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 *

269 related articles for article (PubMed ID: 32421643)

  • 1. Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review.
    Murat F; Yildirim O; Talo M; Baloglu UB; Demir Y; Acharya UR
    Comput Biol Med; 2020 May; 120():103726. PubMed ID: 32421643
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.
    Ullah W; Siddique I; Zulqarnain RM; Alam MM; Ahmad I; Raza UA
    Comput Intell Neurosci; 2021; 2021():2195922. PubMed ID: 34712316
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.
    Sattar S; Mumtaz R; Qadir M; Mumtaz S; Khan MA; De Waele T; De Poorter E; Moerman I; Shahid A
    Sensors (Basel); 2024 Apr; 24(8):. PubMed ID: 38676101
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A deep convolutional neural network model to classify heartbeats.
    Acharya UR; Oh SL; Hagiwara Y; Tan JH; Adam M; Gertych A; Tan RS
    Comput Biol Med; 2017 Oct; 89():389-396. PubMed ID: 28869899
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
    Yıldırım Ö; Pławiak P; Tan RS; Acharya UR
    Comput Biol Med; 2018 Nov; 102():411-420. PubMed ID: 30245122
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats.
    Oh SL; Ng EYK; Tan RS; Acharya UR
    Comput Biol Med; 2018 Nov; 102():278-287. PubMed ID: 29903630
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A novel application of deep learning for single-lead ECG classification.
    Mathews SM; Kambhamettu C; Barner KE
    Comput Biol Med; 2018 Aug; 99():53-62. PubMed ID: 29886261
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.
    Gao J; Zhang H; Lu P; Wang Z
    J Healthc Eng; 2019; 2019():6320651. PubMed ID: 31737240
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.
    Yildirim Ö
    Comput Biol Med; 2018 May; 96():189-202. PubMed ID: 29614430
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An Improved Convolutional Neural Network Based Approach for Automated Heartbeat Classification.
    Wang H; Shi H; Chen X; Zhao L; Huang Y; Liu C
    J Med Syst; 2019 Dec; 44(2):35. PubMed ID: 31853698
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification.
    Hammad M; Meshoul S; Dziwiński P; Pławiak P; Elgendy IA
    Sensors (Basel); 2022 Dec; 22(23):. PubMed ID: 36502049
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.
    Irfan S; Anjum N; Althobaiti T; Alotaibi AA; Siddiqui AB; Ramzan N
    Sensors (Basel); 2022 Jul; 22(15):. PubMed ID: 35957162
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals.
    Daydulo YD; Thamineni BL; Dawud AA
    BMC Med Inform Decis Mak; 2023 Oct; 23(1):232. PubMed ID: 37858107
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A new approach for arrhythmia classification using deep coded features and LSTM networks.
    Yildirim O; Baloglu UB; Tan RS; Ciaccio EJ; Acharya UR
    Comput Methods Programs Biomed; 2019 Jul; 176():121-133. PubMed ID: 31200900
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Cardiac arrhythmia detection using deep learning: A review.
    Parvaneh S; Rubin J; Babaeizadeh S; Xu-Wilson M
    J Electrocardiol; 2019; 57S():S70-S74. PubMed ID: 31416598
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing.
    Hsu PY; Cheng CK
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():292-295. PubMed ID: 33017986
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.
    Çınar A; Tuncer SA
    Comput Methods Biomech Biomed Engin; 2021 Feb; 24(2):203-214. PubMed ID: 32955928
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.
    Yildirim O; Talo M; Ciaccio EJ; Tan RS; Acharya UR
    Comput Methods Programs Biomed; 2020 Dec; 197():105740. PubMed ID: 32932129
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal.
    Anbarasi A; Ravi T; Manjula VS; Brindha J; Saranya S; Ramkumar G; Rathi R
    Biomed Res Int; 2022; 2022():5203401. PubMed ID: 35832849
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals.
    Prusty MR; Pandey TN; Lekha PS; Lellapalli G; Gupta A
    Sci Rep; 2024 Feb; 14(1):2633. PubMed ID: 38302520
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.