Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization

Jin Kook Kim, Sunghoon Jung, Jinwon Park, Sung Won Han

Research output: Contribution to journalArticlepeer-review

Abstract

Diagnosing arrhythmia is difficult, requires significant efforts. Because arrhythmia can be associated with serious diseases, it is important to classify arrhythmia patients with high accuracy, and the basis for the classification model's judgment should be properly demonstrated. Traditional algorithm methods are less accurate, and simply using a high-accuracy image classification deep learning model yields incomprehensible results when the model is visualized with gradient-weighted class activation mapping (Grad-CAM). We want to achieve high-performance deep learning models can also comprehensible visualization. To obtain this, two hypotheses about Grad-CAM were established and the experiment was conducted. As a result, a method that could clearly visualize the response area using Grad-CAM with a higher classification performance of 0.98 accuracy is created.

Original languageEnglish
Article number103408
JournalBiomedical Signal Processing and Control
Volume73
DOIs
Publication statusPublished - 2022 Mar

Keywords

  • Arrhythmia classification
  • Class activation mapping
  • Convolutional neural network
  • Electrocardiogram

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

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