A time local subset feature selection for prediction of sudden cardiac death from ECG signal.
Prediction of sudden cardiac death continues to gain universal attention as a promising approach to saving millions of lives threatened by sudden cardiac death (SCD). This study attempts to promote the literature from mere feature extraction analysis to developing strategies for manipulating the ext...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 7; pp. 1253 - 1271 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2018
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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=130320747&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130320747 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2018 vid: 56 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130320747 130320747 NLM29238903 130320747 10.1007/s11517-017-1764-1 NLM29238903 130320747 ppf: 1253 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A time local subset feature selection for prediction of sudden cardiac death from ECG signal. aug: au: Ebrahimzadeh, Elias Manuchehri, Mohammad Sajad Amoozegar, Sana Araabi, Babak Nadjar Soltanian-Zadeh, Hamid affil: School of Electrical and Computer Engineering, College of Engineering, University of Tehran, N Kargar St., Tehran, Iran sug: subj: Algorithms Signal Processing, Computer Assisted Electrocardiography Death, Sudden, Cardiac Prevention and Control Adult Young Adult Male Heart Rate Physiology ROC Curve Pharmacokinetics Time Factors Female Middle Age Human Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Prediction of sudden cardiac death continues to gain universal attention as a promising approach to saving millions of lives threatened by sudden cardiac death (SCD). This study attempts to promote the literature from mere feature extraction analysis to developing strategies for manipulating the extracted features to target improvement of classification accuracy. To this end, a novel approach to local feature subset selection is applied using meticulous methodologies developed in previous studies of this team for extracting features from non-linear, time-frequency, and classical processes. We are therefore enabled to select features that differ from one another in each 1-min interval before the incident. Using the proposed algorithm, SCD can be predicted 12 min before the onset; thus, more propitious results are achieved. Additionally, through defining a utility function and employing statistical analysis, the alarm threshold has effectively been determined as 83%. Having selected the best combination of features, the two classes are classified using the multilayer perceptron (MLP) classifier. The most effective features would subsequently be discussed considering their prevalence in the rank-based selection. The results indicate the significant capacity of the proposed method for predicting SCD as well as selecting the appropriate processing method at any time before the incident. Graphical abstract ᅟ. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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