A New Framework Based on Recurrence Quantification Analysis for Epileptic Seizure Detection

Niknazar, Mohammad; Mousavi, Seyed Reza; Vahdat, Bijan Vosoughi; Sayyah, Mohammad

doi:10.1109/jbhi.2013.2255132

Cited by 85 publications

(35 citation statements)

References 28 publications

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“…Tzalla et al [3] Time-frequency analysis, artificial neural network 97.73% Guo et al [39] Multiwavelet transform, MLPNN 98.27% Rivero et al [42] Time frequency analysis, KNN 98.40% Kaleem et al [40] Variation of empirical mode decomposition 98.20% Kai Fu et al [10] HMS analysis, SVM 98.80% Niknazar M et al [43] Wavelet transform, RQA, ECOC 98.67% Musa Peker et al [41] Dual-tree complex wavelet transform, complex-valued neural networks 99.15% Jaiswal et al [44] Local neighbor Descriptive pattern, artificial neural network 98.72% This work DWT, multi-domain feature extraction and nonlinear analysis 99.25%…”

Section: S-fnozmentioning

confidence: 99%

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

216

106

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Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

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Section: S-fnozmentioning

confidence: 99%

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

216

106

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“…Firstly, the wavelet transform of EEG signals is estimated, and maximum, minimum, and standard deviation of the absolute values of the wavelet coefficients in each sub-band are extracted as features. [42]. The RQA is well-suited for non-linear data analysis.…”

Section: Wavelet-domain Seizure Detectionmentioning

confidence: 99%

EEG seizure detection and prediction algorithms: a survey

Alshebeili

Alshawi

Ahmad

et al. 2014

EURASIP J. Adv. Signal Process.

227

115

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Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

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“…For input to the algorithms, the embedding dimension (m) was 10, the time delay was 2, and the threshold for the recurrence plot (tau) was 30. For detailed discussions of how these values may be determined, see [41,43,44].…”

Section: Eeg Analysis Methodsmentioning

confidence: 99%

“…In principle, RQA is capable of detecting significant state changes in a dynamical system [36,37,41], which suggests that it may be appropriate for detecting developmental changes in brain function that are associated with chronic neurological and mental dysfunction. Recurrence quantitative analysis has been used for early seizure detection by distinguishing ictal and inter-ictal entropy states [42][43][44] and recently for differentiating children with ASD from typically developing children [45].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear EEG biomarker profiles for autism and absence epilepsy

Bosl

Loddenkemper

Nelson

2017

Neuropsychiatr Electrophysiol

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Background: Although autism and epilepsy are considered to be different disorders, epileptiform EEG activity is common in people with autism even when overt seizures are not present. The relatively high comorbidity between autism and all epilepsy syndromes suggests the possibility of common underlying neurophysiological mechanisms. Although many different epilepsies may be comorbid with autism, absence epilepsy is a generalized epilepsy syndrome with seizures that appear as staring spells, with no motor signs and no focal lesions, making it more difficult to diagnose. Application of nonlinear methods for EEG signal analysis may enable characterization of brain activity that can help to delineate neurophysiological commonalities and differences between autism and epilepsy. Multiscale entropy and recurrence quantitative analysis (RQA) were computed from EEG signals derived from children with autism or absence epilepsy and compared with the goal of finding significant and potentially clinically useful biomarkers neurophysiological differences between these two childhood disorders. Methods: Multiscale entropy and a multiscale version of RQA were computed from EEG data obtained from 92 children were collected in two different settings at Boston Children's Hospital. Short segments of alert resting state EEG were selected for analysis. A complexity index derived from entropy and RQA methods was computed from each of 19 standard EEG channels for all subjects using publicly available software. Statistical comparisons were made between the groups. Machine learning classifiers were also used to determine which derived features were most significantly different among the groups, and to determine classification specificity and sensitivity. Results: Significant differences were found between absence, autism, and control groups in a number of different scalp locations and the values of complexity index. Autism values appeared to be intermediate between epilepsy and control in many locations, and differences between controls and absence patients were more widely distributed across scalp locations. Classification algorithms were able to distinguish absence epilepsy and autism cases from controls with high (>95%) accuracy. Importantly, two independent control groups, although they were derived from different settings and with different equipment were statistically indistinguishable.

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A New Framework Based on Recurrence Quantification Analysis for Epileptic Seizure Detection

Cited by 85 publications

References 28 publications

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

EEG seizure detection and prediction algorithms: a survey

Nonlinear EEG biomarker profiles for autism and absence epilepsy

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