Low-Complexity Compressed Alignment-Aided Compressive Analysis for Real-Time Electrocardiography Telemonitoring

Abstract

In order to implement a real-time electrocardiogram (ECG) telemonitoring, compressed sensing (CS) is a new technology that reduces the power consumption of biosensors and data transmission. Unfortunately, limited label data and computing resources hinder the real-time ECG telemonitoring. Prior experiments have shown that aligning ECG signals is a good way to solve the problem of limited label data. However, the reconstructed learning (RL) framework requires a lot of computing resources, and the compressed learning (CL) framework makes alignment difficult. In this paper, we propose a new compressed alignment-aided compressive analysis (CA-CA) framework that enables simple alignment and low-complexity requirements. From simulation results, we have demonstrated that our technology can maintain more than 95% accuracy while reducing training data (labeled data) by 70%. Therefore, compared to RL, the computation time and memory overhead of CA-CA are reduced by 6.6 times and 2.45 times, respectively. Compared with CL, the inference accuracy with a small amount of labeled data is improved by 13.5%.