Detailed Information on Publication Record
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