Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

Wu, Jihuai; Zhou, Tengfei; Li, Taiyong

doi:10.3390/e22020140

Cited by 79 publications

(39 citation statements)

References 92 publications

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“…To cope with that, a "decomposition and ensemble" framework was widely introduced into time series forecasting. The first step of the "decomposition and ensemble" framework is to decompose the complex raw time series into a group of relatively simple components, then a single predictor is introduced to predict each component independently, and finally these predicted values of all components are assembled as the final predicted results [24][25][26][27]. This idea has also been introduced into the field of crude oil price prediction.…”

Section: Introductionmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.

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Section: Introductionmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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“…However, for CD‐E cases, the proposed model has best performance compared with other methods where the state‐of‐the‐art methods yield ranged by 98 and 99.33% accuracy. For AB‐CD‐E case, Al‐Hadeethi et al [7 ] and Wu et al [10 ] with 99% accuracy stay behind Jiang et al [4 ]. The proposed model yields best performance in AB‐CD‐E case.…”

Section: Resultsmentioning

confidence: 99%

“…Average accuracy of 98.15% was obtained for the cases. Wu et al [10 ] proposed the complementary ensemble empirical mode decomposition‐extreme gradient boosting method for the detection of epileptic seizures. Feature extraction was made from raw signals and decomposed components.…”

Section: Introductionmentioning

confidence: 99%

Analysis of EEG signal for seizure detection based on WPT

Arı

2020

Electron. lett.

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Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer‐based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k‐nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN. The proposed method was compared with other methods in the literature using the same dataset. The comparison results provide the superiority of the proposed method.

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“…Based on this, a different kind of decomposition, named complementary ensemble empirical mode decomposition (CEEMD), can be considered. In this context, the CEEMD, an extension of the EEMD method, has been applied in several fields of knowledge, such as crude oil price forecasting [37], short-term photovoltaic power generation forecasting [38], and detecting epileptic seizures in electroencephalogram [39].…”

Section: Related Workmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

Cited by 79 publications

References 92 publications

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Analysis of EEG signal for seizure detection based on WPT

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

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