Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

Gajić, Dragoljub; Djurović, Željko; Gligorijevic, Jovan; Gennaro, S. Di; Gajić, Ivana

doi:10.3389/fncom.2015.00038

Cited by 132 publications

(61 citation statements)

References 55 publications

(50 reference statements)

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“…Thus, the EEG signal pre-processing with noise removal is especially regarded as a significant step for the epileptic seizure analysis and detection [22,23]. Hence, in this work, a wavelet threshold denoising method is employed, which has a superior performance compared to the Fourier transform denoising method [19]. Specifically, the fourth-order Daubechies (db4) wavelet is selected due to its good local approximated performance for nonstationary signals [19,24].…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, the fourth-order Daubechies (db4) wavelet is selected due to its good local approximated performance for nonstationary signals [19,24]. Five frequency sub-bands of clinical interest are then obtained by using the wavelet decomposition and reconstruction: delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14)(15)(16), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and gamma . Herein, wavelet features of its good localizing properties are extracted from each sub-band in the time-frequency domain, followed by a well-known PCA algorithm of the dimensionality reduction in order to remove the irrelevant or spurious features.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, the original EEG signal (0-64 Hz) is firstly decomposed into its higher frequency part (32-64 Hz) and lower frequency part (0-32 Hz), i.e., the detail and the approximation of the signal at the first level. Then, the approximation of the first decomposition level is additionally divided into its higher (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and lower (0-16 Hz) frequency part, i.e., the detail and approximation at the second level. Thus, the wavelet threshold method can perform well in denoising nonstationary EEG signals, which is defined by [26]:…”

Section: Signal Pre-processing: Wavelet Threshold De-noisingmentioning

confidence: 99%

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

Wang

Xue

et al. 2017

Entropy

223

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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Signal Pre-processing: Wavelet Threshold De-noisingmentioning

confidence: 99%

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

Wang

Xue

et al. 2017

Entropy

223

106

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“…Hence, the focal EEG signal classification is an important research problem. Epileptic activities in EEG signals are classified by extracting features from time, frequency, time-frequency and non-linear analysis of EEG signals [10]. The EEG signals are analyzed using wavelet transform and statistical pattern recognition in order to detect epileptic seizures [11].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

Sharma

Pachori

Acharya

2015

Entropy

171

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The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student's t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.Entropy 2015, 17 5219

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“…Recent studies have proposed various machine learning-based methods for the problem of identifying focal and nonfocal iEEG signals [10][11][12][13] . Typical methods extract appropriate features that reflect the dynamics that characterize normal and epileptic brain activities.…”

Section: Introductionmentioning

confidence: 99%

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

Akter

Islam

Iimura

et al. 2020

Preprint

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Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure on-set zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning techniques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

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Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

Cited by 132 publications

References 55 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

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

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