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Nowadays, an increasing number of people are being diagnosed withcardiovascular diseases (CVDs), the leading cause of death globally. The goldstandard for identifying these heart problems is via electrocardiogram (ECG).The standard 12-lead ECG is widely used in clinical practice and the majorityof current research. However, using a lower number of leads can make ECG morepervasive as it can be integrated with portable or wearable devices. Thisarticle introduces two novel techniques to improve the performance of thecurrent deep learning system for 3-lead ECG classification, making itcomparable with models that are trained using standard 12-lead ECG.Specifically, we propose a multi-task learning scheme in the form of the numberof heartbeats regression and an effective mechanism to integrate patientdemographic data into the system. With these two advancements, we gotclassification performance in terms of F1 scores of 0.9796 and 0.8140 on twolarge-scale ECG datasets, i.e., Chapman and CPSC-2018, respectively, whichsurpassed current state-of-the-art ECG classification methods, even thosetrained on 12-lead data. To encourage further development, our source code ispublicly available at https://github.com/lhkhiem28/LightX3ECG.

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