Weakly Supervised Arrhythmia Detection Based on Deep Convolutional Neural Network

no code implementations • 10 Dec 2020 • Yang Liu, Kuanquan Wang, Qince Li, Runnan He, Yongfeng Yuan, Henggui Zhang

The results show that the models achieve beat-level accuracies of 99. 09% in detecting atrial fibrillation, and 99. 13% in detecting morphological arrhythmias, which are comparable to that of fully supervised learning models, demonstrating their effectiveness.

Arrhythmia Detection ECG Classification +1

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

no code implementations • Frontiers in Physiology 2018 • Runnan He, Kuanquan Wang, Na Zhao, Yang Liu, Yongfeng Yuan, Qince Li, Henggui Zhang

The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities.

Atrial Fibrillation Detection Electrocardiography (ECG) +1