{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/biosignal-analysis-with-matching-pursuit","title":"Biosignal Analysis with Matching-Pursuit Based Adaptive Chirplet Transform","arxiv_id":"1709.08328","date":"2017-09-25","proceeding":null,"authors":["Jie Cui","Dinghui Wang"],"abstract":"Chirping phenomena, in which the instantaneous frequencies of a signal change\nwith time, are abundant in signals related to biological systems. Biosignals\nare non-stationary in nature and the time-frequency analysis is a viable tool\nto analyze them. It is well understood that Gaussian chirplet function is\ncritical in describing chirp signals. Despite the theory of adaptive chirplet\ntransform (ACT) has been established for more than two decades and is well\naccepted in the community of signal processing, application of ACT to\nbio-/biomedical signal analysis is still quite limited, probably because that\nthe power of ACT, as an emerging tool for biosignal analysis, has not yet been\nfully appreciated by the researchers in the field of biomedical engineering. In\nthis paper, we describe a novel ACT algorithm based on the \"coarse-refinement\"\nscheme. Namely, the initial estimate of a chirplet is implemented with the\nmatching-pursuit (MP) algorithm and subsequently it is refined using the\nexpectation-maximization (EM) algorithm, which we coin as MPEM algorithm. We\nemphasize the robustness enhancement of the algorithm in face of noise, which\nis important to biosignal analysis, as they are usually embedded in strong\nbackground noise. We then demonstrate the capability of our algorithm by\napplying it to the analysis of representative biosignals, including visual\nevoked potentials (bioelectrical signals), audible heart sounds and bat\nultrasonic echolocation signals (bioacoustic signals), and human speech. The\nresults show that the MPEM algorithm provides more compact representation of\nsignals under investigation and clearer visualization of their time-frequency\nstructures, indicating considerable promise of ACT in biosignal analysis. The\nMATLAB code repository is hosted on GitHub for free download\n(https://github.com/jiecui/mpact).","url_abs":"http://arxiv.org/abs/1709.08328v1","url_pdf":"http://arxiv.org/pdf/1709.08328v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"biosignal-analysis-with-matching-pursuit","repo_url":"https://github.com/jiecui/mpact","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}