Direct Lead Assignment: A Simple and Scalable Contrastive Learning Method for ECG and Its IoMT Applications

Nowadays, applying deep learning (DL) to electrocardiogram (ECG) analysis has become a significant topic in intelligent healthcare. DL models heavily rely on large-scale labeled ECGs in supervised learning, while labeling ECGs is a costly and time-consuming process. This article proposes a simple self-supervised learning (SSL) method to pretrain models using unlabeled ECGs, improving model performances in a low-data regime. It is termed direct lead assignment (DLA). In pretraining, DLA employs multilead and single-lead encoders to interact between global and lead-specific representations. The pretrained encoders can constitute scalable models, which can be deployed on Internet of Medical Things (IoMT) devices for ECG monitoring with different leads. According to the experiments, DLA outperforms existing SSL methods for ECGs and reduces the reliance on labels by4×–8×. In other words, DLA can make the model obtain better results when labeled data are scarce than the one trained from scratch, as the pretraining helps the model recognize critical ECG patterns in a low-data regime. For IoMT applications, the models are deployed on a Raspberry Pi 2 W with an ARM Cortex-A53 processor. It can run the models in real time. The maximum running time is about 388 ms/10-s record. Thus, DLA shows excellent potential for automatic ECG analysis based on IoMT, aiding cardiologists in diagnosing cardiovascular diseases.