Domain knowledge guided deep atrial fibrillation classification and its visual interpretation

Abstract

Hand-crafted features have been proven useful in solving the electrocardiograph (ECG) classification problem. The features rely on domain knowledge and carry clinical meanings. However, the construction of the features requires tedious fine tuning in practice. Lately, a set of end-to-end deep neural network models have been proposed and show promising results in ECG classification. Though effective, such models learn patterns which usually mismatch human's concept, and thereby it is hard to get a convincing explanation with interpretation methods. This limitation significantly narrows the applicability of deep models, considering it is difficult for cardiologists to accept the unexplainable results from deep learning. To alleviate such limitation, we are bringing the best from the two worlds and propose a domain knowledge guided deep neural network. Specifically, we utilize a deep residual network as a classification framework, within which key feature (P-wave and R-peak position) reconstruction tasks are adopted to incorporate domain knowledge in the learning process. The reconstruction tasks make the model pay more attention to key feature points within ECG. Furthermore, we utilize occlusion method to get visual interpretation and design a visualization at both heartbeat level and feature point level. Our experiments show the superior performance of the proposed ECG classification methods compared to the model without P-wave and R-peak tasks, and the patterns learnt by our model is more explainable.