BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

314 related articles for article (PubMed ID: 29061181)

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

  • 2. 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; 105(1):40-9. PubMed ID: 20732724
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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; 25(S1):189-196. PubMed ID: 28582906
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.
    Boon KH; Khalil-Hani M; Malarvili MB; Sia CW
    Comput Methods Programs Biomed; 2016 Oct; 134():187-96. PubMed ID: 27480743
    [TBL] [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; 32(8):1147-62. PubMed ID: 21709338
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multiscale sample entropy based on discrete wavelet transform for clinical heart rate variability recognition.
    Lee MY; Yu SN
    Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():4299-302. PubMed ID: 23366878
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine.
    Asgari S; Mehrnia A; Moussavi M
    Comput Biol Med; 2015 May; 60():132-42. PubMed ID: 25817534
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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; 1(2):113-21. PubMed ID: 22606666
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings.
    Nguyen A; Ansari S; Hooshmand M; Lin K; Ghanbari H; Gryak J; Najarian K
    Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():526-529. PubMed ID: 30440450
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination.
    Alcaraz R; Rieta JJ
    Comput Biol Med; 2009 Aug; 39(8):697-706. PubMed ID: 19523611
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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; 44(1):51-64. PubMed ID: 18585905
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Alterations in autonomic response head-up tilt testing in paroxysmal atrial fibrillation patients: a wavelet analysis.
    Oliveira MM; da Silva N; Timóteo AT; Feliciano J; Silva S; Xavier R; Rocha I; Silva-Carvalho L; Ferreira R
    Rev Port Cardiol; 2009 Mar; 28(3):243-57. PubMed ID: 19480307
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Beat-to-beat P-wave morphology as a predictor of paroxysmal atrial fibrillation.
    Filos D; Chouvarda I; Tachmatzidis D; Vassilikos V; Maglaveras N
    Comput Methods Programs Biomed; 2017 Nov; 151():111-121. PubMed ID: 28946993
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [The analysis method of the Hilbert spectrum entropy of dividing frequency range for signals of heart rate variability].
    Dong H; Zhang A; Qiu T; Hao X
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2011 Apr; 28(2):248-54. PubMed ID: 21604478
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A support vector machine approach for AF classification from a short single-lead ECG recording.
    Liu N; Sun M; Wang L; Zhou W; Dang H; Zhou X
    Physiol Meas; 2018 Jun; 39(6):064004. PubMed ID: 29794340
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier.
    Li G; Chung WY
    Sensors (Basel); 2013 Dec; 13(12):16494-511. PubMed ID: 24316564
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.