186 related articles for article (PubMed ID: 30010088)
1. Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection.
Warrick PA; Nabhan Homsi M
Physiol Meas; 2018 Oct; 39(11):114002. PubMed ID: 30010088
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.
Parvaneh S; Rubin J; Rahman A; Conroy B; Babaeizadeh S
Physiol Meas; 2018 Aug; 39(8):084003. PubMed ID: 30044235
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. Detection of atrial fibrillation and other abnormal rhythms from ECG using a multi-layer classifier architecture.
Mukherjee A; Dutta Choudhury A; Datta S; Puri C; Banerjee R; Singh R; Ukil A; Bandyopadhyay S; Pal A; Khandelwal S
Physiol Meas; 2019 Jun; 40(5):054006. PubMed ID: 30650387
[TBL] [Abstract][Full Text] [Related]
8. AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.
Rizwan M; Whitaker BM; Anderson DV
Physiol Meas; 2018 Dec; 39(12):124007. PubMed ID: 30524091
[TBL] [Abstract][Full Text] [Related]
9. A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length.
Kamaleswaran R; Mahajan R; Akbilgic O
Physiol Meas; 2018 Mar; 39(3):035006. PubMed ID: 29369044
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. A support vector machine approach for AF classification from a short single-lead ECG recording.
Liu N; Sun M; Wang L; Zhou W; Dang H; Zhou X
Physiol Meas; 2018 Jun; 39(6):064004. PubMed ID: 29794340
[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. Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features.
Shao M; Bin G; Wu S; Bin G; Huang J; Zhou Z
Physiol Meas; 2018 Sep; 39(9):094008. PubMed ID: 30187894
[TBL] [Abstract][Full Text] [Related]
16. Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification.
Sawant NK; Patidar S
Physiol Meas; 2022 Dec; 43(12):. PubMed ID: 36410043
[No Abstract] [Full Text] [Related]
17. A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017.
Kleyko D; Osipov E; Wiklund U
Biomed Phys Eng Express; 2020 Feb; 6(2):025010. PubMed ID: 33438636
[TBL] [Abstract][Full Text] [Related]
18. A low-complexity algorithm for detection of atrial fibrillation using an ECG.
Sadr N; Jayawardhana M; Pham TT; Tang R; Balaei AT; de Chazal P
Physiol Meas; 2018 Jun; 39(6):064003. PubMed ID: 29791322
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. An SVM approach for identifying atrial fibrillation.
Gliner V; Yaniv Y
Physiol Meas; 2018 Sep; 39(9):094007. PubMed ID: 30187892
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]