Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures

Aung, Si Thu; Wongsawat, Yodchanan

doi:10.3389/fphys.2020.00607

Cited by 14 publications

(11 citation statements)

References 46 publications

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“…According to previous works (Aung & Wongsawat, 2020), there are some limitations on multivariate time series analysis, and therefore, MM-mDistEn is proposed to overcome these limitations. First, the phase-space reconstruction and estimation of the probability density between vectors provide hidden complex information.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed method, MM-mDistEn, which is implemented based on distribution entropy, adds two more threshold parameters, 'r' and 'n', to existing parameters. r is set by multiplying the standard deviation of all data values by 0.2, and n is set to 2 (Aung & Wongsawat, 2020). For a given multivariate coarse-grained time series,…”

Section: Multivariate Multiscale Modified-distribution Entropymentioning

confidence: 99%

“…Next, the distance matrix, D s ij is calculated using the parameters r and n, where r is the tolerance number and n is the order of the function, and both parameters are applied in calculating the proposed entropy. A large r and n can lead to noise influence, whereas a small r and n can cause information lose; and therefore, the parameter r is equal to the standard deviation of the series multiplied by 0.2, and n is set to 2 (Aung & Wongsawat, 2020;Chen et al, 2007). When the ePDF is calculated, another parameter value called the bin number, B, is needed, and B is set to 64 for this estimation (Li et al, 2016).…”

Section: Parameter Selectionmentioning

confidence: 99%

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Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Aung¹,

Wongsawat²

2021

PeerJ Computer Science

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Epilepsy is a common neurological disease that affects a wide range of the world population and is not limited by age. Moreover, seizures can occur anytime and anywhere because of the sudden abnormal discharge of brain neurons, leading to malfunction. The seizures of approximately 30% of epilepsy patients cannot be treated with medicines or surgery; hence these patients would benefit from a seizure prediction system to live normal lives. Thus, a system that can predict a seizure before its onset could improve not only these patients’ social lives but also their safety. Numerous seizure prediction methods have already been proposed, but the performance measures of these methods are still inadequate for a complete prediction system. Here, a seizure prediction system is proposed by exploring the advantages of multivariate entropy, which can reflect the complexity of multivariate time series over multiple scales (frequencies), called multivariate multiscale modified-distribution entropy (MM-mDistEn), with an artificial neural network (ANN). The phase-space reconstruction and estimation of the probability density between vectors provide hidden complex information. The multivariate time series property of MM-mDistEn provides more understandable information within the multichannel data and makes it possible to predict of epilepsy. Moreover, the proposed method was tested with two different analyses: simulation data analysis proves that the proposed method has strong consistency over the different parameter selections, and the results from experimental data analysis showed that the proposed entropy combined with an ANN obtains performance measures of 98.66% accuracy, 91.82% sensitivity, 99.11% specificity, and 0.84 area under the curve (AUC) value. In addition, the seizure alarm system was applied as a postprocessing step for prediction purposes, and a false alarm rate of 0.014 per hour and an average prediction time of 26.73 min before seizure onset were achieved by the proposed method. Thus, the proposed entropy as a feature extraction method combined with an ANN can predict the ictal state of epilepsy, and the results show great potential for all epilepsy patients.

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Section: Discussionmentioning

confidence: 99%

Section: Multivariate Multiscale Modified-distribution Entropymentioning

confidence: 99%

Section: Parameter Selectionmentioning

confidence: 99%

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Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Aung¹,

Wongsawat²

2021

PeerJ Computer Science

Self Cite

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“…The authors of [ 16 ] gave two protocols for analyzing the entropy of the EEG, one using a single analysis window, but with each window having different lengths, and the other using multiple windows, each of which can differ in statistical content. In [ 17 ], a modified distribution entropy (mDistEn) for epilepsy detection was proposed. mDistEn corresponds to a higher area under the curve (AUC) value compared to fuzzy entropy and distribution entropy and yields a 92% classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Study on Seizure Detection of EEG Signals Represented in 2D

Xiong

Wang

Zhang

et al. 2021

Sensors

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A seizure is a neurological disorder caused by abnormal neuronal discharges in the brain, which severely reduces the quality of life of patients and often endangers their lives. Automatic seizure detection is an important research area in the treatment of seizure and is a prerequisite for seizure intervention. Deep learning has been widely used for automatic detection of seizures, and many related research works decomposed the electroencephalogram (EEG) raw signal with a time window to obtain EEG signal slices, then performed feature extraction on the slices, and represented the obtained features as input data for neural networks. There are various methods for EEG signal decomposition, feature extraction, and representation, and most of the studies have been based on fixed hardware resources for the design of the scheme, which reduces the adaptability of the scheme in different application scenarios and makes it difficult to optimize the algorithms in the scheme. To address the above issues, this paper proposes a deep learning-based model for seizure detection, mainly characterized by the two-dimensional representation of EEG features and the scalability of neural networks. The model modularizes the main steps of seizure detection and improves the adaptability of the model to different hardware resource constraints, in order to increase the convenience of the algorithm optimization or the replacement of each module. The proposed model consists of five parts, and the model was tested using two epilepsy datasets separately. The experimental results showed that the proposed model has strong generality and good classification accuracy for seizure detection.

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“…Estimation of signal complexity using entropy and fractal is becoming a popular method for feature extraction. An entropy-based feature extraction method in detecting EEG seizures has been reported in [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

2021

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Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the main tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal which includes normal, inter-ictal and ictal is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). Simulations were carried out on varying window length, overlap and FFT points to find the highest detection accuracy. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for the detection and prediction of seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients.

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Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures

Cited by 14 publications

References 46 publications

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

A Study on Seizure Detection of EEG Signals Represented in 2D

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

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