QTANet: Boundary-Aware Deep Learning for Single-Lead QT-Interval Measurement

Accurate QT-interval measurement requires reliable identification of QRS onset and T-wave offset. We present QTANet, a single-lead framework that classifies ECG samples as baseline, QRS or T wave and derives QT from the detected landmarks. We used 179 recordings from the Lobachevsky University Electrocardiography Database, with a fixed test set and patient-wise cross-validation, and external evaluation without fine-tuning on the QT Database (QTDB) and 33 clinical recordings. On the expert-annotated QTDB interval subset of 101 recordings, QTANet achieved a mean absolute error of 34.94 ms and measurement coverage of 95.2%, compared with 40.55 ms and 88.2% for the control without attention and 52.41 ms and 68.7% for NeuroKit2. In a separate expert-annotated subset permitting frame-level evaluation, attention increased per-recording T-wave F1 by 0.061, with improvement in 30 of 41 recordings. Attention most consistently reduced T waves misread as QRS complexes. QTANet outperformed an identically trained published U-Net externally at frame level but had higher internal QT error; a larger modern U-Net outperformed QTANet at frame level and in QTDB QT error. Internal interval-level gains were small and clinical results varied across training seeds. These findings support boundary-derived QT measurement with jointly reported error and coverage; clinical utility requires further validation.