{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:17:35Z","timestamp":1776277055247,"version":"3.50.1"},"reference-count":18,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T00:00:00Z","timestamp":1576108800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National \u201cTwelfth Five-Year\u201d Science and Technology Support Project of China","award":["2015BAI02B04"],"award-info":[{"award-number":["2015BAI02B04"]}]},{"name":"National \u201cTwelfth Five-Year\u201d Science and Technology Support Project of China","award":["17YJ009"],"award-info":[{"award-number":["17YJ009"]}]},{"name":"Science and Technology Plan Project of Jilin Province, China","award":["2015BAI02B04"],"award-info":[{"award-number":["2015BAI02B04"]}]},{"name":"Science and Technology Plan Project of Jilin Province, China","award":["17YJ009"],"award-info":[{"award-number":["17YJ009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2019,12,12]]},"abstract":"<jats:p>Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). The EEMD method solves the \u201cmode mixing\u201d problem in EMD effectively, but it can do nothing about the \u201cend effect,\u201d another problem in the decomposition process. In response to this problem, an improved EEMD method based on support vector regression extension (SVR-EEMD) is proposed and verified by simulated data and real-world PPG data. Experiments show that the SVR-EEMD method can solve the \u201cend effect\u201d efficiently to get a better decomposition performance than the traditional EEMD method and bring more benefits to the noise processing of PPG signals.<\/jats:p>","DOI":"10.1155\/2019\/5363712","type":"journal-article","created":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T18:31:33Z","timestamp":1576175493000},"page":"1-10","source":"Crossref","is-referenced-by-count":11,"title":["SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8905-7641","authenticated-orcid":true,"given":"Guangda","family":"Liu","sequence":"first","affiliation":[{"name":"College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, 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