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Journal Abstract Search

183 related items for PubMed ID: 32112683

  • 1. A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning.
    Rizwan A, Zoha A, Mabrouk IB, Sabbour HM, Al-Sumaiti AS, Alomainy A, Imran MA, Abbasi QH.
    IEEE Rev Biomed Eng; 2021; 14():219-239. PubMed ID: 32112683
    [Abstract] [Full Text] [Related]

  • 2. Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network.
    Cai W, Chen Y, Guo J, Han B, Shi Y, Ji L, Wang J, Zhang G, Luo J.
    Comput Biol Med; 2020 Jan; 116():103378. PubMed ID: 31778896
    [Abstract] [Full Text] [Related]

  • 3. Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device.
    Marsili IA, Biasiolli L, Masè M, Adami A, Andrighetti AO, Ravelli F, Nollo G.
    Comput Biol Med; 2020 Jan; 116():103540. PubMed ID: 31751811
    [Abstract] [Full Text] [Related]

  • 4. A novel IRBF-RVM model for diagnosis of atrial fibrillation.
    Kong D, Zhu J, Wu S, Duan C, Lu L, Chen D.
    Comput Methods Programs Biomed; 2019 Aug; 177():183-192. PubMed ID: 31319947
    [Abstract] [Full Text] [Related]

  • 5. Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.
    Ghosh SK, Tripathy RK, Paternina MRA, Arrieta JJ, Zamora-Mendez A, Naik GR.
    J Med Syst; 2020 May 10; 44(6):114. PubMed ID: 32388733
    [Abstract] [Full Text] [Related]

  • 6. Machine learning detection of Atrial Fibrillation using wearable technology.
    Lown M, Brown M, Brown C, Yue AM, Shah BN, Corbett SJ, Lewith G, Stuart B, Moore M, Little P.
    PLoS One; 2020 May 10; 15(1):e0227401. PubMed ID: 31978173
    [Abstract] [Full Text] [Related]

  • 7. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine.
    Asgari S, Mehrnia A, Moussavi M.
    Comput Biol Med; 2015 May 10; 60():132-42. PubMed ID: 25817534
    [Abstract] [Full Text] [Related]

  • 8. A novel atrial fibrillation automatic detection algorithm based on ensemble learning and multi-feature discrimination.
    Wan X, Liu Y, Mei X, Ye J, Zeng C, Chen Y.
    Med Biol Eng Comput; 2024 Jun 10; 62(6):1809-1820. PubMed ID: 38388761
    [Abstract] [Full Text] [Related]

  • 9. 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 10; 165():53-67. PubMed ID: 30337081
    [Abstract] [Full Text] [Related]

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  • 11. Prediction of atrial fibrillation in patients with cardiac dysfunctions: P wave signal-averaged ECG and chemoreflexsensitivity in atrial fibrillation.
    Budeus M, Hennersdorf M, Felix O, Reimert K, Perings C, Wieneke H, Erbel R, Sack S.
    Europace; 2007 Aug 10; 9(8):601-7. PubMed ID: 17507361
    [Abstract] [Full Text] [Related]

  • 12. A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection.
    Shao M, Zhou Z, Bin G, Bai Y, Wu S.
    Sensors (Basel); 2020 Jan 22; 20(3):. PubMed ID: 31979184
    [Abstract] [Full Text] [Related]

  • 13. 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 22; 2016():3461-3464. PubMed ID: 28269045
    [Abstract] [Full Text] [Related]

  • 14. 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]

  • 15. Study of atrial activities for abnormality detection by phase rectified signal averaging technique.
    Maji U, Pal S, Mitra M.
    J Med Eng Technol; 2015 Sep 24; 39(5):291-302. PubMed ID: 26084877
    [Abstract] [Full Text] [Related]

  • 16. AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.
    Rizwan M, Whitaker BM, Anderson DV.
    Physiol Meas; 2018 Dec 24; 39(12):124007. PubMed ID: 30524091
    [Abstract] [Full Text] [Related]

  • 17. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals.
    Faust O, Shenfield A, Kareem M, San TR, Fujita H, Acharya UR.
    Comput Biol Med; 2018 Nov 01; 102():327-335. PubMed ID: 30031535
    [Abstract] [Full Text] [Related]

  • 18. Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.
    Lai D, Bu Y, Su Y, Zhang X, Ma CS.
    IEEE J Biomed Health Inform; 2020 Jun 01; 24(6):1569-1578. PubMed ID: 32175879
    [Abstract] [Full Text] [Related]

  • 19. Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications.
    Bollmann A, Husser D, Mainardi L, Lombardi F, Langley P, Murray A, Rieta JJ, Millet J, Olsson SB, Stridh M, Sörnmo L.
    Europace; 2006 Nov 01; 8(11):911-26. PubMed ID: 17043067
    [Abstract] [Full Text] [Related]

  • 20. Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records.
    Ng Y, Liao MT, Chen TL, Lee CK, Chou CY, Wang W.
    Artif Intell Med; 2023 Oct 01; 144():102644. PubMed ID: 37783539
    [Abstract] [Full Text] [Related]

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