Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating

Hassan, Ahnaf Rashik; Siuly, Siuly; Zhang, Yanchun

doi:10.1016/j.cmpb.2016.09.008

Cited by 207 publications

(54 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As a result, the use of TQWT-based multilevel filtering enhances the discriminating ability of the conventional K-NN entropy features. In the previous studies, the TQWT framework together with K-NN entropy [9] and spectral features [10] have been proposed for epileptic EEG classification. The proposed method in this paper presents a generalized form of K-NN entropy in the TQWT framework, which has also been studied for the classification of seizure and seizure-free EEG signals [9].…”

Section: Discussionmentioning

confidence: 99%

“…They achieved classification accuracy of 97.75% with the least squares support vector machine (LS-SVM) classifier. In [10], the authors decomposed EEG signals using TQWT and extracted spectral features from the TQWT decomposed sub-band signals followed by the bagging algorithm to classify epileptic EEG signals. They achieved classification accuracy of 98.40% in classifying seizure, seizure-free and the normal classes of EEG signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

View full text Add to dashboard Cite

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high-and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Most techniques for the detection and prediction of epileptic seizures involve linear and non-linear processing of electroencephalographic (EEG) signals, which reflect the electrical activity in the brain (Acharya et al, 2012; Rana et al, 2012; Duque-Munoz et al, 2014; Hassan et al, 2016; Li et al, 2016). Some studies have achieved excellent results, with 100% accuracy for seizure detection (Alam and Bhuiyan, 2013).…”

Section: Introductionmentioning

confidence: 99%

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

et al. 2017

View full text Add to dashboard Cite

Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h−1 in the recordings from patients with epilepsy and 0.19 h−1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

show abstract

“…Electroencephalography (EEG), which contains the information of most motor-sensory activities and cognitive processes, provides a signal source for an alternative approach. EEG recordings are particularly important in the diagnosis of epilepsy [3] and in brain computer interface (BCI) [4]. Several studies have shown that using EEG analysis can reveal pain responses from various stimulations such as heat or cold [5][6][7], electrical ones [8,9] and laser [10,11].…”

Section: Introductionmentioning

confidence: 99%

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

Tripanpitak

Viriyavit

Huang

et al. 2020

Sensors

View full text Add to dashboard Cite

Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi's fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.

show abstract

Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating

Cited by 207 publications

References 38 publications

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

Contact Info

Product

Resources

About