A MATLAB Algorithm to Automatically Estimate the QT Interval and Other ECG Parameters and Validation Using a Machine...

Area of Science:

• Cardiology
• Biomedical Engineering
• Computational Medicine

Background:

• Myocardial repolarization and QT duration are vital for diagnosing and monitoring congenital long QT syndrome (LQTS).
• Accurate QT interval measurement is essential for effective patient management.
• Existing methods may lack the precision or automation required for large-scale screening.

Purpose of the Study:

• To develop and validate a novel algorithm for automatic estimation of the QT interval and T-wave morphology parameters.
• To assess the algorithm's performance in classifying LQTS patients based on QTc interval duration.
• To provide a transparent and reproducible tool for QT interval analysis in LQTS.

Main Methods:

• A novel algorithm based on Lepeschkin's tangent method was developed for digital ECGs.
• The algorithm was applied to 466 LQTS patients and 40 healthy controls.
• Performance was validated against expert measurements and the MUSE™ system, using a Support Vector Machine classifier.

Main Results:

• The algorithm accurately estimated QT intervals and T-wave morphology parameters.
• The Support Vector Machine classifier achieved 78.1% accuracy and an AUC of 0.85 in classifying LQTS patients with prolonged QTc.
• Results showed good agreement with expert measurements and the MUSE™ system.

Conclusions:

• The developed MATLAB®-based algorithm offers a reliable method for automatic QT interval and QTc calculation in LQTS patients.
• This approach has the potential to enhance automated screening, diagnostic accuracy, and patient management for LQTS.
• The algorithm provides a transparent and reproducible solution for analyzing ECG data in long QT syndrome.