Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.
The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classification cost by using fewer features and to improve t...
| Publicado en: | Medical Hypotheses Vol. 135 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Feb2020
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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=141636404&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141636404 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03069877 NZO jtl: Medical Hypotheses issn: 03069877 maglogo: N pubinfo: dt: Feb2020 vid: 135 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 141636404 141636404 NLM31731060 141636404 10.1016/j.mehy.2019.109464 NLM31731060 141636404 ppct: 1 formats: tig: atl: Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis. aug: au: Açıkoğlu, Merve Tuncer, Seda Arslan affil: Fırat University Faculty of Engineering, Software Engineering, 23119 Elazig, Turkey sug: subj: Electroencephalography Seizures Diagnosis Sensitivity and Specificity Decision Support Systems, Clinical False Positive Results Infant, Newborn Cluster Analysis Algorithms Signal Processing, Computer Assisted Diagnosis, Computer Assisted Human Infant, Newborn: birth-1 month ab: The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classification cost by using fewer features and to improve the classification performance of the model by removing the irrelevant features. In doing so, the classification performance of each FS algorithm on each EEG channel difference was also evaluated. The dataset used in the study included EEG measurements and visual EEG annotations that were recorded from 79 term neonates. Multiple features were extracted from each channel difference using the Feature extraction (FE). Subsequently, a novel feature subset was generated for the classification using FS algorithms. The classification performance of each selected feature was assessed based on multiple criteria. The use of features extracted by the combined use of FS algorithms showed higher performance compared to the use of all features. In this study, 18 channel differences were analyzed. Better performance was achieved by using 3 of the selected 14 features or 2 of the selected features. The C4-P4 channel difference showed the highest classification performance (98.8%) among all channel differences. In the literature, FE has already been performed for the classification of the dataset used in the present study. The primary aim of the present study was to perform the same classification with the minimum number of features. The results indicated that feature reduction reduced the cost and also improved the performance of the classification. These results seem to be highly promising and thus can be used in clinical practice and shed light for future studies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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