AN-YEU(ANDY) WU2022-05-192022-05-1920209.78151E+1215206149https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089234258&doi=10.1109%2fICASSP40776.2020.9053866&partnerID=40&md5=4ffa74e148c39f7903f718ef360a64c1https://scholars.lib.ntu.edu.tw/handle/123456789/611213In 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%. © 2020 IEEE.Atrial fibrillation detection; compressed alignment; compressive analysis; compressive sensing; real-time ECG telemonitoring[SDGs]SDG7Alignment; Audio signal processing; Electrocardiography; Labeled data; Speech communication; Compressed learning; Compressive sensing; Computation time; Computing resource; ECG signals; Memory overheads; Tele-monitoring; Training data; Data reductionconference paper10.1109/ICASSP40776.2020.90538662-s2.0-85089234258