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Journal Abstract Search

121 related items for PubMed ID: 18003370

  • 21. Do nonlinearities play a significant role in short term, beat-to-beat variability?
    Choi HG, Mukkamala R, Moody GB, Mark RG.
    Comput Cardiol; 2001; 28():53-6. PubMed ID: 14640089
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  • 22. Assessing the strength of cardiac and sympathetic baroreflex controls via transfer entropy during orthostatic challenge.
    Porta A, Marchi A, Bari V, De Maria B, Esler M, Lambert E, Baumert M.
    Philos Trans A Math Phys Eng Sci; 2017 Jun 28; 375(2096):. PubMed ID: 28507235
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  • 25. New model to estimate mean blood pressure by heart rate with stroke volume changing influence.
    Al-Jaafreh MO, Al-Jumaily AA.
    Conf Proc IEEE Eng Med Biol Soc; 2006 Jun 28; 2006():1803-5. PubMed ID: 17946482
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  • 26. A multivariate time-variant AR method for the analysis of heart rate and arterial blood pressure.
    Di Virgilio V, Barbieri R, Mainardi L, Strano S, Cerutti S.
    Med Eng Phys; 1997 Mar 28; 19(2):109-24. PubMed ID: 9203145
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  • 29. Time-varying surrogate data to assess nonlinearity in nonstationary time series: application to heart rate variability.
    Faes L, Zhao H, Chon KH, Nollo G.
    IEEE Trans Biomed Eng; 2009 Mar 28; 56(3):685-95. PubMed ID: 19272872
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  • 30. Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series.
    Porta A, Bassani T, Bari V, Pinna GD, Maestri R, Guzzetti S.
    IEEE Trans Biomed Eng; 2012 Mar 28; 59(3):832-41. PubMed ID: 22194232
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  • 32. Changes in blood pressure, blood flow towards the head and heart rate during 90 deg head-up tilting for 30 min in anaesthetized male rats.
    Nishimura H, Yamasaki M.
    Exp Physiol; 2018 Jan 01; 103(1):31-39. PubMed ID: 29086448
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  • 33. Simulation of short-term pressure regulation during the tilt test in a coupled 3D-0D closed-loop model of the circulation.
    Lau KD, Figueroa CA.
    Biomech Model Mechanobiol; 2015 Aug 01; 14(4):915-29. PubMed ID: 25567754
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  • 36. Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals.
    Faes L, Porta A, Cucino R, Cerutti S, Antolini R, Nollo G.
    Biol Cybern; 2004 Jun 01; 90(6):390-9. PubMed ID: 15278463
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  • 37. Time-varying spectral analysis for comparison of HRV and PPG variability during tilt table test.
    Gil E, Orini M, Bailon R, Vergara JM, Mainardi L, Laguna P.
    Annu Int Conf IEEE Eng Med Biol Soc; 2010 Jun 01; 2010():3579-82. PubMed ID: 21096832
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  • 38. Open loop linear parametric modeling of the QT variability.
    Porta A, Tobaldini E, Magagnin V, Bassani T, Gnecchi-Ruscone T, Montano N.
    Annu Int Conf IEEE Eng Med Biol Soc; 2009 Jun 01; 2009():6453-6. PubMed ID: 19964431
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  • 39. Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm.
    Tarvainen MP, Georgiadis S, Lipponen JA, Hakkarainen M, Karjalainen PA.
    Annu Int Conf IEEE Eng Med Biol Soc; 2009 Jun 01; 2009():1-4. PubMed ID: 19963704
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  • 40. Predicting vasovagal syncope from heart rate and blood pressure: A prospective study in 140 subjects.
    Virag N, Erickson M, Taraborrelli P, Vetter R, Lim PB, Sutton R.
    Heart Rhythm; 2018 Sep 01; 15(9):1404-1410. PubMed ID: 29715516
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