These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

126 related articles for article (PubMed ID: 34891392)

  • 1. Deep Learning-Based Data-Point Precise R-Peak Detection in Single-Lead Electrocardiograms.
    Oudkerk Pool MD; de Vos BD; Winter MM; Isgum I
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():718-721. PubMed ID: 34891392
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs.
    Lee H; Shin M
    Sensors (Basel); 2021 Jun; 21(13):. PubMed ID: 34202805
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.
    Sodmann P; Vollmer M; Nath N; Kaderali L
    Physiol Meas; 2018 Oct; 39(10):104005. PubMed ID: 30235165
    [TBL] [Abstract][Full Text] [Related]  

  • 4. ECG signal classification with binarized convolutional neural network.
    Wu Q; Sun Y; Yan H; Wu X
    Comput Biol Med; 2020 Jun; 121():103800. PubMed ID: 32568678
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network.
    Zahid MU; Kiranyaz S; Ince T; Devecioglu OC; Chowdhury MEH; Khandakar A; Tahir A; Gabbouj M
    IEEE Trans Biomed Eng; 2022 Jan; 69(1):119-128. PubMed ID: 34110986
    [TBL] [Abstract][Full Text] [Related]  

  • 6. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.
    Attia ZI; Noseworthy PA; Lopez-Jimenez F; Asirvatham SJ; Deshmukh AJ; Gersh BJ; Carter RE; Yao X; Rabinstein AA; Erickson BJ; Kapa S; Friedman PA
    Lancet; 2019 Sep; 394(10201):861-867. PubMed ID: 31378392
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.
    Rubin J; Parvaneh S; Rahman A; Conroy B; Babaeizadeh S
    J Electrocardiol; 2018; 51(6S):S18-S21. PubMed ID: 30122456
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.
    Hsieh CH; Li YS; Hwang BJ; Hsiao CH
    Sensors (Basel); 2020 Apr; 20(7):. PubMed ID: 32290113
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set.
    Krasteva V; Christov I; Naydenov S; Stoyanov T; Jekova I
    Sensors (Basel); 2021 Oct; 21(20):. PubMed ID: 34696061
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG.
    Plesinger F; Nejedly P; Viscor I; Halamek J; Jurak P
    Physiol Meas; 2018 Sep; 39(9):094002. PubMed ID: 30102251
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.
    Zhang X; Li J; Cai Z; Zhang L; Chen Z; Liu C
    Med Biol Eng Comput; 2021 Jan; 59(1):165-173. PubMed ID: 33387183
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks.
    Xiong Z; Stiles MK; Gillis AM; Zhao J
    Comput Biol Med; 2022 Jul; 146():105551. PubMed ID: 35533458
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.
    Baek YS; Lee SC; Choi W; Kim DH
    Sci Rep; 2021 Jun; 11(1):12818. PubMed ID: 34140578
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples.
    Baalman SWE; Schroevers FE; Oakley AJ; Brouwer TF; van der Stuijt W; Bleijendaal H; Ramos LA; Lopes RR; Marquering HA; Knops RE; de Groot JR
    Int J Cardiol; 2020 Oct; 316():130-136. PubMed ID: 32315684
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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; 39(9):094005. PubMed ID: 30102603
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Detection of Atrial Fibrillation from RR Intervals and PQRST Morphology using a Neural Network Ensemble.
    Khamis H; Chen J; Stephen Redmond J; Lovell NH
    Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5998-6001. PubMed ID: 30441703
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multi-information fusion neural networks for arrhythmia automatic detection.
    Chen A; Wang F; Liu W; Chang S; Wang H; He J; Huang Q
    Comput Methods Programs Biomed; 2020 Sep; 193():105479. PubMed ID: 32388066
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform.
    Wu Z; Feng X; Yang C
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():1908-1912. PubMed ID: 31946271
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.
    Raghunath S; Pfeifer JM; Ulloa-Cerna AE; Nemani A; Carbonati T; Jing L; vanMaanen DP; Hartzel DN; Ruhl JA; Lagerman BF; Rocha DB; Stoudt NJ; Schneider G; Johnson KW; Zimmerman N; Leader JB; Kirchner HL; Griessenauer CJ; Hafez A; Good CW; Fornwalt BK; Haggerty CM
    Circulation; 2021 Mar; 143(13):1287-1298. PubMed ID: 33588584
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings.
    Hong S; Zhou Y; Wu M; Shang J; Wang Q; Li H; Xie J
    Physiol Meas; 2019 Jun; 40(5):054009. PubMed ID: 30943458
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.