A novel atrial fibrillation automatic detection algorithm based on ensemble learning and multi-feature discrimination

[1] Huang C(2018)Atrial fibrillation: current understanding and recommendations for treatment-2018 Chin J Card Pacing Electrophysiol 32.04 315-368

[2] Zhang S(2022)An early warning of atrial fibrillation based on short-time ECG signals J Healthc Eng 18 1-7

[3] Huang D(2022)Semi-supervised learning for automatic atrial fibrillation detection in 24-hour Holter monitoring IEEE J Biomed Health Inform 26 3791-3801

[4] Qiu C(2022)Research on identification and classification of myocardial infarction based on electrocardiogram Chin J Med Phys 39 992-997

[5] Zhao T(2017)Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier Comput Cardiol 44 1-4

[6] Wang X(2018)Automatic detection and classification of atrial fibrillation based on RR interval and multiple eigenvalues J Biomed Eng 35 550-556

[7] Zhang P(2017)Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation Australas Phys Eng Sci Med 40 707-716

[8] Chen Y(2022)An automated detection of atrial fibrillation from single-lead ECG using HRV features and machine learning [J] J Electrocardiol 75 70-81

[9] Lin F(2011)A novel method for detection of the transition between atrial fibrillation and sinus rhythm IEEE Trans Biomed Eng 58 1113-1119

[10] Wang X(2021)ECG feature selection and machine learning in intelligent detection of atrial fibrillation J Electron Meas Instrumen 35 1-10

[1] Huang C(2018)Atrial fibrillation: current understanding and recommendations for treatment-2018 Chin J Card Pacing Electrophysiol 32.04 315-368

[2] Zhang S(2022)An early warning of atrial fibrillation based on short-time ECG signals J Healthc Eng 18 1-7

[3] Huang D(2022)Semi-supervised learning for automatic atrial fibrillation detection in 24-hour Holter monitoring IEEE J Biomed Health Inform 26 3791-3801

[4] Qiu C(2022)Research on identification and classification of myocardial infarction based on electrocardiogram Chin J Med Phys 39 992-997

[5] Zhao T(2017)Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier Comput Cardiol 44 1-4

[6] Wang X(2018)Automatic detection and classification of atrial fibrillation based on RR interval and multiple eigenvalues J Biomed Eng 35 550-556

[7] Zhang P(2017)Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation Australas Phys Eng Sci Med 40 707-716

[8] Chen Y(2022)An automated detection of atrial fibrillation from single-lead ECG using HRV features and machine learning [J] J Electrocardiol 75 70-81

[9] Lin F(2011)A novel method for detection of the transition between atrial fibrillation and sinus rhythm IEEE Trans Biomed Eng 58 1113-1119

[10] Wang X(2021)ECG feature selection and machine learning in intelligent detection of atrial fibrillation J Electron Meas Instrumen 35 1-10