Year: 2024
Type: conference-paper
Abstract: ECG delineation is crucial for assessing drug-induced proarrhythmic risks, but still heavily depends on expert cardiologists despite existing automated methods.Our study proposes a deep learning model, E-DelUnet, to accurately locate key ECG fiducial points: P onset , QRS onset , QRS of f set , T peak , and T end .E-DelUnet is an adapted U-Net without skip connections and up-sampling, improving speed, performance, and reducing overfitting.It processes 1.2s single-lead ECGs and outputs binary masks marking fiducials.We trained the model on 2,054 ECGs from Verapamil and Quinidine studies using 5-fold cross-validation and tested it on 13,085 ECGs from other drug studies.Compared to traditional U-Net, wavelet-based methods, and residual networks, E-DelUnet showed the best performance, closely matching cardiologist measurements (from 3.86 ± 2.9 ms for the QRS onset to 6.77 ± 7.41 ms for the T peak ).This model holds promise for improving ECG analysis in drug safety assessments and clinical workflows.
Source: Computing in cardiology
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405435317