A Shallow Domain Knowledge Injection (SDK‐Injection) Method for Improving CNN‐Based ECG Pattern Classification

Soyeon Oh, Minsoo Lee

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


ECG pattern classification for identifying the progress status of various heart diseases is a typical nonlinear problem. Therefore, deep learning‐based automatic ECG diagnosis is being widely studied, and for this purpose, the CNN is mainly used to classify ECG patterns. In this case, it is hard to expect any further improvement in accuracy after optimizing the parameters. We propose a shallow domain knowledge injection method that can improve the accuracy of the existing parameter‐optimized CNN. The proposed method can improve the accuracy by effectively injecting shallow domain knowledge, that can be acquired by non‐medical experts, into the existing parameter‐optimized CNN. The experiments show that the proposed method can be applied to both heart disease diagnoses and general ECG classification tasks, while improving the existing accuracy for both types of tasks.

Original languageEnglish
Article number1307
JournalApplied Sciences (Switzerland)
Issue number3
StatePublished - 1 Feb 2022

Bibliographical note

Funding Information:
Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1062559).

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • Attention mechanism
  • Classification
  • Convolutional neural network
  • ECG
  • Time series data


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