Related papers: Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble

Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble

URL: http://arxiv.org/abs/2109.12063v1
Date: Fri, 24 Sep 2021 16:41:27 GMT
Title: Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble
Authors: Hiroshi Seki, Takashi Nakano, Koshiro Ikeda, Shinji Hirooka, Takaaki Kawasaki, Mitsutomo Yamada, Shumpei Saito, Toshitaka Yamakawa, Shimpei Ogawa
Abstract summary: We propose deep neural network (DNN)-based ECG models that incorporate DivideMix and weight averaging (SWA) DivideMix was used to refine the noisy label by using two separate models. SWA was used to enhance the effect of the models generated by DivideMix.
Score: 2.6736079396231807
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Automatic diagnosis of multiple cardiac abnormalities from reduced-lead electrocardiogram (ECG) data is challenging. One of the reasons for this is the difficulty of defining labels from standard 12-lead data. Reduced-lead ECG data usually do not have identical characteristics of cardiac abnormalities because of the noisy label problem. Thus, there is an inconsistency in the annotated labels between the reduced-lead and 12-lead ECG data. To solve this, we propose deep neural network (DNN)-based ECG classifier models that incorporate DivideMix and stochastic weight averaging (SWA). DivideMix was used to refine the noisy label by using two separate models. Besides DivideMix, we used a model ensemble technique, SWA, which also focuses on the noisy label problem, to enhance the effect of the models generated by DivideMix. Our classifiers (ami_kagoshima) received scores of 0.49, 0.47, 0.48, 0.47, and 0.47 (ranked 9th, 10th, 10th, 11th, and 10th, respectively, out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions, respectively, of the hidden test set with the challenge evaluation metric. We obtained the scores of 0.701, 0.686, 0.693, 0.693, and 0.685 on the 10-fold cross validation, and 0.623, 0.593, 0.606, 0.612, and 0.601 on the hidden validation set for each lead combination.

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