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532 related items for PubMed ID: 30337081

  • 1. Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal.
    Ebrahimzadeh E, Kalantari M, Joulani M, Shahraki RS, Fayaz F, Ahmadi F.
    Comput Methods Programs Biomed; 2018 Oct; 165():53-67. PubMed ID: 30337081
    [Abstract] [Full Text] [Related]

  • 2. Prediction of paroxysmal atrial fibrillation using new heart rate variability features.
    Parsi A, Glavin M, Jones E, Byrne D.
    Comput Biol Med; 2021 Jun; 133():104367. PubMed ID: 33866252
    [Abstract] [Full Text] [Related]

  • 3. Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods.
    Chesnokov YV.
    Artif Intell Med; 2008 Jun; 43(2):151-65. PubMed ID: 18455375
    [Abstract] [Full Text] [Related]

  • 4. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.
    Boon KH, Khalil-Hani M, Malarvili MB.
    Comput Methods Programs Biomed; 2018 Jan; 153():171-184. PubMed ID: 29157449
    [Abstract] [Full Text] [Related]

  • 5. Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy.
    Xin Y, Zhao Y.
    Biomed Eng Online; 2017 Oct 23; 16(1):121. PubMed ID: 29061181
    [Abstract] [Full Text] [Related]

  • 6. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal.
    Mohebbi M, Ghassemian H.
    Comput Methods Programs Biomed; 2012 Jan 23; 105(1):40-9. PubMed ID: 20732724
    [Abstract] [Full Text] [Related]

  • 7. Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal.
    Mohebbi M, Ghassemian H.
    Physiol Meas; 2011 Aug 23; 32(8):1147-62. PubMed ID: 21709338
    [Abstract] [Full Text] [Related]

  • 8. An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal.
    Ebrahimzadeh E, Foroutan A, Shams M, Baradaran R, Rajabion L, Joulani M, Fayaz F.
    Comput Methods Programs Biomed; 2019 Feb 23; 169():19-36. PubMed ID: 30638589
    [Abstract] [Full Text] [Related]

  • 9. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.
    Boon KH, Khalil-Hani M, Malarvili MB, Sia CW.
    Comput Methods Programs Biomed; 2016 Oct 23; 134():187-96. PubMed ID: 27480743
    [Abstract] [Full Text] [Related]

  • 10. Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation.
    Martínez A, Abásolo D, Alcaraz R, Rieta JJ.
    Med Eng Phys; 2015 Jul 23; 37(7):692-7. PubMed ID: 25956053
    [Abstract] [Full Text] [Related]

  • 11. Non-episode-dependent assessment of paroxysmal atrial fibrillation through measurement of RR interval dynamics and atrial premature contractions.
    Hickey B, Heneghan C, de Chazal P.
    Ann Biomed Eng; 2004 May 23; 32(5):677-87. PubMed ID: 15171622
    [Abstract] [Full Text] [Related]

  • 12. Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning.
    Ganapathy N, Baumgärtel D, Deserno TM.
    Sensors (Basel); 2021 May 19; 21(10):. PubMed ID: 34069717
    [Abstract] [Full Text] [Related]

  • 13. A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia.
    Wang LH, Yan ZH, Yang YT, Chen JY, Yang T, Kuo IC, Abu PAR, Huang PC, Chen CA, Chen SL.
    Sensors (Basel); 2021 Aug 01; 21(15):. PubMed ID: 34372459
    [Abstract] [Full Text] [Related]

  • 14. Study of atrial activities for abnormality detection by phase rectified signal averaging technique.
    Maji U, Pal S, Mitra M.
    J Med Eng Technol; 2015 Aug 01; 39(5):291-302. PubMed ID: 26084877
    [Abstract] [Full Text] [Related]

  • 15. Study on the P-wave feature time course as early predictors of paroxysmal atrial fibrillation.
    Martínez A, Alcaraz R, Rieta JJ.
    Physiol Meas; 2012 Dec 01; 33(12):1959-74. PubMed ID: 23138002
    [Abstract] [Full Text] [Related]

  • 16. Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG.
    Xin Y, Zhao Y, Mu Y, Li Q, Shi C.
    Technol Health Care; 2017 Jul 20; 25(S1):189-196. PubMed ID: 28582906
    [Abstract] [Full Text] [Related]

  • 17. Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation.
    Mohebbi M, Ghassemian H, Asl BM.
    J Med Signals Sens; 2011 May 20; 1(2):113-21. PubMed ID: 22606666
    [Abstract] [Full Text] [Related]

  • 18. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal.
    Asl BM, Setarehdan SK, Mohebbi M.
    Artif Intell Med; 2008 Sep 20; 44(1):51-64. PubMed ID: 18585905
    [Abstract] [Full Text] [Related]

  • 19. Ranking of the most reliable beat morphology and heart rate variability features for the detection of atrial fibrillation in short single-lead ECG.
    Christov I, Krasteva V, Simova I, Neycheva T, Schmid R.
    Physiol Meas; 2018 Sep 24; 39(9):094005. PubMed ID: 30102603
    [Abstract] [Full Text] [Related]

  • 20. Automated detection of atrial fibrillation episode using novel heart rate variability features.
    Gilani M, Eklund JM, Makrehchi M.
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug 24; 2016():3461-3464. PubMed ID: 28269045
    [Abstract] [Full Text] [Related]

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