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

• Published in: Applied sciences Vol. 12; no. 3; p. 1307
• Main Authors: Oh, Soyeon, Lee, Minsoo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2022
• Subjects: Accuracy; attention mechanism; Cardiovascular disease; Cardiovascular diseases; Classification; Clinical medicine; convolutional neural network; Coronary artery disease; Data mining; Deep learning; Domains; ECG; EKG; Electrocardiography; Heart; Heart diseases; Hospitals; Injection; Internal medicine; Ischemia; Knowledge; Knowledge acquisition; Medical diagnosis; Medicine; Neural networks; Noise; Pattern classification; Signal processing; Smartwatches; time series data; Wavelet transforms; Wearable computers; Wearable technology
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12031307

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.