Identification of Epileptic Seizures from Scalp EEG Signals Based on TQWT

Bhattacharyya, Abhijit; Singh, Lokesh; Pachori, Ram Bilas

doi:10.1007/978-981-13-0923-6_18

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…The EEG signals have a chaotic and nonlinear nature. Related works showed that nonlinear feature extraction methods play a significant role in improving the functionality and accuracy of the epileptic seizure diagnosis using EEG signals [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. The most important nonlinear feature extraction methods from EEG signals include various types of entropies [83], FDs [84], graphs [85], the largest Lyapunov exponent (LLE) [86], and correlation coefficients (CC) [87].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

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Section: Introductionmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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“…e motivation stems from the successful deployment of TQWT in other biomedical signal processing applications such as detecting epileptic seizures [34,[43][44][45][46][47][48][49] and alcoholism [50] by EEG signals, detecting coronary artery disease [51] by heart rate variability (HRV) signals, heart valve [52,53] and septal defects disorders [54,55], aortic and mitral disorders [56,57] by cardiac sound signals [58], detecting hand movements [59] and amyotrophic lateral sclerosis (ALS) disorder [60] by electromyogram (EMG) signals, and sleep apnea [61] by electrocardiogram (ECG) signals that indicate the ability of TQWT in biosignal processing application.…”

Section: Contribution Eeg Signal Is Nonstationary and Complexmentioning

confidence: 99%

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

Sadiq

Akbari

Rehman

et al. 2021

Journal of Healthcare Engineering

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For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

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“…EEG signals have been employed in the diagnosis of several neurological illnesses by extracting unique features and classifying them with different classifiers in automated detection systems. Neurophysiological disorders such as alcoholism [12], dementia [13,14], epileptic seizure [15], schizophrenia [16,17], Parkinson's disease [18,19], and depressive disorder [20,21] are some of the areas where EEG signals are employed in automatic detection. The EEG signals of ADHD children are different from that normal child in terms of complex randomness, amplitude, and frequency.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of ADHD from EEG Signals Using Tunable Q-Factor Wavelet Transform

et al. 2022

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The automatic identification of Attention Deficit Hyperactivity Disorder (ADHD) is essential for developing ADHD diagnosis tools that assist healthcare professionals. Recently, there has been a lot of interest in ADHD detection from EEG signals because it seemed to be a rapid method for identifying and treating this disorder. This paper proposes a technique for detecting ADHD from EEG signals with the nonlinear features extracted using tunable Q-wavelet transform (TQWT). The 16 channels of EEG signal data are decomposed into the optimal amount of time-frequency sub-bands using the TQWT filter banks. The unique feature vectors are evaluated using Katz and Higuchi nonlinear fractal dimension methods at each decomposed levels. An Artificial Neural Network classifier with a 10-fold cross-validation method is found to be an effective classifier for discriminating ADHD and normal subjects. Different performance metrics reveal that the proposed technique could effectively classify the ADHD and normal subjects with the highest accuracy. The statistical analysis showed that the Katz and Higuchi nonlinear feature estimation methods provide potential features that can be classified with high accuracy, sensitivity, and specificity and is suitable for automatic detection of ADHD. The proposed system is capable of accurately distinguishing between ADHD and non-ADHD subjects with a maximum accuracy of 100%.

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Identification of Epileptic Seizures from Scalp EEG Signals Based on TQWT

Cited by 13 publications

References 23 publications

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

Detection and Classification of ADHD from EEG Signals Using Tunable Q-Factor Wavelet Transform

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