Automatic Diagnosis with 12-Lead ECG Signals

摘要

Electrocardiogram (ECG) is strong evidence in the diagnosis of a wide range of heart-related diseases, and it is becoming increasingly important in the medical field recently. However, inferencing diseases with ECG signals is both time-consuming and error-prone even for licensed physicians, which arises the urgency of developing a fast and accurate automatic diagnosis algorithm. In this paper, we explore both deep learning models and well-designed feature engineering from ECG waveform. By combining the two methods, we propose an automatic diagnosis framework that can extract meaningful features both with and without human interventions. Experimental results on the ECG competition demonstrate that our framework can reach accurate results on heart-related diseases diagnosis.