292 related articles for article (PubMed ID: 33438636)
1. 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]
2. 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]
3. 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]
4. Detection of atrial fibrillation using discrete-state Markov models and Random Forests.
Kalidas V; Tamil LS
Comput Biol Med; 2019 Oct; 113():103386. PubMed ID: 31446318
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Time-varying coherence function for atrial fibrillation detection.
Lee J; Nam Y; McManus DD; Chon KH
IEEE Trans Biomed Eng; 2013 Oct; 60(10):2783-93. PubMed ID: 23708769
[TBL] [Abstract][Full Text] [Related]
7. A novel method for detection of the transition between atrial fibrillation and sinus rhythm.
Huang C; Ye S; Chen H; Li D; He F; Tu Y
IEEE Trans Biomed Eng; 2011 Apr; 58(4):1113-9. PubMed ID: 21134807
[TBL] [Abstract][Full Text] [Related]
8. A novel atrial fibrillation automatic detection algorithm based on ensemble learning and multi-feature discrimination.
Wan X; Liu Y; Mei X; Ye J; Zeng C; Chen Y
Med Biol Eng Comput; 2024 Jun; 62(6):1809-1820. PubMed ID: 38388761
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Bimodal classification algorithm for atrial fibrillation detection from m-health ECG recordings.
Kruger GH; Latchamsetty R; Langhals NB; Yokokawa M; Chugh A; Morady F; Oral H; Berenfeld O
Comput Biol Med; 2019 Jan; 104():310-318. PubMed ID: 30528214
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. Detecting atrial fibrillation from short single lead ECGs using statistical and morphological features.
Athif M; Yasawardene PC; Daluwatte C
Physiol Meas; 2018 Jun; 39(6):064002. PubMed ID: 29767635
[TBL] [Abstract][Full Text] [Related]
15. Device agnostic AI-based analysis of ambulatory ECG recordings.
Kennedy A; Doggart P; Smith SW; Finlay D; Guldenring D; Bond R; McCausland C; McLaughlin J
J Electrocardiol; 2022; 74():154-157. PubMed ID: 36283253
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device.
Marsili IA; Biasiolli L; Masè M; Adami A; Andrighetti AO; Ravelli F; Nollo G
Comput Biol Med; 2020 Jan; 116():103540. PubMed ID: 31751811
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.
Xiong Z; Nash MP; Cheng E; Fedorov VV; Stiles MK; Zhao J
Physiol Meas; 2018 Sep; 39(9):094006. PubMed ID: 30102248
[TBL] [Abstract][Full Text] [Related]
20. Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases.
Carrara M; Carozzi L; Moss TJ; de Pasquale M; Cerutti S; Lake DE; Moorman JR; Ferrario M
J Electrocardiol; 2015; 48(6):943-6. PubMed ID: 26320371
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
