BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

287 related articles for article (PubMed ID: 30102239)

  • 1. Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device.
    Smisek R; Hejc J; Ronzhina M; Nemcova A; Marsanova L; Kolarova J; Smital L; Vitek M
    Physiol Meas; 2018 Sep; 39(9):094003. PubMed ID: 30102239
    [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. Machine learning detection of Atrial Fibrillation using wearable technology.
    Lown M; Brown M; Brown C; Yue AM; Shah BN; Corbett SJ; Lewith G; Stuart B; Moore M; Little P
    PLoS One; 2020; 15(1):e0227401. PubMed ID: 31978173
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal.
    Ebrahimzadeh E; Kalantari M; Joulani M; Shahraki RS; Fayaz F; Ahmadi F
    Comput Methods Programs Biomed; 2018 Oct; 165():53-67. PubMed ID: 30337081
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings.
    Nguyen A; Ansari S; Hooshmand M; Lin K; Ghanbari H; Gryak J; Najarian K
    Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():526-529. PubMed ID: 30440450
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches.
    Jahan MS; Mansourvar M; Puthusserypady S; Wiil UK; Peimankar A
    Int J Med Inform; 2022 Jul; 163():104790. PubMed ID: 35552189
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost.
    Chen Y; Wang X; Jung Y; Abedi V; Zand R; Bikak M; Adibuzzaman M
    Physiol Meas; 2018 Oct; 39(10):104006. PubMed ID: 30183685
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Accuracy of a smartwatch based single-lead electrocardiogram device in detection of atrial fibrillation.
    Rajakariar K; Koshy AN; Sajeev JK; Nair S; Roberts L; Teh AW
    Heart; 2020 May; 106(9):665-670. PubMed ID: 31911507
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal.
    Asl BM; Setarehdan SK; Mohebbi M
    Artif Intell Med; 2008 Sep; 44(1):51-64. PubMed ID: 18585905
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection.
    Shao M; Zhou Z; Bin G; Bai Y; Wu S
    Sensors (Basel); 2020 Jan; 20(3):. PubMed ID: 31979184
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks.
    Xiong Z; Stiles MK; Gillis AM; Zhao J
    Comput Biol Med; 2022 Jul; 146():105551. PubMed ID: 35533458
    [TBL] [Abstract][Full Text] [Related]  

  • 20. From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy.
    Jiménez-Serrano S; Rodrigo M; Calvo CJ; Millet J; Castells F
    Physiol Meas; 2022 Jun; 43(6):. PubMed ID: 35609610
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 15.