Eigenspace Time Frequency Based Features for Accurate Seizure Detection from EEG Data

Deriche, Mohamed; Arafat, S.; Alinsaif, Sadiq; Siddiqui, Mohammed Ali

doi:10.1016/j.irbm.2019.02.002

Cited by 20 publications

(15 citation statements)

References 25 publications

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“…It is obvious by comparison that increasing the number of extracted features will improve the recognition effect. In [ 17 , 18 , 41 , 42 , 43 ], all of them have achieved good classification accuracies with a variety of features extracted, and some of them go as high as

of accuracy. However, the algorithm complexity and calculation cost will increase with the increase of the number of features extracted.…”

Section: Discussionmentioning

confidence: 99%

“…Seo and Tsuda proposed a new method that was based on the dynamic mode decomposition (DMD) in order to find a distinctive contrast between the ictal and inter-ictal patterns [ 16 ]. When compared with previous time-domain analysis, frequency-domain analysis, and time-frequency analysis [ 17 , 18 ], the entropy based nonlinear analysis method has been applied to characterize brain activities to research the pathophysiological mechanisms underlying the neurological conditions [ 19 , 20 ]. Entropy is a metrics which is different from fractal dimension, and it is a kind of index to measure the probability of new pattern in nonlinear time series.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Liu

2020

Entropy

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Epilepsy is one of the most ordinary neuropathic illnesses, and electroencephalogram (EEG) is the essential method for recording various brain rhythm activities due to its high temporal resolution. The conditional entropy of ordinal patterns (CEOP) is known to be fast and easy to implement, which can effectively measure the irregularity of the physiological signals. The present work aims to apply the CEOP to analyze the complexity characteristics of the EEG signals and recognize the epilepsy EEG signals. We discuss the parameter selection and the performance analysis of the CEOP based on the neural mass model. The CEOP is applied to the real EEG database of Bonn epilepsy for identification. The results show that the CEOP is an excellent metrics for the analysis and recognition of epileptic EEG signals. The differences of the CEOP in normal and epileptic brain states suggest that the CEOP could be a judgment tool for the diagnosis of the epileptic seizure.

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of accuracy. However, the algorithm complexity and calculation cost will increase with the increase of the number of features extracted.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Liu

2020

Entropy

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“…In DMD based epileptic seizure detection approach, sub-band powers based and DMD-HOS moments based features are introduced using the DMD spectrum. In computer-aided epileptic seizure detection and prediction studies, EEG subband powers of different frequency bands like delta (0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-60 Hz), and DMD-HOS moments are calculated using conventional Power Spectral Density [17,40]. Using the estimated DMD spectrum, similar to the classical PSD approach, Delta (P δ ), Theta (P θ ), Alpha (P α ), Beta (P β ), and Gamma (P γ )) subband powers are calculated as…”

Section: Dynamic Mode Decompositionmentioning

confidence: 99%

“…The wavelet transform and its derivative [5,14], Discrete WT (DWT) and Wavelet Packed Decomposition (WPD) [7] based approaches were successfully utilized in the seizure classification studies. Another TF analysis approaches such as The Hilbert Vibration Decomposition (HVD) [15], Variational Mode Decomposition (VMD), Hilbert transforms (HT) [16], the smoothed pseudo-Wigner-Ville distribution (SPWVD) [17], Hilbert-Huang transform (HHT) [18], short-time Fourier transform (STFT) [14,19], the analytic time-frequency flexible wavelet transform (ATFFWT) [20], The Wigner-Ville distribution (WVD) [21] have been frequently used in seizure detection and prediction studies.…”

Section: Introductionmentioning

confidence: 99%

Epileptic EEG Classification by Using Advanced Signal Decomposition Methods

Cura¹,

Akan²

2021

Epilepsy - Update on Classification, Etiologies, Instrumental Diagnosis and Treatment

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Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. In this chapter, advanced signal analysis methods such as Empirical Mode Decomposition (EMD), Ensembe (EMD), Dynamic mode decomposition (DMD), and Synchrosqueezing Transform (SST) are utilized to classify epileptic EEG signals. EMD and its derivative, EEMD are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). In this study multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then essential IMFs are selected. Finally, time-and spectral-domain, and nonlinear features are extracted from selected IMFs and classified. DMD is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. We present single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. As a third method, we use the SST representations of seizure and pre-seizure EEG data. Various features are calculated and classified by Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes (NB), Logistic Regression (LR), Boosted Trees (BT), and Subspace kNN (S-kNN) to detect pre-seizure and seizure signals. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.

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“…The entropy features such as ApEn, SampEn, and Reyni's entropy have been used in non-nested generalised exemplars classifier (NNge) classifier for focal and NF class discrimination [7]. In [8], features have been extracted using single value decomposition (SVD) from the EEG signal and used the SVM classifier to identify five classes. Apart from the above approaches, various transformation techniques such as Fourier transform (FT) [9], short time FT (STFT) [10,11], wavelet transform (WT) [12] etc.…”

Section: Introductionmentioning

confidence: 99%

Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification

Parija

Dash

Bisoi

2020

IET signal process.

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This study proposes an improved hybrid model built with empirical mode decomposition (EMD) features combined with weighted multi-kernel random vector functional link network (WMKRVFLN) where the kernel parameters are optimised with an efficient optimisation algorithm known as water cycle algorithm (WCA) for diagnosis and classification of epileptic electroencephalogram (EEG) signals. The proposed model with optimisation is known as WCA-EMD-WMKRVFLN. The tanh and wavelet kernel functions are contributing together to the effectiveness of the proposed model. The features generated from EMD in terms of intrinsic mode functions (IMFs) are modulated to find important statistical and entropy based features and these features in a reduced form are employed as inputs to the model to classify epileptic EEG signals. The presented approach is evaluated in terms of percentage correct classification accuracy (ACC), specificity and sensitivity using two datasets and is compared with different classifiers and state-of-the-art techniques. The highest accuracies of 99.69% (five-class) and 100% (three-class) achieved using the Bonn-University dataset and 99.0% ACC (two-class) achieved using the Bern-Barcelona dataset. The achieved results report that the presented approach is a promising approach for EEG signal classification and is superior to several state-of-the-art techniques and is highly comparable to many such techniques.

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Eigenspace Time Frequency Based Features for Accurate Seizure Detection from EEG Data

Cited by 20 publications

References 25 publications

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Epileptic EEG Classification by Using Advanced Signal Decomposition Methods

Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification

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