QTcNet: a deep learning model for direct heart rate corrected QT interval estimation.

ABSTRACT:

Aims

Automated QTc measurements from commercial ECG systems often diverge from expert readings. We developed QTcNet, a deep learning model trained and validated on multiple large ECG datasets to improve automated QTc measurement accuracy.

Methods and results

QTcNet employs a regression-based convolutional neural network architecture. It was trained on 120 300 algorithm-labelled ECGs (60 150 from an internal hospital cohort and 60 150 from the MIMIC-IV dataset) after correction for a vendor-specific +15 ms bias. Performance was evaluated against expert QTc measurements in three independent datasets: PTB Diagnostic ECG Database (n = 100 ECGs in validation set), QTcMS (n = 210), and ECGRDVQ (n = 5219). The effect of fine-tuning on cardiologist-annotated ECGs was tested in the P

SUBMITTER: Plagwitz L 

PROVIDER: S-EPMC12696413 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature