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 *

124 related articles for article (PubMed ID: 32431951)

  • 1. Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network.
    Huang ML; Wu YS
    Biomed Eng Lett; 2020 May; 10(2):183-193. PubMed ID: 32431951
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks.
    Ramesh J; Solatidehkordi Z; Aburukba R; Sagahyroon A
    Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770543
    [TBL] [Abstract][Full Text] [Related]  

  • 3. AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals.
    Radhakrishnan T; Karhade J; Ghosh SK; Muduli PR; Tripathy RK; Acharya UR
    Comput Biol Med; 2021 Oct; 137():104783. PubMed ID: 34481184
    [TBL] [Abstract][Full Text] [Related]  

  • 4. ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation.
    Zhao Z; Zhang Y; Deng Y; Zhang X
    Comput Biol Med; 2018 Nov; 102():168-179. PubMed ID: 30290297
    [TBL] [Abstract][Full Text] [Related]  

  • 5. ECG signal classification based on deep CNN and BiLSTM.
    Cheng J; Zou Q; Zhao Y
    BMC Med Inform Decis Mak; 2021 Dec; 21(1):365. PubMed ID: 34963455
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification.
    Darmawahyuni A; Nurmaini S; Rachmatullah MN; Tutuko B; Sapitri AI; Firdaus F; Fansyuri A; Predyansyah A
    PeerJ Comput Sci; 2022; 8():e825. PubMed ID: 35174263
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.
    Madan P; Singh V; Singh DP; Diwakar M; Pant B; Kishor A
    Bioengineering (Basel); 2022 Apr; 9(4):. PubMed ID: 35447712
    [TBL] [Abstract][Full Text] [Related]  

  • 9. An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network.
    Zhang D; Chen Y; Chen Y; Ye S; Cai W; Chen M
    J Healthc Eng; 2021; 2021():7167891. PubMed ID: 34616536
    [TBL] [Abstract][Full Text] [Related]  

  • 10. TP-CNN: A Detection Method for atrial fibrillation based on transposed projection signals with compressed sensed ECG.
    Zhang H; Dong Z; Sun M; Gu H; Wang Z
    Comput Methods Programs Biomed; 2021 Oct; 210():106358. PubMed ID: 34478912
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Towards Automated Optimization of Residual Convolutional Neural Networks for Electrocardiogram Classification.
    Fki Z; Ammar B; Ayed MB
    Cognit Comput; 2023 Feb; ():1-11. PubMed ID: 36819737
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal.
    Erdenebayar U; Kim H; Park JU; Kang D; Lee KJ
    J Korean Med Sci; 2019 Feb; 34(7):e64. PubMed ID: 30804732
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Parallel Cross Convolutional Recurrent Neural Network for Automatic Imbalanced ECG Arrhythmia Detection with Continuous Wavelet Transform.
    Toma TI; Choi S
    Sensors (Basel); 2022 Sep; 22(19):. PubMed ID: 36236496
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Detecting atrial fibrillation by deep convolutional neural networks.
    Xia Y; Wulan N; Wang K; Zhang H
    Comput Biol Med; 2018 Feb; 93():84-92. PubMed ID: 29291535
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM.
    Liu F; Li H; Wu T; Lin H; Lin C; Han G
    ISA Trans; 2023 Jul; 138():397-407. PubMed ID: 36898911
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network.
    Liu Z; Zhou B; Jiang Z; Chen X; Li Y; Tang M; Miao F
    J Am Heart Assoc; 2022 Apr; 11(7):e023555. PubMed ID: 35322685
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features.
    Bao Z; Li D; Jiang S; Zhang L; Zhang Y
    J Healthc Eng; 2023; 2023():3269144. PubMed ID: 36718172
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals.
    Dai H; Hwang HG; Tseng VS
    Comput Methods Programs Biomed; 2021 May; 203():106035. PubMed ID: 33770545
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory.
    Yang M; Liu W; Zhang H
    Front Physiol; 2022; 13():982537. PubMed ID: 36545286
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 7.