A novel recursive Fourier transform for nonuniform sampled signals: application to heart rate variability spectrum estimation.
We present a novel method to iteratively calculate discrete Fourier transforms for discrete time signals with sample time intervals that may be widely nonuniform. The proposed recursive Fourier transform (RFT) does not require interpolation of the samples to uniform time intervals, and each iterativ...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 7; pp. 697 - 708 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104906631&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104906631 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2009 vid: 47 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104906631 NLM19247700 2010304520 10.1007/s11517-009-0461-0 NLM19247700 104906631 ppf: 697 ppct: 11 formats: fmt: @attributes: type: P tig: atl: A novel recursive Fourier transform for nonuniform sampled signals: application to heart rate variability spectrum estimation. aug: au: Holland A Aboy M Holland, Alexander Aboy, Mateo affil: Draeger Medical, Telford, PA, USA sug: subj: Heart Rate Variability Signal Processing, Computer Assisted ab: We present a novel method to iteratively calculate discrete Fourier transforms for discrete time signals with sample time intervals that may be widely nonuniform. The proposed recursive Fourier transform (RFT) does not require interpolation of the samples to uniform time intervals, and each iterative transform update of N frequencies has computational order N. Because of the inherent non-uniformity in the time between successive heart beats, an application particularly well suited for this transform is power spectral density (PSD) estimation for heart rate variability. We compare RFT based spectrum estimation with Lomb-Scargle Transform (LST) based estimation. PSD estimation based on the LST also does not require uniform time samples, but the LST has a computational order greater than Nlog(N). We conducted an assessment study involving the analysis of quasi-stationary signals with various levels of randomly missing heart beats. Our results indicate that the RFT leads to comparable estimation performance to the LST with significantly less computational overhead and complexity for applications requiring iterative spectrum estimations. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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