Channel selection for automatic seizure detection

Duun‐Henriksen, Jonas; Kjær, Troels W.; Madsen, Rasmus Elsborg; Remvig, Line Sofie; Thomsen, Carsten; Sørensen, Helge Bjarup Dissing

doi:10.1016/j.clinph.2011.06.001

Cited by 92 publications

(59 citation statements)

References 18 publications

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“…It was found that the best channel selection method is based upon maximum variance during seizure, which led to a seizure detection sensitivity of 96 % and false detection rate of 0.14/h using three channels. The work of Duun-Henriksen et al [12] falls in the class of filtering techniques, as it used an independent measure for channel selection, which is the variance. The authors did not use a search strategy.…”

Section: Selection Based On Entropymentioning

confidence: 99%

“…In certain applications, there is a need to select some of these channels for EEG seizure detection/prediction because the computational load required for a seizure detection/prediction algorithm increases as a function of the number of channels [12]. Reducing the number of channels is of an utmost importance, for example, in the development of portable medical support systems for epilepsy patients, as reducing algorithmic computational complexity will lead to faster real-time response and lower power consumption to maintain longer time for operation.…”

Section: Channel Selection For Seizure Detection/ Predictionmentioning

confidence: 99%

“…This subsection presents four trends for channel selection for EEG seizure detection and prediction, with a common thread that they are all based on signal statistics. Duun-Henriksen et al [12] investigated different channel selection schemes based on different statistical criteria as follows:…”

Section: Filtering Techniquesmentioning

confidence: 99%

“…In the method developed by Duun-Henriksen et al [12], after channel selection, a feature extraction process utilizing wavelet transform is performed on the selected channels. A support vector machine (SVM) was used as a classifier for seizure detection, with a non-linear radial basis kernel having a regularization term of 0.5 and a cost factor varying between 0.05 and 0.5.…”

Section: Selection Based On Entropymentioning

confidence: 99%

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A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

349

181

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Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

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Section: Selection Based On Entropymentioning

confidence: 99%

Section: Channel Selection For Seizure Detection/ Predictionmentioning

confidence: 99%

Section: Filtering Techniquesmentioning

confidence: 99%

Section: Selection Based On Entropymentioning

confidence: 99%

See 2 more Smart Citations

A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

349

181

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“…In this paper, we will focus in particular on childhood absence epilepsy (CAE), which is a common idiopathic generalized epilepsy syndrome [1,2]. The pediatric neurologist's main objective is to neutralize all absence seizures through appropriate medication, while inducing a few side effects.…”

Section: Introductionmentioning

confidence: 99%

Differentiating Interictal and Ictal States in Childhood Absence Epilepsy through Permutation Rényi Entropy

Mammone

Duun‐Henriksen²,

Kjær

et al. 2015

Entropy

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Permutation entropy (PE) has been widely exploited to measure the complexity of the electroencephalogram (EEG), especially when complexity is linked to diagnostic information embedded in the EEG. Recently, the authors proposed a spatial-temporal analysis of the EEG recordings of absence epilepsy patients based on PE. The goal here is to improve the ability of PE in discriminating interictal states from ictal states in absence seizure EEG. For this purpose, a parametrical definition of permutation entropy is introduced here in the field of epileptic EEG analysis: the permutation Rényi entropy (PEr). PEr has been extensively tested against PE by tuning the involved parameters (order, delay time and alpha). The achieved results demonstrate that PEr outperforms PE, as there is a statistically-significant, wider gap between the PEr levels during the interictal states and PEr levels observed in the ictal states compared to PE. PEr also outperformed PE as the input to a classifier aimed at discriminating interictal from ictal states.

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A forecasting tool for prediction of epileptic seizures using a machine learning approach

Asharindavida

Hossain

Thacham

et al. 2018

Concurrency and Computation

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ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases.In the window-based analysis of low-frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20-30 minutes prior to the actual occurrence of seizures. KEYWORDS Electrocardiogram (ECG), Electroensephalogram (EEG), Empirical Mode Decomposition (EMD), epileptic seizure, machine learning 1 Concurrency Computat Pract Exper. 2020;32:e5111.wileyonlinelibrary.com/journal/cpe

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Channel selection for automatic seizure detection

Cited by 92 publications

References 18 publications

A review of channel selection algorithms for EEG signal processing

A review of channel selection algorithms for EEG signal processing

Differentiating Interictal and Ictal States in Childhood Absence Epilepsy through Permutation Rényi Entropy

A forecasting tool for prediction of epileptic seizures using a machine learning approach

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