# Detecting QT prolongation From a Single-lead ECG With Deep Learning

arXiv: https://arxiv.org/abs/2401.05378 · HTML: https://mlsanity.com/paper/2401.05378/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)

## Built on by (most cited first, 4 of 4)

- [On-device Computation of Single-lead ECG Parameters for Real-time Remote Cardiac Health Assessment: A Real-world Validation Study](https://mlsanity.com/paper/2502.17499/) (2025)
- [Unlocking Telemetry Potential: Self-Supervised Learning for Continuous Clinical Electrocardiogram Monitoring](https://mlsanity.com/paper/2406.16915/) (2024)
- [Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised Models](https://mlsanity.com/paper/2408.15272/) (2024)
- [AICRN: Attention-Integrated Convolutional Residual Network for Interpretable Electrocardiogram Analysis](https://mlsanity.com/paper/2508.12162/) (2025)

## Abstract (arXiv)

For a number of antiarrhythmics, drug loading requires a 3 day hospitalization with monitoring for QT prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and to use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. Using 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. QTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. Drug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning.