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

conference paper

English

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.

P Wave Detection, Weakly Supervised Learning, Atrial Fibrillation, Supraventricular Tachycardias, Rhythm Irregularities, Deep Neural Networks, Temporal Convolutional Neural Network

HEJČ, J.; ŘEDINA, R.; POSPÍŠIL, D.; RAKOVÁ, I.; KOLÁŘOVÁ, J.; STÁREK, Z.

20. 11. 2023

IEEE Computer Society

Atlanta

2325-887X

Computing in Cardiology

50

neuvedeno

United States of America

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https://cinc.org/archives/2023/pdf/CinC2023-321.pdf