Online MECG Compression Based on Incremental Tensor Decomposition for Wearable Devices

Lightweight and real-time multi-lead electrocardiogram (MECG) compression on wearable devices is important and challenging for long-term health monitoring. To utilize all three kinds of correlations of MECG data simultaneously, we construct 3-order incremental tensor and formulate data compression problem as tensor decomposition. However, the conventional tensor decomposition algorithms for large-scale tensor are usually too computationally expensive to apply to wearable devices. To reduce the computation complexity, we develop online compression approach by incremental tracking the CANDECOMP/PARAFAC (CP) decomposition of dynamic incremental tensors, which can efficiently utilize the tensor compression result based on the previous MECG data to derive the tensor compression upon arriving of new data. We evaluate the performance of our method with the Physikalisch-Technische Bundesanstalt MECG diagnostic dataset. Our method can achieve the averaged percentage root-mean-square difference (PRD) of 8.35%+/- 2.28% and the compression ratio (CR) of 43.05 +/- 2.01, which is better than five state-of-the-art of methods. Additionally, it can also well preserve the information of R-peak. Our method is suitable for near real-time MECG compression on wearable devices.

Automated summary

A lightweight and real-time multi-lead electrocardiogram (MECG) compression method is developed using 3-order incremental tensor and CANDECOMP/PARAFAC (CP) decomposition algorithm to efficiently utilize tensor compression on wearable devices. Evaluation shows that the method outperforms five state-of-the-art methods with better performance on compression ratio and preservation of R-peak information, suitable for near real-time MECG compression on wearable devices.