196 related articles for article (PubMed ID: 29157449)
1. 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]
2. 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]
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 wavelet α-entropy.
Xin Y; Zhao Y
Biomed Eng Online; 2017 Oct; 16(1):121. PubMed ID: 29061181
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. Methodology for the prediction of paroxysmal atrial fibrillation based on heart rate variability feature analysis.
Castro H; Garcia-Racines JD; Bernal-Norena A
Heliyon; 2021 Nov; 7(11):e08244. PubMed ID: 34765772
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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; 21(15):. PubMed ID: 34372459
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Atrial Fibrillation Prediction from Critically Ill Sepsis Patients.
Bashar SK; Ding EY; Walkey AJ; McManus DD; Chon KH
Biosensors (Basel); 2021 Aug; 11(8):. PubMed ID: 34436071
[TBL] [Abstract][Full Text] [Related]
15. 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; 169():19-36. PubMed ID: 30638589
[TBL] [Abstract][Full Text] [Related]
16. Study of atrial activities for abnormality detection by phase rectified signal averaging technique.
Maji U; Pal S; Mitra M
J Med Eng Technol; 2015; 39(5):291-302. PubMed ID: 26084877
[TBL] [Abstract][Full Text] [Related]
17. 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; 37(7):692-7. PubMed ID: 25956053
[TBL] [Abstract][Full Text] [Related]
18. An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.
Houshyarifar V; Chehel Amirani M
Biomed Mater Eng; 2016 Aug; 27(2-3):275-85. PubMed ID: 27567781
[TBL] [Abstract][Full Text] [Related]
19. Predicting termination of paroxysmal atrial fibrillation using empirical mode decomposition of the atrial activity and statistical features of the heart rate variability.
Mohebbi M; Ghassemian H
Med Biol Eng Comput; 2014 May; 52(5):415-27. PubMed ID: 24599701
[TBL] [Abstract][Full Text] [Related]
20. 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; 33(12):1959-74. PubMed ID: 23138002
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
