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 *

135 related articles for article (PubMed ID: 35849679)

  • 1. DDCNN: A Deep Learning Model for AF Detection From a Single-Lead Short ECG Signal.
    Yu Z; Chen J; Liu Y; Chen Y; Wang T; Nowak R; Lv Z
    IEEE J Biomed Health Inform; 2022 Oct; 26(10):4987-4995. PubMed ID: 35849679
    [TBL] [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
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Dual-Channel Neural Network for Atrial Fibrillation Detection From a Single Lead ECG Wave.
    Fang B; Chen J; Liu Y; Wang W; Wang K; Singh AK; Lv Z
    IEEE J Biomed Health Inform; 2023 May; 27(5):2296-2305. PubMed ID: 34665746
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings.
    Zhang P; Ma C; Sun Y; Fan G; Song F; Feng Y; Zhang G
    Comput Biol Med; 2021 Dec; 139():104880. PubMed ID: 34700255
    [TBL] [Abstract][Full Text] [Related]  

  • 6. LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from Single Lead ECG On Wearable and Implantable Devices.
    Sahu I; Ukil A; Khandelwal S; Pal A
    Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():1655-1658. PubMed ID: 36085683
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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; 44(6):114. PubMed ID: 32388733
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.
    Lee HC; Chen CY; Lee SJ; Lee MC; Tsai CY; Chen ST; Li YJ
    Comput Biol Med; 2022 Jul; 146():105584. PubMed ID: 35551013
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 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. Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier.
    Jekova I; Christov I; Krasteva V
    Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015834
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Review of Deep Learning-Based Atrial Fibrillation Detection Studies.
    Murat F; Sadak F; Yildirim O; Talo M; Murat E; Karabatak M; Demir Y; Tan RS; Acharya UR
    Int J Environ Res Public Health; 2021 Oct; 18(21):. PubMed ID: 34769819
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.
    Kumar D; Peimankar A; Sharma K; Domínguez H; Puthusserypady S; Bardram JE
    Comput Methods Programs Biomed; 2022 Jun; 221():106899. PubMed ID: 35640394
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 12-lead ECG signal processing and atrial fibrillation prediction in clinical practice.
    Hsieh JC; Shih H; Xin LL; Yang CC; Han CL
    Technol Health Care; 2023; 31(2):417-433. PubMed ID: 36093717
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings.
    Fan X; Yao Q; Cai Y; Miao F; Sun F; Li Y
    IEEE J Biomed Health Inform; 2018 Nov; 22(6):1744-1753. PubMed ID: 30106699
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Transfer Learning for Detection of Atrial Fibrillation in Deterministic Compressive Sensed ECG.
    Abdelazez M; Rajan S; Chan ADC
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():5398-5401. PubMed ID: 33019201
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.
    Rahman MA; Ahmed S; Fattah SA
    Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():1024-1027. PubMed ID: 36086584
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 7.