Random ensemble learning for EEG classification.
Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of fea...
| Publicado en: | Artificial Intelligence in Medicine Vol. 84; pp. 146 - 159 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Jan2018
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| 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=127618499&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127618499 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Jan2018 vid: 84 pid: 1004 pub: Elsevier B.V. artinfo: ui: 127618499 127618499 NLM29306539 10.1016/j.artmed.2017.12.004 NLM29306539 127618499 ppf: 146 ppct: 13 formats: tig: atl: Random ensemble learning for EEG classification. aug: au: Hosseini, Mohammad-Parsa Pompili, Dario Elisevich, Kost Soltanian-Zadeh, Hamid affil: Department of Electrical and Computer Engineering, Rutgers University, NJ 08854, United States sug: subj: Electroencephalography Brain Mapping Methods Brain Physiology Brain Physiopathology Seizures Diagnosis Signal Processing, Computer Assisted Automation Seizures Physiopathology Seizures Classification Reproducibility of Results Time Factors False Negative Results Predictive Value of Tests False Positive Results Neural Networks (Computer) Ferrans and Powers Quality of Life Index ab: Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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