Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks.

ABSTRACT: Detection and delineation are key steps for retrieving and structuring information of the electrocardiogram (ECG), being thus crucial for numerous tasks in clinical practice. Digital signal processing (DSP) algorithms are often considered state-of-the-art for this purpose but require laborious rule readaptation for adapting to unseen morphologies. This work explores the adaptation of the the U-Net, a deep learning (DL) network employed for image segmentation, to electrocardiographic data. The model was trained using PhysioNet's QT database, a small dataset of 105 2-lead ambulatory recordings, while being independently tested for many architectural variations, comprising changes in the model's capacity (depth, width) and inference strategy (single- and multi-lead) in a fivefold cross-valida

SUBMITTER: Jimenez-Perez G 

PROVIDER: S-EPMC7806759 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature