Low-Complexity Compressive Analysis in Sub-Eigenspace for ECG Telemonitoring System

Abstract

Compressive sensing (CS) is attractive in long-term electrocardiography (ECG) telemonitoring to extend life-time for resource-limited wireless wearable sensors. Moreover, health monitoring has emphasized the need for edge computing to process real-time data without the bandwidth costs. However, the reconstructed analysis (RA) and the compressed learning (CL) frameworks have extremely high memory and computational overhead, cost-prohibitive for online usage at resource-constrained edge device. In this paper, to efficiently analyze the received CS measurements with different levels of compression, we propose a low-complexity framework of Compressive Analysis in Sub-Eigenspace (CA-SE) based on subspace-based representation. The dictionary is used for sifting the sub-eigen information from the CS measurements online, and it is built by eigenspace learning offline. The framework can reduce the memory overhead with a single light-weight machine learning model and multiple small filter matrices, and the computational complexity with sifting by matrix-vector product rather than sparse coding. CA-SE is implemented in ECG-based atrial fibrillation detection. The memory overhead of CA-SE is 13 and 39 times fewer compared with RA and CL, respectively, and the computational complexity of CA-SE is 42 and 10 times fewer compared with RA and CL, respectively.