EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers

Omidvar, Mehdi; Zahedi, Abdulhamid; Bakhshi, Hamidreza

doi:10.1007/s12652-020-02837-8

Cited by 64 publications

(31 citation statements)

References 46 publications

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“…WT is a mathematical operation that can perform convolution operation on time series or signals in time domain and frequency domain at the same time. Discrete wavelet transform (DWT) is commonly used in hydrology [ 20 ]. Given the original signal x ( t ), DWT can be defined as

where k is the position index, j is the decomposition level, and ψ ∗ is the wavelet function.…”

Section: Research On Runoff Driving Factor Mining Based On Big Data Analysismentioning

confidence: 99%

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Ting

2021

Computational Intelligence and Neuroscience

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Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco-environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China.

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where k is the position index, j is the decomposition level, and ψ ∗ is the wavelet function.…”

Section: Research On Runoff Driving Factor Mining Based On Big Data Analysismentioning

confidence: 99%

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Ting

2021

Computational Intelligence and Neuroscience

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“…The term may be used in external or internal affairs that may trigger negative sentiments and associated physiological variations [3]. A cognitive-evaluative constituent was recently introduced into the stress mechanism to describe both inter and intra individual changeability in the connotation between induced stress levels and environmental events [4,5]. Underneath this notion, the mapping between stress response and induced stress levels is neither universal nor constant, as it is delimited through cognitive procedures of assessment.…”

Section: Introductionmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

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Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical e fects. Moreover, hormones, physiological e fects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using di ferent mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. A ter that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

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“…The authors of [ 4 ] claimed that their approach achieved an accuracy of 94.41% over the Bern Barcelona database. In the literature [ 5 , 6 , 7 , 8 ], we observed that only DWT has been considered for EEG signals decomposition to propose a seizure detection approach. However, the impact of using DWT over the multi-channel EEG signals/data has not been addressed.…”

Section: Introductionmentioning

confidence: 99%

“…However, the impact of using DWT over the multi-channel EEG signals/data has not been addressed. However, the approach mentioned in [ 5 ] achieved an accuracy of 98% using a random forest classifier; the approach mentioned in [ 6 ] achieved an accuracy of 99.25% using an SVM classifier; and the approach mentioned in [ 7 ] achieved an accuracy of 100% with a GA-ANN classifier. The approaches mentioned in [ 5 , 6 , 7 ] have tested over the University of Bonn single channel EEG dataset only, but they have not used CHB-MIT or other multi-channel EEG dataset to test their proposed approach.…”

Section: Introductionmentioning

confidence: 99%

“…However, the approach mentioned in [ 5 ] achieved an accuracy of 98% using a random forest classifier; the approach mentioned in [ 6 ] achieved an accuracy of 99.25% using an SVM classifier; and the approach mentioned in [ 7 ] achieved an accuracy of 100% with a GA-ANN classifier. The approaches mentioned in [ 5 , 6 , 7 ] have tested over the University of Bonn single channel EEG dataset only, but they have not used CHB-MIT or other multi-channel EEG dataset to test their proposed approach. On the other hand, the authors of [ 8 ] have considered the University of Bonn and CHB-MIT EEG datasets (i.e., single and multi channel EEG dataset) to test their approach and claimed that their approach with SVM achieved an accuracy of 89.03% over CHB-MIT and 97.67% over Bonn.…”

Section: Introductionmentioning

confidence: 99%

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DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection

et al. 2022

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Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.

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EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers

Cited by 64 publications

References 46 publications

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection

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