Does a novel unsupervised 12-lead ECG feature learning framework improve the accuracy of myocardial infarction and atrial fibrillation detection compared to existing models?
A novel unsupervised feature learning framework for 12-lead ECGs improves the accuracy of myocardial infarction and atrial fibrillation detection and is suitable for lightweight Internet of Medical Things applications.
The development of healthcare industry, especially Internet of Medical Things (IoMT), has generated considerable unlabeled electrocardiogram (ECG) signals. This article proposes a new unsupervised feature learning method for these unlabeled 12-lead ECGs, a type of 12-channel 1-D time series. Based on contrastive predictive coding (CPC), it considers the characteristics of 12-lead ECGs and develops novel lead-separation CPC (LSCPC) and lead-combination CPC (LCCPC). Specifically, LSCPC captures intralead features for each lead, while LCCPC combines all the leads and explores interlead relationships. Furthermore, a fusion model of LSCPC and LCCPC generates final representations. The Physikalisch-Technische Bundesanstalt (PTB)-XL database that contains 21837 12-lead records is used for unsupervised feature learning. Using learned features, linear classifiers are trained to accomplish the downstream tasks. 448 ECG records from 148 myocardial infarction (MI) and 52 healthy control subjects of the PTB database are used for MI detection. 6877 records from the CPSC-2018 database are used for atrial fibrillation (AF) detection, including 918 normal records, 1098 AF records, and 4861 other records. Using fivefold cross-validation, our model achieves 90.38% and 73.27% accuracy in MI and AF detection, respectively. Compared with existing models, it improves the performances by at least 2.32% for MI detection and 3.99% for AF detection. The model has been deployed on a lightweight embedded system (800-MHz ARM processor, 1-GB RAM). The maximum latency is only 465.34 ms, which can satisfy the real-time constraints. Overall, all the results have demonstrated the potential of our method for real-world healthcare, and lightweight IoMT applications.
Liu et al. (Wed,) studied this question.