https://scholars.lib.ntu.edu.tw/handle/123456789/611227
標題: | Low-complexity compressed analysis in eigenspace with limited labeled data for real-time electrocardiography telemonitoring | 作者: | AN-YEU(ANDY) WU | 關鍵字: | Compressed Analysis; Compressed sensing; Real-Time ECG Telemonitoring; Task-Driven Dictionary Learning | 公開日期: | 2019 | 起(迄)頁: | 459-463 | 來源出版物: | 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings | 摘要: | 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063074656&doi=10.1109%2fGlobalSIP.2018.8646402&partnerID=40&md5=c6eef5b4cb94635595abc3df51248e50 https://scholars.lib.ntu.edu.tw/handle/123456789/611227 |
ISBN: | 9.78173E+12 | DOI: | 10.1109/GlobalSIP.2018.8646402 | SDG/關鍵字: | 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 |
顯示於: | 電機工程學系 |
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