Toward self-validating ECG Systems: A personalized, uncertainty-aware approach for detecting and estimating lead...

Area of Science:

• Artificial Intelligence in Healthcare
• Biomedical Signal Processing
• Medical Device Technology

Background:

• Accurate electrocardiogram (ECG) interpretation relies heavily on correct electrode placement.
• Existing solutions primarily address lead swaps in standard 12-lead ECGs.
• Increasing use of portable and reduced-lead ECG devices necessitates robust electrode misplacement detection.

Purpose of the Study:

• To develop an automated framework for detecting and quantifying ECG electrode misplacement.
• To enhance the diagnostic reliability of ECG recordings, especially with non-standard configurations.
• To support the wider adoption of ECG technology in diverse clinical and remote settings.

Main Methods:

• An end-to-end, personalized, uncertainty-aware framework utilizing deep convolutional encoders and regression heads was developed.
• The system processed ECG waveforms, identifying lead source areas and estimating misplacement magnitude and direction.
• Patient-specific ECG morphology and Monte Carlo dropout for uncertainty quantification were integrated.

Main Results:

• The framework achieved high accuracy in detecting lead source areas (>94%) and estimating misplacement (MAE of 2.2 cm).
• Personalization improved accuracy to 97.5% and reduced MAE to 2.0 cm, maintaining performance for specific diagnoses.
• Uncertainty quantification further boosted accuracy to 98.6% and lowered MAE to 1.8 cm by flagging ambiguous cases.

Conclusions:

• A practical solution for improving ECG lead placement accuracy was introduced.
• The developed method enables self-validating lead positioning, enhancing diagnostic reliability.
• This technology can facilitate broader ECG adoption in clinical and decentralized healthcare settings.