Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration

An increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). The standard 12-lead ECG is widely used in clinical practice and most of the current research. However, using fewer leads can make ECG more pervasive as it can be integrated with portable or wearable devices. This article introduces two novel techniques to improve the performance of the current deep learning system for 3-lead ECG classification, making it comparable with models that are trained using standard 12-lead ECG. Specifically, we propose a multi-task learning scheme in the form of the number of heartbeats regression and an effective mechanism to integrate patient demographic data into the system. With these two advancements, we got classification performance in terms of F1 scores of 0.9796 and 0.8140 on two large-scale ECG datasets, i.e., Chapman and CPSC2018, respectively, which surpassed current state-of-the-art ECG classification methods, even those trained on 12-lead data. Our source code is available at github.com/lhkhiem28/LightX3ECG.