https://scholars.lib.ntu.edu.tw/handle/123456789/611227
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | AN-YEU(ANDY) WU | en_US |
dc.date.accessioned | 2022-05-19T07:46:38Z | - |
dc.date.available | 2022-05-19T07:46:38Z | - |
dc.date.issued | 2019 | - |
dc.identifier.isbn | 9.78173E+12 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063074656&doi=10.1109%2fGlobalSIP.2018.8646402&partnerID=40&md5=c6eef5b4cb94635595abc3df51248e50 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/611227 | - |
dc.description.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. | - |
dc.language | en_US | - |
dc.relation.ispartof | 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings | - |
dc.subject | Compressed Analysis; Compressed sensing; Real-Time ECG Telemonitoring; Task-Driven Dictionary Learning | - |
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 | - |
dc.title | Low-complexity compressed analysis in eigenspace with limited labeled data for real-time electrocardiography telemonitoring | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.1109/GlobalSIP.2018.8646402 | - |
dc.identifier.scopus | 2-s2.0-85063074656 | - |
dc.relation.pages | 459-463 | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.cerifentitytype | Publications | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Electronics Engineering | - |
crisitem.author.dept | Intel-NTU Connected Context Computing Center | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0003-4731-8633 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: International Research Centers | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 電機工程學系 |
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