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<p>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&#xa0;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&#xa0;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.</p>

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Automated Characterization of Sudden Cardiac Death Using Locality Preserving Projection and Fuzzy Entropy Based on Empirical Mode Decomposition from ECG Signals

  • 期刊论文

  • 发表日期：

    2025年8月16日

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  • Manhong Shi,
  • Yinuo Shi,
  • Wenkang Zhou,
  • Xue Qi

摘要

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.