Detection of Epileptic Seizure EEG Signal Using Multiscale Entropies and Complete Ensemble Empirical Mode Decomposition

2020 ◽  
Vol 116 (1) ◽  
pp. 845-864
Author(s):  
Gurwinder Singh ◽  
Manpreet Kaur ◽  
Birmohan Singh
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizure ◽  
Ensemble Empirical Mode Decomposition ◽  
Eeg Signal ◽  
Mode Decomposition

scholarly journals Epileptic Seizures Detection based on Empirical Mode Decomposition and Hilbert-Huang transform of EEG Signal

2015 ◽  
Vol 1 (2) ◽  
pp. 295
Author(s):  
Mokhtar Mohammadi ◽  
Aso M. Darwesh
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizure ◽  
Spectral Energy ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. We present a new method for classification of ictal and seizure-free intracranial EEG recordings. The proposed method uses the application of multivariate empirical mode decomposition (MEMD) algorithm combines with the Hilbert transform as the Hilbert-Huang transform (HHT) and analyzing spectral energy of the intrinsic mode function of the signal. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signals time-frequency-energy distributions. Classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. The algorithm was tested in 6 intracranial channels EEG records acquired in 9 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.

scholarly journals Eye Blink Artefact Removal of Single Frontal EEG Channel Algorithm using Ensemble Empirical Mode Decomposition and Outlier Detection

2021 ◽  
Vol 17 (6) ◽  
pp. 731-741
Author(s):  
Mohd Nurul Al Hafiz Sha'abani ◽  
Norfaiza Fuad ◽  
Norezmi Jamal
Keyword(s):  
Outlier Detection ◽  
Empirical Mode Decomposition ◽  
Single Channel ◽  
Wiener Filter ◽  
Ensemble Empirical Mode Decomposition ◽  
Detection Technique ◽  
Eeg Signal ◽  
Eye Blink ◽  
Mode Decomposition ◽  
Artefact Removal

Recently, the emergence of various applications to use EEG has evolved the EEG device to become wearable with fewer electrodes. Unfortunately, the process of removing artefact becomes challenging since the conventional method requires an additional artefact reference channel or multichannel recording to be working. By focusing on frontal EEG channel recording, this paper proposed an alternative single-channel eye blink artefact removal method based on the ensemble empirical mode decomposition and outlier detection technique. The method removes the segment of the potential eyeblinks artefact on the residual of a pre-determined level of decomposition. An outlier detection technique is introduced to identify the peak of the eyeblink based on the extreme value of the residual signal. The results showed that the corrected EEG signal achieved high correlation, low RMSE and have small differences in PSD when compared to the reference clean EEG. Comparing with an adaptive Wiener filter technique, the corrected EEG signal by the proposed method had better signal-to-artefact ratio.

scholarly journals Performance Evaluation of Empirical Mode Decomposition Algorithms for Mental Task Classification

2016 ◽  
Author(s):  
Akshansh Gupta ◽  
Dhirendra Kumar ◽  
Anirban Chakraborti ◽  
Kiran Sharma
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Compact Representation ◽  
Support Vector ◽  
Second Phase ◽  
Eeg Signal ◽  
Mental Task ◽  
Mode Decomposition ◽  
Task Classification ◽  
Mental Tasks

AbstractBrain Computer Interface (BCI), a direct pathway between the human brain and computer, is one of the most pragmatic applications of EEG signal. The electroencephalograph (EEG) signal is one of the monitoring techniques to observe brain functionality. Mental Task Classification (MTC) based on EEG signals is a demanding BCI. Success of BCI system depends on the efficient analysis of these signals. Empirical Mode Decomposition (EMD) is a filter based heuristic technique which is utilized to analyze EEG signal in recent past. There are several variants of EMD algorithms which have their own merits and demerits. In this paper, we have explored three variants of EMD algorithms named Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) on EEG data for MTC-based BCI. Features are extracted from EEG signal in two phases; in the first phase, the signal is decomposed into different oscillatory functions with the help of different EMD algorithms and eight different parameters (features) are calculated for each function for compact representation in the second phase. These features are fed into Support Vector Machine (SVM) classifier to classify the different mental tasks. We have formulated two different types of MTC, the first one is binary and second one is multi-MTC. The proposed work outperforms the existing work for both binary and multi mental tasks classification.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

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