|Title:||Low-complexity compressed analysis in eigenspace with limited labeled data for real-time electrocardiography telemonitoring||Authors:||AN-YEU(ANDY) WU||Keywords:||Compressed Analysis; Compressed sensing; Real-Time ECG Telemonitoring; Task-Driven Dictionary Learning||Issue Date:||2019||Start page/Pages:||459-463||Source:||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings||Abstract:||
To 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.
|ISBN:||9.78173E+12||DOI:||10.1109/GlobalSIP.2018.8646402||metadata.dc.subject.other:||Compressed sensing; Electrocardiography; Principal component analysis; Complexity modeling; Compressed Analysis; Compressive sensing; Dictionary learning; Edge classification; Tele-monitoring; Time requirements; Training time; Biomedical signal processing|
|Appears in Collections:||電機工程學系|
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