

confidence: 99%

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Poza

Hornero

Abásolo

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Initially a quantity describing the degree of disorder in a thermodynamic system, entropy is later widely used to assess the uncertainty of a system [14]. From the perspective of information theory, entropy is the amount of information contained in a generalized probability distribution.…”

Section: Entropy-based Feature Extractionmentioning

confidence: 99%

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Zhang

Min

2017

Applied Sciences

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Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.

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“…In this sub-section some recent studies are reviewed and compared to our study. In 2005, Kannathal et al [22] used entropy estimators (spectral entropies, Renyi's entropy, state space reconstruction, Kolmogorov-Sinai entropy, and approximate entropy) in order to detect epilepsy in EEG. The EEG data used for the study was obtained from Bonn University.…”

Section: Eeg Application To Epilepsymentioning

confidence: 99%

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Zabihi

Kıranyaz

İnce

et al. 2013

IET Intelligent Signal Processing Conference 2013 (ISP 2013)

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Over recent years, due to the increase in the epileptic patient population, issues of diagnosing and treatment of epilepsy have become more and more prominent and much research has been done in this field in consequence. However, there are still many gaps and lack of knowledge in interpreting Electroencephalograph (EEG) signals in order to solve the problem.Particular problems in this area include difficulties in detecting the seizure events (due to the different seizure types and their variability from patient to patient or even in an individual over time), and dealing with long-term EEG recordings, which is an onerous and time consuming task for electroencephalographers.The thesis discusses the two problem areas using EEG data from four subjects with overall 21 hours of recording from the CHB-MIT scalp benchmark EEG dataset. We propose a patient specific seizure detection technique, which selects the optimal feature subsets, and train a dedicated classifier for each patient in order to maximize the classification performance. To exploit the characteristics of a patient's EEG pattern as much as possible, we used a large set of features in the proposed framework, namely time domain, frequency domain, time-frequency domain and nonlinear features, and selected the most crucial features among them by using Conditional Mutual Information Maximization (CMIM) technique. We further performed extensive comparative evaluations against 6 other feature selection methods to demonstrate the superiority of the CMIM.Support Vector Machine (SVM) with the linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set, i.e. an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average sensitivity and specificity rates of 93.78% and 99.05% are obtained using CMIM, respectively. PREFACEThis master thesis was carried out in the MUVIS group at the signal processing department of Tampere University of Technology. It was funded by the Academy of Finland.I wish to express my sincere gratitude to Professor Serkan Kiranyaz, whose inspiring guidance and inexhaustible patience have helped me cultivate a systematic approach towards the research. I am indebted to him for the freedom he gave me in implementing my ideas. I have also acquired valuable insights through his instruction in academic approach.I am very much grateful to Professor Turker Ince for his warm support and his valuable suggestions. Besides, I would also like to thank Professor Moncef Gabbouj for providing me with the opportunity for the thesis work. He gave me a lot of trust and flexibility in the project.I have been extremely fortunate in having valuable friends who have made a homely atmosphere for me during my stay in Tampere

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Entropies for detection of epilepsy in EEG

Cited by 683 publications

References 30 publications

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

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