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Background <p>Wearables with integrated electrocardiogram (ECG) acquisition have made single-lead ECGs widely accessible to patients and consumers. However, the 12-lead ECG remains the gold standard for most clinical cardiac assessments. In this study, we developed a neural network to reconstruct 12-lead ECGs from single-lead and dual-lead ECGs, and evaluated the mathematical accuracy.</p> Methods <p>We used lead I or leads I and II from 9514 individuals from the Physikalisch-Technische Bundesanstalt (PTB-XL) cohort and a generative adversarial network, with the aim of recreating the missing leads from the 12-lead ECG. ECGs were divided into training, validation, and testing (10%). Original and recreated leads were measured with a commercially available algorithm. Differences in means and variances were assessed with Student’s t-tests and F-tests, respectively. Calibration and bias were assessed with Bland-Altman plots. Inter-lead correlations were compared in original and recreated ECGs.</p> Results <p>The variability of precordial ECG amplitudes is significantly reduced in recreated ECGs compared to real ECGs (all <i>p</i>&#xa0;&lt;&#xa0;0.05), indicating regression-to-the-mean. Amplitude averages are recreated with bias (<i>p</i>&#xa0;&lt;&#xa0;0.05 for most leads). Reconstruction errors depend on the real amplitudes, suggesting regression-to-the-mean (<i>R</i><sup>2</sup> between target and error in R-peak amplitude in lead V3: 0.92). The relations between lead markers have a similar slope but are much stronger due to reduced variance (R-peak amplitude <i>R</i><sup>2</sup> between leads I and V3, real ECGs: 0.04, recreated ECGs: 0.49). Using two leads does not significantly improve 12-lead recreation.</p> Conclusions <p>AI-based 12-lead ECG reconstruction results in a regression-to-the-mean effect rather than personalized output, rendering it unsuitable for clinical use.</p>

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Evaluating the feasibility of 12-lead electrocardiogram reconstruction from limited leads using deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月25日

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  • Oriana Presacan,
  • Alexandru Dorobanţiu,
  • Jonas L. Isaksen,
  • Tobias Willi,
  • Claus Graff,
  • Michael A. Riegler,
  • Arun R. Sridhar,
  • Jørgen K. Kanters,
  • Vajira Thambawita

摘要

Background

Wearables with integrated electrocardiogram (ECG) acquisition have made single-lead ECGs widely accessible to patients and consumers. However, the 12-lead ECG remains the gold standard for most clinical cardiac assessments. In this study, we developed a neural network to reconstruct 12-lead ECGs from single-lead and dual-lead ECGs, and evaluated the mathematical accuracy.

Methods

We used lead I or leads I and II from 9514 individuals from the Physikalisch-Technische Bundesanstalt (PTB-XL) cohort and a generative adversarial network, with the aim of recreating the missing leads from the 12-lead ECG. ECGs were divided into training, validation, and testing (10%). Original and recreated leads were measured with a commercially available algorithm. Differences in means and variances were assessed with Student’s t-tests and F-tests, respectively. Calibration and bias were assessed with Bland-Altman plots. Inter-lead correlations were compared in original and recreated ECGs.

Results

The variability of precordial ECG amplitudes is significantly reduced in recreated ECGs compared to real ECGs (all p < 0.05), indicating regression-to-the-mean. Amplitude averages are recreated with bias (p < 0.05 for most leads). Reconstruction errors depend on the real amplitudes, suggesting regression-to-the-mean (R2 between target and error in R-peak amplitude in lead V3: 0.92). The relations between lead markers have a similar slope but are much stronger due to reduced variance (R-peak amplitude R2 between leads I and V3, real ECGs: 0.04, recreated ECGs: 0.49). Using two leads does not significantly improve 12-lead recreation.

Conclusions

AI-based 12-lead ECG reconstruction results in a regression-to-the-mean effect rather than personalized output, rendering it unsuitable for clinical use.