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.
497 related articles for article (PubMed ID: 35552189)
21. Extracting deep features from short ECG signals for early atrial fibrillation detection. Wu X; Zheng Y; Chu CH; He Z Artif Intell Med; 2020 Sep; 109():101896. PubMed ID: 34756213 [TBL] [Abstract][Full Text] [Related]
22. Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study. Chuang BB; Yang AC JMIR Form Res; 2024 Mar; 8():e47803. PubMed ID: 38466973 [TBL] [Abstract][Full Text] [Related]
23. A two-step method for paroxysmal atrial fibrillation event detection based on machine learning. Wang Y; Liu S; Jia H; Deng X; Wang A; Yang C Math Biosci Eng; 2022 Jul; 19(10):9877-9894. PubMed ID: 36031973 [TBL] [Abstract][Full Text] [Related]
24. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Asgari S; Mehrnia A; Moussavi M Comput Biol Med; 2015 May; 60():132-42. PubMed ID: 25817534 [TBL] [Abstract][Full Text] [Related]
25. A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate. Zhou X; Ding H; Wu W; Zhang Y PLoS One; 2015; 10(9):e0136544. PubMed ID: 26376341 [TBL] [Abstract][Full Text] [Related]
27. Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques. Yue Y; Chen C; Liu P; Xing Y; Zhou X Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450743 [TBL] [Abstract][Full Text] [Related]
28. A novel approach for automatic detection of Atrial Fibrillation based on Inter Beat Intervals and Support Vector Machine. Andersen RS; Poulsen ES; Puthusserypady S Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():2039-2042. PubMed ID: 29060297 [TBL] [Abstract][Full Text] [Related]
29. Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning. Ganapathy N; Baumgärtel D; Deserno TM Sensors (Basel); 2021 May; 21(10):. PubMed ID: 34069717 [TBL] [Abstract][Full Text] [Related]
30. 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]
31. Accurate detection of atrial fibrillation events with R-R intervals from ECG signals. Duan J; Wang Q; Zhang B; Liu C; Li C; Wang L PLoS One; 2022; 17(8):e0271596. PubMed ID: 35925979 [TBL] [Abstract][Full Text] [Related]
32. 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]
33. An automated detection of atrial fibrillation from single‑lead ECG using HRV features and machine learning. Udawat AS; Singh P J Electrocardiol; 2022; 75():70-81. PubMed ID: 35918202 [TBL] [Abstract][Full Text] [Related]
34. Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records. Ng Y; Liao MT; Chen TL; Lee CK; Chou CY; Wang W Artif Intell Med; 2023 Oct; 144():102644. PubMed ID: 37783539 [TBL] [Abstract][Full Text] [Related]
35. Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods. Kim Y; Lee M; Yoon J; Kim Y; Min H; Cho H; Park J; Shin T Diagnostics (Basel); 2023 Sep; 13(17):. PubMed ID: 37685387 [TBL] [Abstract][Full Text] [Related]
36. 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]
37. A novel IRBF-RVM model for diagnosis of atrial fibrillation. Kong D; Zhu J; Wu S; Duan C; Lu L; Chen D Comput Methods Programs Biomed; 2019 Aug; 177():183-192. PubMed ID: 31319947 [TBL] [Abstract][Full Text] [Related]
38. Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study. Kwon S; Hong J; Choi EK; Lee E; Hostallero DE; Kang WJ; Lee B; Jeong ER; Koo BK; Oh S; Yi Y JMIR Mhealth Uhealth; 2019 Jun; 7(6):e12770. PubMed ID: 31199302 [TBL] [Abstract][Full Text] [Related]
39. An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG). Mandala S; Rizal A; Adiwijaya ; Nurmaini S; Suci Amini S; Almayda Sudarisman G; Wen Hau Y; Hanan Abdullah A PLoS One; 2024; 19(4):e0297551. PubMed ID: 38593145 [TBL] [Abstract][Full Text] [Related]
40. 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] [Previous] [Next] [New Search]