EEG channels reduction using PCA to increase XGBoost’s accuracy for stroke detection

Fitriah, Nilam; Wijaya, Sastra Kusuma; Fanany, Mohamad Ivan; Badri, Cholid; Rezal, Muhammad

doi:10.1063/1.4991232

Cited by 16 publications

(4 citation statements)

References 6 publications

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“…Liu and Qiao [45] proposed a prediction method based on clustering and XGboost algorithms for the incidence of heart disease, which shows that the proposed method was feasible and effective. Fitriah et al [46] proposed an algorithm combining PCA preprocessing with XGBoost classification to diagnose stroke patients in Indonesia, and the accuracy of diagnosis was increased by using fewer electrodes. PCA could reduce dimensionality and computation cost without decreasing classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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“…There are a large number of studies on EEG channel reduction using data compression methods like PCA (Principal Component Analysis), ICA (Independent Component Analysis) etc. [56][57][58]. However, most of the algorithms have huge computational overhead and are time-consuming.…”

Section: Motor Imagery Data Classifier With Ocnnmentioning

confidence: 99%

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

Ghosh,

Vigneswaran,

Rohan

et al. 2024

Preprint

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In this paper, we propose two models of oscillatory neural networks - the Deep Oscillatory Neural Network (DONN) and a convolutional variation of it named Oscillatory Convolutional Neural Network (OCNN) – and apply the models to a variety of problems involving the classification and prediction of Electroencephalogram (EEG) signals. Deep neural networks applied to signal processing problems will have to incorporate various architectural features to remember the history of the input signals e.g., loops between the layers, “gated” neurons, and tapped delay lines. But real brains have rich dynamics expressed in terms of frequency bands like alpha, beta, gamma, delta, etc. To incorporate this aspect of brain dynamics in a Recurrent Neural Network (RNN) we propose to use nonlinear oscillators as dynamic neuron models in the hidden layers. The two oscillatory deep neural networks proposed are applied to the following EEG classification and prediction problems: Prediction of nearby EEG channels, classification of single-channel EEG data (healthy vs. epileptic, different stages of sleep stage classification), and multi-channel EEG data (Epileptic vs. Normal, Left vs. right-hand Motor imagery movement, and healthy vs. Claustrophobic EEG).

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“…Tree boosting can be defined as a learning method designed to improve the classification of weaker classifiers through repeatedly adding new decision trees to the ensembles. It has been shown by previous research works [6], that in cases when the dimentionality of the feature vector is very high, applying PCA before employing XGBoost can enhance both speed and performance of the XGBoost method. After applying the feature extraction method detailed in Sec.…”

Section: Classificationmentioning

confidence: 99%

DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization

Alavi

2020

Preprint

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The key method for determining the structure of a protein to date is X-ray crystallography, which is a very expensive technique that suffers from high attrition rate. On the contrary, a sequence-based predictor that is capable of accurately determining protein crystallization property, would not only overcome such limitations, but also would reduce the trial-and-error settings required to perform crystallization. In this work, to predict protein crystallizability, we have developed a novel sequence-based hybrid method that employs two separate, yet fully automated, concepts for extracting features from protein sequences. Specifically, we use a deep convolutional neural network on a publicly available dataset to extract descriptive features directly from the sequences, then fuse such feature with structural-and-physio-chemical driven features (such as amino-acid composition or AAIndex-based physicochemical properties). Dimentionality reduction is then performed on the resulting features and the output vectors are applied to train optimized gradient boosting machine (XGBoostt). We evaluate our method through three publicly available test sets, and show that our proposed DHS-Crystallize algorithm outperforms state-of-the-art methods, and achieves higher performance compared to using DCNN-deriven features, or structural-and-physio-chemical driven features alone.

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EEG channels reduction using PCA to increase XGBoost’s accuracy for stroke detection

Cited by 16 publications

References 6 publications

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization

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