Automated Characterization of Sudden Cardiac Death Using Locality Preserving Projection and Fuzzy Entropy Based on Empirical Mode Decomposition from ECG Signals.
The early prediction of sudden cardiac death (SCD) has garnered considerable global attention as a potentially life-saving intervention for at-risk individuals. While various strategies have been proposed, many are constrained by prediction time resolution (typically analyzing 1- to 2-min ECG segmen...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 18 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
8/16/2025
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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=187385009&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187385009 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/16/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187385009 187385009 187385009 10.1007/s10916-025-02239-3 187385009 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Characterization of Sudden Cardiac Death Using Locality Preserving Projection and Fuzzy Entropy Based on Empirical Mode Decomposition from ECG Signals. aug: au: Shi, Manhong Shi, Yinuo Zhou, Wenkang Qi, Xue affil: https://ror.org/01pn91c28 College of Information and Network Engineering, Anhui Science and Technology University, 233000, Bengbu, China sug: subj: Death, Sudden, Cardiac Risk Factors Disease Attributes Early Diagnosis Electrocardiography Methods Risk Assessment Human Adolescence Adult Middle Age Aged, 80 and Over Male Female Funding Source Validation Studies Sensitivity and Specificity Comparative Studies Descriptive Statistics Predictive Value of Tests Heart Rate Variability Automation Death, Sudden, Cardiac Diagnosis Cross Training T-Tests Wilcoxon Rank Sum Test Electrocardiography Classification Prediction Models Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged, 80 & over Male Female ab: The early prediction of sudden cardiac death (SCD) has garnered considerable global attention as a potentially life-saving intervention for at-risk individuals. While various strategies have been proposed, many are constrained by prediction time resolution (typically analyzing 1- to 2-min ECG segments) and early prediction time windows not exceeding 20 min. In this study, we propose a novel yet straightforward methodology that combines locality preserving projection (LPP) features and fuzzy entropy (FuEn) based on empirical mode decomposition (EMD) from individual ECG beats containing 1000 data points. Specifically, 15 features were extracted: 14 discriminative LPP features selected from the training data using the feature ranking method, along with one FuEn feature calculated from the first intrinsic mode function (IMF1) of the EMD. These selected features are applied to test data to differentiate between normal subjects and those at risk of SCD. A distinguishing aspect of our approach is that it analyzes each single ECG beat for SCD prediction, rather than relying on 1- or 2-min segments. Additionally, we incorporate group-based fivefold cross-validation to ensure a robust evaluation of prediction performance. Our method successfully predicts SCD 30 min in advance with an accuracy of 97.6%. In principle, the features extracted from this methodology can be integrated into portable medical sensors for real-time SCD risk assessment, suitable for use both in medical facilities and at home under the supervision of healthcare providers. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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