Atrial Fibrillation Detection with Single-Lead Electrocardiogram Based on Temporal Convolutional Network-ResNet
SENSORS(2024)
摘要
Atrial fibrillation, one of the most common persistent cardiac arrhythmias globally, is known for its rapid and irregular atrial rhythms. This study integrates the temporal convolutional network (TCN) and residual network (ResNet) frameworks to effectively classify atrial fibrillation in single-lead ECGs, thereby enhancing the application of neural networks in this field. Our model demonstrated significant success in detecting atrial fibrillation, with experimental results showing an accuracy rate of 97% and an F1 score of 87%. These figures indicate the model's exceptional performance in identifying both majority and minority classes, reflecting its balanced and accurate classification capability. This research offers new perspectives and tools for diagnosis and treatment in cardiology, grounded in advanced neural network technology.
更多查看译文
关键词
atrial fibrillation,electrocardiogram,temporal convolutional network,residual network
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要