{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T10:50:28Z","timestamp":1773831028210,"version":"3.50.1"},"reference-count":35,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61875062"],"award-info":[{"award-number":["61875062"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hubei Provincial Natural Science Foundation Innovation and Development Joint Fund Project","award":["2025AFD626"],"award-info":[{"award-number":["2025AFD626"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n Seismocardiography (SCG) contains rich physiological information about the structure and function of the heart, providing a new dimension for early screening and dynamic monitoring of cardiovascular diseases. However, SCG is relatively weak, susceptible to severe external interference, even has strong individual differences in morphology, making it difficult for traditional algorithms to accurately detect AO, AC and other core features.\n Approach.<\/jats:italic>\n Herein, Combining the time\u2013frequency joint distribution characteristics of SCG, we improve a novel adaptive extended Kalman filter (KF) algorithm for accurate AO and AC detection. To achieve unified modeling for different individual SCG, Gaussian mixture module is used to fit the morphological template in the time\u2013frequency domain based on the optimal estimation strategy. Then, in order to balance the estimation accuracy and computational efficiency of the nonlinear system constructed based on SCG, the extended KF is constructed by linearizing the state transition equation. Moreover, with the aim of accurately estimating the time-varying noise components in motion, the forgetting factor\n \n \n \n <\/jats:tex-math>\n \n \n \n \n \u03b1<\/mml:mi>\n k<\/mml:mi>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n is introduced based on the residual\n \n \n \n <\/jats:tex-math>\n \n \n \n \n e<\/mml:mi>\n k<\/mml:mi>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n with the low-pass filtering strategy to adaptively update the measurement noise covariance matrix\n \n \n \n <\/jats:tex-math>\n \n \n R<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , thereby achieving high-quality filtering to SCG with the AO and AC area.\n Main results.<\/jats:italic>\n In addition, the experiment is conducted on the open-source CEBS dataset, indicating that the proposed algorithm has better filtering effect and higher AO and AC\u2019 detection accurate on the static SCG. Furthermore, the portable hardware system is designed for collecting SCG during 6 min walk test. Meanwhile, the impedance cardiography equipment is employed to record heart rate, left ventricular ejection time and other hemodynamics parameters. Compared with these common algorithms, the proposed algorithm also has better detection performance on the SCG during exercise.\n Significance.<\/jats:italic>\n In the future, the proposed algorithm will be integrated with the portable SCG hardware system designed, which is expected to be applied in the convenient diagnosis of heart diseases, the dynamic measurement of cardiovascular parameters, the dynamic blood pressure measurement without the need for wearing cuffs and more medical scene.\n <\/jats:p>","DOI":"10.1088\/1361-6579\/ae4b81","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T22:53:17Z","timestamp":1772232797000},"page":"035010","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel adaptive extended Kalman filter algorithm driven by time\u2013frequency Gaussian mixture model for accurate AO and AC detection based on portable seismocardiography"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9092-3217","authenticated-orcid":true,"given":"Yingbin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5373-0870","authenticated-orcid":true,"given":"Guoping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zibo","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"pmeaae4b81bib1","first-page":"1","type":"conference-proceedings","article-title":"Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation","author":"Akhlaghi","year":"2017"},{"key":"pmeaae4b81bib2","doi-asserted-by":"publisher","first-page":"3805","DOI":"10.1109\/JSEN.2017.2701349","type":"journal-article","article-title":"Automatic detection of seismocardiogram sensor misplacement for robust pre-ejection period estimation in unsupervised settings","volume":"17","author":"Ashouri","year":"2017","journal-title":"IEEE Sens. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-10-30","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-02-27","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-18","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}