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Time-series ECG Imputation Using a Pattern-Based Masking Framework.

Sukardi Suba1, Alexander Novak2, Xiaojuan Xia3

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Summary
This summary is machine-generated.

This study found that pattern-based missing data in electrocardiogram (ECG) time series is more challenging for imputation models than random missing data. The Self-Attention-based Imputation for Time Series (SAITS) model performed best overall.

Keywords:
electrocardiogramimputation modelmachine learningtime series

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Cardiology

Background:

  • Continuous electrocardiogram (ECG) monitoring is crucial in hospitals, but missing data hinders real-time predictive systems.
  • Existing research on ECG time-series imputation is limited, and current benchmarking often uses unrealistic random masking.

Purpose of the Study:

  • To evaluate and benchmark various imputation methods for continuous ECG time-series data.
  • To compare performance under both random and realistic pattern-based missingness conditions.

Main Methods:

  • Extracted time-domain features from 40 patients' 12-lead Holter recordings (2.5-4 hours each).
  • Introduced missingness using random and pattern-based masking.
  • Compared seven imputation methods: global mean, linear interpolation, KNN, MICE, softImpute, SMILES, and SAITS.
  • Evaluated performance using Mean Absolute Error (MAE).

Main Results:

  • All imputation methods showed higher MAE under pattern-based masking compared to random masking.
  • SAITS demonstrated the best performance across both masking types.
  • Simpler methods like SoftImpute and KNN showed competitive performance, especially at certain missingness levels.

Conclusions:

  • Random masking may underestimate the real-world accuracy of time-series imputation techniques.
  • Context-specific imputation strategies, considering masking approach and method, are vital.
  • Balancing model complexity with practical factors is essential for real-time ECG data deployment.