KLI

xECGNet: Fine-tuning attention map within convolutional neural network to improve detection and explainability of concurrent cardiac arrhythmias

Metadata Downloads
Abstract
Background and objective Detecting abnormal patterns within an electrocardiogram (ECG) is crucial for diagnosing cardiovascular diseases. We start from two unresolved problems in applying deep-learning based ECG classification models to clinical practice: first, although multiple cardiac arrhythmia (CA) types may co-occur in real life, the majority of previous detection methods have focused on one-to-one relationships between ECG and CA type, and second, it has been difficult to explain how neural-network based CA classifiers make decisions. We hypothesize that fine-tuning attention maps with regard to all possible combinations of ground-truth (GT) labels will improve both the detection and interpretability of co-occurring CAs. Methods To test our hypothesis, we propose an end-to-end convolutional neural network (CNN), xECGNet, that fine-tunes the attention map to resemble the averaged response maps of GT labels. Fine-tuning is achieved by adding to the objective function a regularization loss between the attention map and the reference (averaged) map. Performance is assessed by F1 score and subset accuracy. Results The main experiment demonstrates that fine-tuning alone significantly improves a model's multilabel subset accuracy from 75.8% to 84.5% when compared with the baseline model. Also, xECGNet shows the highest F1 score of 0.812 and yields a more explainable map that encompasses multiple CA types, when compared to other baseline methods. Conclusions xECGNet has implications in that it tackles the two obstacles for the clinical application of CNN-based CA detection models with a simple solution of adding one additional term to the objective function. (c) 2021 Elsevier B.V. All rights reserved.
Author(s)
김영학유정선전태준
Issued Date
2021
Type
Article
Keyword
AnalysisArrhythmiaCardiac arrhythmiaConvolutional neural networkECGElectrocardiogramElectrocardiographyExplainable AIHeartMedical AIMultilabel classificationNeural networks
DOI
10.1016/j.cmpb.2021.106281
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8074
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2557539156&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,xECGNet:%20Fine-tuning%20attention%20map%20within%20convolutional%20neural%20network%20to%20improve%20detection%20and%20explainability%20of%20concurrent%20cardiac%20arrhythmias&offset=0&pcAvailability=true
Publisher
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Location
아일랜드
Language
영어
ISSN
0169-2607
Citation Volume
208
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
Authorize & License
  • Authorize공개
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.