Ensemble learning algorithm based on multi-parameters for sleep staging.
The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EE...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 8; pp. 1693 - 1708 |
|---|---|
| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Aug2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137705915&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137705915 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2019 vid: 57 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137705915 137705915 NLM31104274 137705915 10.1007/s11517-019-01978-z NLM31104274 137705915 ppf: 1693 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ensemble learning algorithm based on multi-parameters for sleep staging. aug: au: Wang, Qiangqiang Zhao, Dechun Wang, Yi Hou, Xiaorong affil: Chongqing University of Posts and Telecommunications, Chongqing, China sug: subj: Sleep Stages Algorithms Electroencephalography Methods Logistic Regression Physics Mathematics Brain Physiology Resource Databases Random Assignment Decision Trees Human ab: The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction. In feature selection, 15 feature algorithms in time domain, time-frequency domain, and nonlinearity are selected to obtain 30 feature parameters. Feature selection is very important for eliminating irrelevant and redundant features. Feature selection algorithms as Fisher score, Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS), and Fast Correlation-Based Filter Solution (FCBF) were used. The paper establishes a new ensemble learning algorithm based on stacking model. The basic layers are k-Nearest Neighbor (KNN), Random Forest (RF), Extremely Randomized Trees (ERT), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) and the second layer is a Logistic regression. Comparing classification of RF, Gradient Boosting Decision Tree (GBDT), and XGBoost, the accuracies and kappa coefficients are 96.67% and 0.96 using the proposed method. It is higher than other classification algorithms.The results show that the proposed method can accurately sleep staging using single-channel EEG and has a high ability to predict sleep staging. Graphical abstract. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|