D 2023

Weakly Supervised P Wave Segmentation in Pathological Electrocardiogram Signals Using Deep Multiple-Instance Learning

HEJC, Jakub, Richard REDINA, David POSPÍŠIL, Ivana RAKOVA, Jana KOLAROVA et. al.

Basic information

Original name

Weakly Supervised P Wave Segmentation in Pathological Electrocardiogram Signals Using Deep Multiple-Instance Learning

Authors

HEJC, Jakub (203 Czech Republic), Richard REDINA (203 Czech Republic), David POSPÍŠIL (203 Czech Republic, belonging to the institution), Ivana RAKOVA (203 Czech Republic), Jana KOLAROVA (203 Czech Republic) and Zdeněk STÁREK (203 Czech Republic, belonging to the institution)

Edition

Atlanta, 2023 Computing in Cardiology, p. 1-4, 4 pp. 2023

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

30201 Cardiac and Cardiovascular systems

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

Organization unit

Faculty of Medicine

ISBN

979-8-3503-8252-5

Keywords in English

Pathological Electrocardiogram Signals; Deep Multiple-Instance Learning

Tags

Tags

International impact, Reviewed
Změněno: 22/8/2024 10:40, Mgr. Tereza Miškechová

Abstract

V originále

Detection of obscured P waves remains a largely unexplored topic. This study proposes a weakly supervised learning approach for P wave feature embedding by leveraging surrogate labels and 3265 eight-lead electrocardiographic (ECG) signals with diverse cardiac rhythms, including supraventricular tachycardias, atrial fibrillation, and paced rhythms. The proposed method employs a temporal convolutional neural network and multiple instance learning to learn pyramidal feature embeddings that estimate both labeled and unlabeled instances of the P wave. The fine-tuned model achieved a temporally aggregated Dice score of 81.1%, outperforming the baseline model by 1.0%. On the subset with sinus rhythms and minor rhythm irregularities, the model consistently achieved recall and precision of around 84-85% for P wave onset and offset. The framework can be used to learn embeddings correlated with the distribution of the atrial depolarization, using only a fraction of labeled samples. Surrogate labels allow us to embed more detailed context, which may enhance the performance and interpretability of deep neural networks in downstream tasks in the future