AN-YEU(ANDY) WU2022-05-192022-05-1920199.78173E+12https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063074656&doi=10.1109%2fGlobalSIP.2018.8646402&partnerID=40&md5=c6eef5b4cb94635595abc3df51248e50https://scholars.lib.ntu.edu.tw/handle/123456789/611227To achieve real-time electrocardiography (ECG) telemonitoring, one of the major obstacles to overcome is the scarce bandwidth. Compressed sensing (CS) has emerged as a promising technique to greatly compress the ECG signal with little computation. Furthermore, with edge-classification, the data rate can be reduced by transmitting abnormal ECG signals only. However, there are three main limitations: limited amount of labeled ECG data, tight battery constraint of edge devices and low response time requirement. Task-driven dictionary learning (TDDL) appears as an appropriate classifier to render low complexity and high generalization. Combining CS with TDDL directly (CA-N) will degrade classification and require higher complexity model. In this paper, we propose an eigenspace-aided compressed analysis (CA-E) integrating principal component analysis (PCA), CS and TDDL, sustaining not only light complexity but high performance under exiguous labeled ECG dataset. Simulation results show that CA-E reduces about 67% parameters, 76% training time, 87% inference time and has a smaller accuracy variance to the CA-N counterpart. © 2018 IEEE.Compressed Analysis; Compressed sensing; Real-Time ECG Telemonitoring; Task-Driven Dictionary LearningCompressed sensing; Electrocardiography; Principal component analysis; Complexity modeling; Compressed Analysis; Compressive sensing; Dictionary learning; Edge classification; Tele-monitoring; Time requirements; Training time; Biomedical signal processingconference paper10.1109/GlobalSIP.2018.86464022-s2.0-85063074656