A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals.
Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals,...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 10; pp. 1 - 2 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Oct2018
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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=132085507&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132085507 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2018 vid: 42 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132085507 132085507 132085507 10.1007/s10916-018-1031-5 132085507 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals. aug: au: Amezquita-Sanchez, Juan P. Valtierra-Rodriguez, Martin Adeli, Hojjat Perez-Ramirez, Carlos A. affil: Faculty of Engineering, Departments Biomedical and Electromechanical, ENAP-RG, Autonomous University of Queretaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P, 76807, San Juan del Río, Qro., Mexico sug: subj: Death, Sudden, Cardiac Diagnosis Signal Processing, Computer Assisted Echocardiography Neural Networks (Computer) Human Algorithms Adult Middle Age Adolescence Aged Aged, 80 and Over Massachusetts Male Female Adult: 19-44 years Middle Aged: 45-64 years Adolescent: 13-18 years Aged: 65+ years Aged, 80 & over Male Female ab: Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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