Epileptic seizure detection using non linear analysis of EEG

Vijith, V. S.; Jacob, Jisu Elsa; Iype, Thomas; Gopakumar, K.; Yohannan, Doris George

doi:10.1109/inventive.2016.7830193

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…A significant number of methods have been developed to analyze nonlinear signals and characterize a biological system’s dynamic behavior. Nonlinear approaches, such as effective correlation dimension, correlation density, Hurst exponent, Lyapunov exponents, approximate entropy (ApEn), and sampling entropy (SampEn), have been applied to electrophysiological signals such as ECG and EEG [ 7 , 8 , 9 ]. This study focuses on ApEn and SampEn because numerous studies that use these estimators have emerged in recent years, both at the research-theoretical level and at the level of technological development [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Espinosa

Talero

Weinstein

2020

Entropy

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Electrocardiography (ECG) and electroencephalography (EEG) signals provide clinical information relevant to determine a patient’s health status. The nonlinear analysis of ECG and EEG signals allows for discovering characteristics that could not be found with traditional methods based on amplitude and frequency. Approximate entropy (ApEn) and sampling entropy (SampEn) are nonlinear data analysis algorithms that measure the data’s regularity, and these are used to classify different electrophysiological signals as normal or pathological. Entropy calculation requires setting the parameters r (tolerance threshold), m (immersion dimension), and τ (time delay), with the last one being related to how the time series is downsampled. In this study, we showed the dependence of ApEn and SampEn on different values of τ, for ECG and EEG signals with different sampling frequencies (Fs), extracted from a digital repository. We considered four values of Fs (128, 256, 384, and 512 Hz for the ECG signals, and 160, 320, 480, and 640 Hz for the EEG signals) and five values of τ (from 1 to 5). We performed parametric and nonparametric statistical tests to confirm that the groups of normal and pathological ECG and EEG signals were significantly different (p < 0.05) for each F and τ value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with Fs and τ. For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of Fs, and τ larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of Fs and τ larger than 1. Therefore, adjusting τ may be convenient for signals that were acquired with different Fs to ensure a reliable clinical classification. Furthermore, it is useful to set τ to values larger than 1 to reduce the computational cost.

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Section: Introductionmentioning

confidence: 99%

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Espinosa

Talero

Weinstein

2020

Entropy

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“…S. Vijith et al [9] Approximate entropy SVM Bonn 89%, 91% The proposed approach Hypothesis Features Random Forest Bonn 94.0% Orhan et al [24] Wavelet coefficients ANN Bonn 95.6% Acharya et al [5] Non-linear SVM and GMM Bonn 96.1% Krishnakumar and Thanushkodi [13] STFT and non-linear ANN Bonn 96.2% Acharya et al [7] Non-linear SVM, KNN, FC, ANN, DT, GMM, and NBC Bonn 98.1%…”

Section: Ref Features Classification Dataset Resultsmentioning

confidence: 99%

“…Acharya et al [5] used a correlation dimension, a fractal dimension, the Hurst exponent, the largest Lyapunov exponent, and approximate entropy with Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers. Similarly, Acharya et al [7], Yang et al [8], S. Vijith et al [9], Thilagaraj et al [10], and Li et al [11] used similar or other features and classifiers and had similar or slightly more accurate results. Overall, the use of nonlinear features for epilepsy detection and classification resulted in an output with an accuracy of up to 98% using features that required time with a complexity of O(n log n) or higher, where n is the length of the acquired signal.…”

Section: Nonlinear Feature-based Epilepsy Seizure Detectionmentioning

confidence: 97%

Statistical-Hypothesis-Aided Tests for Epilepsy Classification

et al. 2019

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In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for the purpose of the proposed approach were used. From each test, various output scalars were extracted and used as features in the proposed detection and classification task. Experiments that were conducted on the basis of a Bonn University dataset showed that the proposed approach had very accurate results ( 98.4 % ) in the detection task and outperformed state-of-the-art methods in a similar task on the same dataset. The proposed approach also had accurate results ( 94.0 % ) in the classification task, but it did not outperform state-of-the-art methods in a similar task on the same dataset. However, the proposed approach had less time complexity in comparison with those methods that achieved better results.

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“…Fifteen studies had the investigation of group differences as their only outcome, while diagnostic accuracy indices were the only outcome for two studies [30,31]. Three studies examined both outcomes, with diagnostic accuracy indices computed for those predictors that were significant in grouplevel analyses [27,32,33].…”

Section: Studies In Epilepsy Cohortsmentioning

confidence: 99%

“…Early studies tended to describe the study population in terms of seizure types, with four studies including people with nonlesional epilepsy characterized by partial seizures [34], or a mixed sample with generalized or partial seizures [27,35,36]. The remain- der of the studies categorized their population according to epilepsy types or epilepsy syndromes, including nine studies on idiopathic generalized epilepsy (IGE/PGE); [24, 28,30,32,37,38,39,40,41], two studies including both a sample with IGE and a sample with non-lesional focal epilepsy [42,43], two studies studying IGE and non-lesional temporal lobe epilepsy (TLE) as a single sample [31,33], two studies focusing on non-lesional TLE only [22,44], and one study focusing on cryptogenic focal epilepsy only [45].…”

Section: Studies In Epilepsy Cohortsmentioning

confidence: 99%

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Faiman

Smith

Hodsoll

et al. 2021

Epilepsy & Behavior

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Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

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Epileptic seizure detection using non linear analysis of EEG

Cited by 20 publications

References 8 publications

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Statistical-Hypothesis-Aided Tests for Epilepsy Classification

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

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