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

• Main Authors: Pua, Yo-Woei, Chou, Ching-Yao, Wu, An-Yeu Andy
• Format: Conference Proceeding
• Language: English
• Subjects: Atrial fibrillation detection; compressed alignment; compressive analysis; compressive sensing; Electrocardiography; real-time ECG telemonitoring; Real-time systems; Sensors; Signal processing; Simulation; Speech processing; Training data
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP40776.2020.9053866

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%.