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

Featured Application The proposed method can improve the accuracy of an existing parameter-optimized CNN for ECG pattern classification. Especially, by applying the proposed method to single ECG-based analysis, the existing accuracy can be improved to a level similar to, or higher than, that of multi-ECG-based analysis. Since wearable devices, such as smart watches, mainly measure a single ECG stream, the proposed method can be applied to the core technologies of wearables-based smart hospital or healthcare services. In addition to aiding heart disease diagnoses, it can be applied to various IoT services, based on general analyses using ECG. 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.