{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:06Z","timestamp":1760242386859,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,21]],"date-time":"2017-06-21T00:00:00Z","timestamp":1498003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel method for extracting the fetal ECG (FECG) from a single-lead abdominal signal. A dynamical model for a modified abdominal signal is proposed, in which both the maternal ECG (MECG) and the FECG are modeled, and then a parallel marginalized particle filter (par-MPF) is used for tracking the abdominal signal. Finally, the FECG and MECG are simultaneously separated. Several experiments are conducted using both simulated and clinical signals. The results indicate that the method proposed in this paper effectively extracts the FECG and outperforms other Bayesian filtering algorithms.<\/jats:p>","DOI":"10.3390\/s17061456","type":"journal-article","created":{"date-parts":[[2017,6,22]],"date-time":"2017-06-22T02:39:15Z","timestamp":1498099155000},"page":"1456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Single-Lead Fetal ECG Extraction Based on a Parallel Marginalized Particle Filter"],"prefix":"10.3390","volume":"17","author":[{"given":"Zhidong","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Electronics and Information, Hangzhou Dianzi University; Hangdian Smart City Research Center of Zhejiang Province, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiling","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjun","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yefei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihui","family":"Ye","sequence":"additional","affiliation":[{"name":"Women\u2019s Hospital School of Medicine Zhejiang University, Hangzhou 310006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1088\/0967-3334\/35\/7\/1685","article-title":"Noninvasive fetal QRS detection using an echo state network and dynamic programming","volume":"35","author":"Lukosevicius","year":"2014","journal-title":"Physiol. 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